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Jiao, Yuzhong, Kayton Wai Keung Cheung, Mark Ping Chan Mok, and Yiu Kei Li. "Spatial Distance-based Interpolation Algorithm for Computer Generated 2D+Z Images." Electronic Imaging 2020, no. 2 (January 26, 2020): 140–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.2.sda-140.
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Computer generated 2D plus Depth (2D+Z) images are common input data for 3D display with depth image-based rendering (DIBR) technique. Due to their simplicity, linear interpolation methods are usually used to convert low-resolution images into high-resolution images for not only depth maps but also 2D RGB images. However linear methods suffer from zigzag artifacts in both depth map and RGB images, which severely affects the 3D visual experience. In this paper, spatial distance-based interpolation algorithm for computer generated 2D+Z images is proposed. The method interpolates RGB images with the help of depth and edge information from depth maps. Spatial distance from interpolated pixel to surrounding available pixels is utilized to obtain the weight factors of surrounding pixels. Experiment results show that such spatial distance-based interpolation can achieve sharp edges and less artifacts for 2D RGB images. Naturally, it can improve the performance of 3D display. Since bilinear interpolation is used in homogenous areas, the proposed algorithm keeps low computational complexity.
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Wyczechowski, Marcin, Łukasz Wąs, and Łukasz Pietrzak. "ANALIZA ZMIAN CHORÓB SKÓRNYCH PRZY POMOCY ODWZOROWAŃ PRZESTRZENI BARW." Informatics Control Measurement in Economy and Environment Protection 7, no. 3 (September 30, 2017): 68–71. http://dx.doi.org/10.5604/01.3001.0010.5219.
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Głównym celem artykułu jest analiza zmian dermatologicznych skóry w oparciu o analizę modelu przestrzeni barw, z których autorzy są w stanie wydobyć informacje przydatne dla systemu ekspertowego. W artykule autorzy badają zdjęcia zmiana skórnych wprowadzając autorski algorytm ekstrakcji cech używając zmodyfikowanej analizy przestrzeni barw. Autorzy również skupią się nad analizą zdjęcia uwzględniając model przestrzeni kolorów taki jak RGB oraz YCbCr z wyżej wymienionych modeli autorzy ekstrahują kolejne składowe przenosząc reprezentacje danej składowej na inną. Autorzy skupiają się na skali YCbCr, gdyż niesie ona ze sobą informację inaczej skonstruowaną o konkretnym obrazie aniżeli jego odpowiednik w RGB.
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Xu, Chi, Jun Zhou, Wendi Cai, Yunkai Jiang, Yongbo Li, and Yi Liu. "Robust 3D Hand Detection from a Single RGB-D Image in Unconstrained Environments." Sensors 20, no. 21 (November 7, 2020): 6360. http://dx.doi.org/10.3390/s20216360.
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Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.
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Vieira, Katherine, Giovanni Carraro, Vladimir Korchagin, Artem Lutsenko, Terrence M. Girard, and William van Altena. "Milky Way Thin and Thick Disk Kinematics with Gaia EDR3 and RAVE DR5." Astrophysical Journal 932, no. 1 (June 1, 2022): 28. http://dx.doi.org/10.3847/1538-4357/ac6b9b.
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Abstract We present a detailed analysis of kinematics of the Milky Way disk in the solar neighborhood using the Gaia DR3 catalog. To determine the local kinematics of the stellar disks of the Milky Way we use a complete sample of 278,228 red giant branch (RGB) stars distributed in a cylinder, centered at the Sun with a 1 kpc radius and half-height of 0.5 kpc. We determine separately the kinematical properties of RGB stars for each Galactic hemisphere in search of possible asymmetries. The kinematical properties of the RGB stars reveal the existence of two kinematically distinct components: the thin disk with mean velocities V R , V ϕ , V Z of about −1, −239, 0 km s−1, correspondingly, and velocity dispersions σ R , σ ϕ , σ Z of 31, 20, and 11 km s−1, and the Thick disk with mean velocity components of about +1, −225, 0 km s−1, and velocity dispersions of 49, 35, and 22 km s−1. We find that up to 500 pc in height above/below the Galactic plane, Thick disk stars comprise about half the stars of the disk. Such a high amount of RGB stars with Thick disk kinematics points at the secular evolution scenario origin for the Thick disk of the Milky Way.
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Yeom, Junho, Jinha Jung, Anjin Chang, Akash Ashapure, Murilo Maeda, Andrea Maeda, and Juan Landivar. "Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture." Remote Sensing 11, no. 13 (June 29, 2019): 1548. http://dx.doi.org/10.3390/rs11131548.
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Unmanned aerial vehicle (UAV) platforms with sensors covering the red-edge and near-infrared (NIR) bands to measure vegetation indices (VIs) have been recently introduced in agriculture research. Consequently, VIs originally developed for traditional airborne and spaceborne sensors have become applicable to UAV systems. In this study, we investigated the difference in tillage treatments for cotton and sorghum using various RGB and NIR VIs. Minimized tillage has been known to increase farm sustainability and potentially optimize productivity over time; however, repeated tillage is the most commonly-adopted management practice in agriculture. To this day, quantitative comparisons of plant growth patterns between conventional tillage (CT) and no tillage (NT) fields are often inconsistent. In this study, high-resolution and multi-temporal UAV data were used for the analysis of tillage effects on plant health and the performance of various vegetation indices investigated. Time series data over ten dates were acquired on a weekly basis by RGB and multispectral (MS) UAV platforms: a DJI Phantom 4 Pro and a DJI Matrice 100 with the SlantRange 3p sensor. Ground reflectance panels and an ambient illumination sensor were used for the radiometric calibration of RGB and MS orthomosaic images, respectively. Various RGB and NIR-based vegetation indices were then calculated for the comparison between CT and NT treatments. In addition, a one-tailed Z-test was conducted to check the significance of VIs’ difference between CT and NT treatments. The results showed distinct differences in VIs between tillage treatments during the whole growing season. NIR-based VIs showed better discrimination performance than RGB-based VIs. Out of 13 VIs, the modified soil adjusted vegetation index (MSAVI) and optimized soil adjusted vegetation index (OSAVI) showed better performance in terms of quantitative difference measurements and the Z-test between tillage treatments. The modified green red vegetation index (MGRVI) and excess green (ExG) showed reliable separability and can be an alternative for economic RGB UAV application.
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Yuan, Wenan, Sai Raghavendra Prasad Poosa, and Rutger Francisco Dirks. "Comparative Analysis of Color Space and Channel, Detector, and Descriptor for Feature-Based Image Registration." Journal of Imaging 10, no. 5 (April 28, 2024): 105. http://dx.doi.org/10.3390/jimaging10050105.
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The current study aimed to quantify the value of color spaces and channels as a potential superior replacement for standard grayscale images, as well as the relative performance of open-source detectors and descriptors for general feature-based image registration purposes, based on a large benchmark dataset. The public dataset UDIS-D, with 1106 diverse image pairs, was selected. In total, 21 color spaces or channels including RGB, XYZ, Y′CrCb, HLS, L*a*b* and their corresponding channels in addition to grayscale, nine feature detectors including AKAZE, BRISK, CSE, FAST, HL, KAZE, ORB, SIFT, and TBMR, and 11 feature descriptors including AKAZE, BB, BRIEF, BRISK, DAISY, FREAK, KAZE, LATCH, ORB, SIFT, and VGG were evaluated according to reprojection error (RE), root mean square error (RMSE), structural similarity index measure (SSIM), registration failure rate, and feature number, based on 1,950,984 image registrations. No meaningful benefits from color space or channel were observed, although XYZ, RGB color space and L* color channel were able to outperform grayscale by a very minor margin. Per the dataset, the best-performing color space or channel, detector, and descriptor were XYZ/RGB, SIFT/FAST, and AKAZE. The most robust color space or channel, detector, and descriptor were L*a*b*, TBMR, and VGG. The color channel, detector, and descriptor with the most initial detector features and final homography features were Z/L*, FAST, and KAZE. In terms of the best overall unfailing combinations, XYZ/RGB+SIFT/FAST+VGG/SIFT seemed to provide the highest image registration quality, while Z+FAST+VGG provided the most image features.
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Febriani, Eka Fitria, Maheno Sri Widodo, and Abdul Rahem Faqih. "Phytochemical Analysis from Three Different Methanolic Extracts of Red Ginger (Zingiber officinale var. Rubrum) Against LC50 Treatment of Zebra Fish as Model Fish." Journal of Aquaculture and Fish Health 12, no. 2 (May 27, 2023): 179–90. http://dx.doi.org/10.20473/jafh.v12i2.36747.
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Red ginger (Zingiber officinale var. Rubrum) as one of the Indonesian spices has so many important roles, especially in the health sector as a medicinal plant, which has many active compounds including phenols, alkaloids, flavonoids, steroids and tannins. From some researches before, we can know that plants which grow in different places or locations have different tolerances generally. That statements can show that they have different content in their constituent metabolites definitely. Therefore, phytochemical analysis is needed to analyze the content of each red ginger (Z. officinale var. Rubrum) from three locations such as Red ginger (Z. officinale var. Rubrum) from Batu, Malang (RGB), Plaosan, Magetan (RGP) and Simalungun, Medan (RGS) to analyze each content its contains. Also, perform LC50 was needed for analyzing its effect on Zebra fish (Danio rerio) as a model fish. For this research, we use an experimental study with a Completely Randomized Factorial Design (CRFD) method that has two independent variable such as different dosage and type of red ginger (Z. officinale var. Rubrum). In this study, from five dosages (10 mg/l; 20 mg/l; 30 mg/l; 40 mg/l; 50 mg/l) and three types of red ginger (Z. officinale var. Rubrum) treatment, it found that the highest mortality and lowest survival rate were at the highest dosage (50 mg/l) in RGB and extreme increase or decrease in the curve is found in RGP treatment.
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Büker, Linda Christin, Finnja Zuber, Andreas Hein, and Sebastian Fudickar. "HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints." Sensors 21, no. 4 (February 14, 2021): 1356. http://dx.doi.org/10.3390/s21041356.
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With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).
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Xu, Jing Ya, Hao Xue Liu, and Min Huang. "Discussion on Colorimetric Computation Method of LCD Display." Advanced Materials Research 174 (December 2010): 68–71. http://dx.doi.org/10.4028/www.scientific.net/amr.174.68.
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The display colorimetric accuracy is a key factor to ensure printing quality. The main mission for display’s color management is to compute its display color to get accurate colorimetric characterization, namely to ascertain the converting relationship between the RGB color data and their corresponding CIE X Y Z values. The related color values of EIZO, Mac and common PC displays were measured in the experiment, fitted the parameter, and computed the displayed color with gain-offset-gamma(GOG)model.Two-Primary Crosstalk (TPC) model, Piecewise Partition (PP)model. The three models were analyzed and compared with the measurement values. The model performance was assessed using 729 color samples uniformly distributed on the RGB display gamut. Compare the measured values and computed values and then find the more precise way to determine the display’s colorimetric characterization.
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Kraszewski, Bartłomiej. "Określenie wpływu jakości atrybutu RGB powiązanego z danymi naziemnego skaningu laserowego na proces segmentacji." Bulletin of the Military University of Technology 64, no. 2 (June 30, 2015): 111–21. http://dx.doi.org/10.5604/12345865.1157316.
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Ropelewska, Ewa, and Krzysztof P. Rutkowski. "Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures." European Food Research and Technology 247, no. 9 (June 14, 2021): 2371–77. http://dx.doi.org/10.1007/s00217-021-03797-9.
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AbstractThe peaches belonging to different cultivars can be characterized by differentiation in properties. The aim of this study was to evaluate the usefulness of individual parts of fruit (skin, flesh, stone and seed) for cultivar discrimination of peaches based on textures determined using image analysis. Discriminant analysis was performed using the classifiers of Bayes net, logistic, SMO, multi-class classifier and random forest based on a set of combined textures selected from all color channels R, G, B, L, a, b, X, Y, Z and for textures selected separately for RGB, Lab and XYZ color spaces. In the case of sets of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z), the accuracy of 100% was observed for flesh, stones and seeds for selected classifiers. The sets of textures selected from RGB color space produced the correctness equal to 100% in the case of flesh and seeds of peaches. In the case of Lab and XYZ color spaces, slightly lower accuracies than for RGB color space were obtained and the accuracy reaching 100% was noted only for the discrimination of seeds of peaches. The research proved the usefulness of selected texture parameters of fruit flesh, stones and seeds for successful discrimination of peach cultivars with an accuracy of 100%. The distinguishing between cultivars may be important for breeders, consumers and the peach industry for ensuring adequate processing conditions and equipment parameters. The cultivar identification of fruit by human may be characterized by large errors. The molecular or chemical methods may require special equipment or be time-consuming. The image analysis may ensure objective, rapid and relatively inexpensive procedure and high accuracy for peach cultivar discrimination.
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Röhling, Hanna Marie, Karen Otte, Sophia Rekers, Carsten Finke, Rebekka Rust, Eva-Maria Dorsch, Behnoush Behnia, Friedemann Paul, and Tanja Schmitz-Hübsch. "RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks." International Journal of Environmental Research and Public Health 19, no. 24 (December 17, 2022): 16989. http://dx.doi.org/10.3390/ijerph192416989.
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Background: Instrumental motion analysis constitutes a promising development in the assessment of motor function in clinical populations affected by movement disorders. To foster implementation and facilitate interpretation of respective outcomes, we aimed to establish normative data of healthy subjects for a markerless RGB-Depth camera-based motion analysis system and to illustrate their use. Methods: We recorded 133 healthy adults (56% female) aged 20 to 60 years with an RGB-Depth camera-based motion analysis system. Forty-three spatiotemporal parameters were extracted from six short, standardized motor tasks—including three gait tasks, stepping in place, standing-up and sitting down, and a postural control task. Associations with confounding factors, height, weight, age, and sex were modelled using a predictive linear regression approach. A z-score normalization approach was provided to improve usability of the data. Results: We reported descriptive statistics for each spatiotemporal parameter (mean, standard deviation, coefficient of variation, quartiles). Robust confounding associations emerged for step length and step width in comfortable speed gait only. Accessible normative data usage was lastly exemplified with recordings from one randomly selected individual with multiple sclerosis. Conclusion: We provided normative data for an RGB depth camera-based motion analysis system covering broad aspects of motor capacity.
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Łuczak, Dominik. "Machine Fault Diagnosis through Vibration Analysis: Time Series Conversion to Grayscale and RGB Images for Recognition via Convolutional Neural Networks." Energies 17, no. 9 (April 23, 2024): 1998. http://dx.doi.org/10.3390/en17091998.
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Accurate and timely fault detection is crucial for ensuring the smooth operation and longevity of rotating machinery. This study explores the effectiveness of image-based approaches for machine fault diagnosis using data from a 6DOF IMU (Inertial Measurement Unit) sensor. Three novel methods are proposed. The IMU6DoF-Time2GrayscaleGrid-CNN method converts the time series sensor data into a single grayscale image, leveraging the efficiency of a grayscale representation and the power of convolutional neural networks (CNNs) for feature extraction. The IMU6DoF-Time2RGBbyType-CNN method utilizes RGB images. The IMU6DoF-Time2RGBbyAxis-CNN method employs an RGB image where each channel corresponds to a specific axis (X, Y, Z) of the sensor data. This axis-aligned representation potentially allows the CNN to learn the relationships between movements along different axes. The performance of all three methods is evaluated through extensive training and testing on a dataset containing various operational states (idle, normal, fault). All methods achieve high accuracy in classifying these states. While the grayscale method offers the fastest training convergence, the RGB-based methods might provide additional insights. The interpretability of the models is also explored using Grad-CAM visualizations. This research demonstrates the potential of image-based approaches with CNNs for robust and interpretable machine fault diagnosis using sensor data.
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Kotlarz, Jan, and Mariusz Kacprzak. "Species diversity of forest stands estimation algorithm using RGB images of the tree crowns." Pomiary Automatyka Robotyka 21, no. 1 (March 30, 2017): 63–70. http://dx.doi.org/10.14313/par_223/63.
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Filho, Júlio Cezar Cotrim Moreira, and João Rodrigues Tavares Junior. "Avaliação da precisão temática de composições de NDBI, NDVI, NDWI." Revista Brasileira de Geomática 4, no. 1 (March 21, 2016): 3. http://dx.doi.org/10.3895/rbgeo.v4n1.5462.
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Este artigo trata de experimentos usando composições de índices físicos NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDBI (Normalized Difference Built-Up Index), para avaliar a precisão temática do classificador Máxima Verossimilhança (MAXVER) usando exatidão global, índice kappa e teste Z. A área de estudo foi o entorno da Lagoa Olho D’Água localizada em Jaboatão dos Guararapes-PE. Foram usadas duas cenas TM LANDSAT-5, órbita-ponto 214-066, de 17/03/20111 e 29/09/2011, do DGI-INPE (Divisão de Geração de Imagens do Instituto Nacional de Pesquisas Espaciais), e todo o processamento realizado no SPRING 5.0.6. Os resultados indicam que apenas usando índices físicos substituindo composições RGB, a acurácia temática é muito degradada. A classificação MAXVER da composição NDBI-TM4-TM-3 e IHS obtiveram bons resultados em acurácia temática, demonstrando que o método de combinações de índices físicos e composições RGB podem ser usados para melhorar os resultados da classificação MAXVER.
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Ramezani Dooraki, Amir, and Deok-Jin Lee. "A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments." Machines 10, no. 7 (June 22, 2022): 500. http://dx.doi.org/10.3390/machines10070500.
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In this paper, we introduce a self-trained controller for autonomous navigation in static and dynamic (with moving walls and nets) challenging environments (including trees, nets, windows, and pipe) using deep reinforcement learning, simultaneously trained using multiple rewards. We train our RL algorithm in a multi-objective way. Our algorithm learns to generate continuous action for controlling the UAV. Our algorithm aims to generate waypoints for the UAV in such a way as to reach a goal area (shown by an RGB image) while avoiding static and dynamic obstacles. In this text, we use the RGB-D image as the input for the algorithm, and it learns to control the UAV in 3-DoF (x, y, and z). We train our robot in environments simulated by Gazebo sim. For communication between our algorithm and the simulated environments, we use the robot operating system. Finally, we visualize the trajectories generated by our trained algorithms using several methods and illustrate our results that clearly show our algorithm’s capability in learning to maximize the defined multi-objective reward.
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Dalcanton, Julianne J., Eric F. Bell, Yumi Choi, Andrew E. Dolphin, Morgan Fouesneau, Léo Girardi, David W. Hogg, Anil C. Seth, and Benjamin F. Williams. "The Panchromatic Hubble Andromeda Treasury. XX. The Disk of M31 is Thick." Astronomical Journal 166, no. 2 (July 31, 2023): 80. http://dx.doi.org/10.3847/1538-3881/accc83.
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Abstract We present a new approach to measuring the thickness of a partially face-on stellar disk, using dust geometry. In a moderately-inclined disk galaxy, the fraction of reddened stars is expected to be 50% everywhere, assuming that dust lies in a thin midplane. In a thickened disk, however, a wide range of radii project onto the line of sight. Assuming stellar density declines with radius, this geometrical projection leads to differences in the numbers of stars on the near and far sides of the thin dust layer. The fraction of reddened stars will thus differ from the 50% prediction, with a deviation that becomes larger for puffier disks. We map the fraction of reddened red giant branch (RGB) stars across M31, which shows prominent dust lanes on only one side of the major axis. The fraction of reddened stars varies systematically from 20% to 80%, which requires that these stars have an exponential scale height h z that is 0.14 ± 0.015 times the exponential scale length (h r ≈ 5.5 kpc). M31's RGB stars must therefore have h z = 770 ± 80 pc, which is far thicker than the Milky Way’s thin disk, but comparable to its thick disk. The lack of a significant thin disk in M31 is unexpected, but consistent with its interaction history and high disk velocity dispersion. We suggest that asymmetric reddening be used as a generic criterion for identifying “thick disk”-dominated systems, and discuss prospects for future 3D tomographic mapping of the gas and stars in M31.
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Bonatto, Charles, and Ana L. Chies-Santos. "Lifting the dust veil from the globular cluster Palomar 2." Monthly Notices of the Royal Astronomical Society 493, no. 2 (February 20, 2020): 2688–93. http://dx.doi.org/10.1093/mnras/staa510.
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ABSTRACT This work employs high-quality Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) F606W and F814W photometry to correct for the differential reddening affecting the colour–magnitude diagram (CMD) of the poorly studied globular cluster (GC) Palomar 2. Differential reddening is taken into account by assuming that morphological differences among CMDs extracted across the field of view of Palomar 2 correspond essentially to shifts (quantified in terms of δE(B − V)) along the reddening vector due to a non-uniform dust distribution. The average reddening difference over all partial CMDs is $\overline{{\delta E(B-V)}}=0.24\pm 0.08$, with the highest reaching δE(B − V) = 0.52. The corrected CMD displays well-defined and relatively narrow evolutionary sequences, especially for the evolved stars, i.e. the red giant, horizontal, and asymptotic giant branches (RGB, HB, and AGB, respectively). The average width of the upper main sequence and RGB profiles of the corrected CMD corresponds to 56 per cent of the original one. Parameters measured on this CMD show that Palomar 2 is ≈13.25 Gyr old, has the mass $M\sim 1.4\times 10^5\, \rm {\mathrm{ M}_\odot}$ stored in stars, is affected by the foreground E(B − V) ≈ 0.93, is located at d⊙ ≈ 26 kpc from the Sun, and is characterized by the global metallicity Z/Z⊙ ≈ 0.03, which corresponds to the range −1.9 ≤ [Fe/H] ≤ −1.6 (for 0.0 ≤ [α/Fe] ≤ +0.4), quite consistent with other outer halo GCs. Additional parameters are the absolute magnitude MV ≈ −7.8, and the core and half-light radii rC ≈ 2.6 pc and RHL ≈ 4.7 pc, respectively.
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Kossowski, Maciej. "Zastosowania dronów w badaniach rzeźby terenu, struktury i tekstury osadów na przykładach z Polski Środkowej." Acta Universitatis Lodziensis. Folia Geographica Physica, no. 21 (March 31, 2023): 35–47. http://dx.doi.org/10.18778/1427-9711.21.03.
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Drony (bezzałogowe statki powietrzne – BSP) wraz z rozwojem technologii są coraz częściej wykorzystywane do rozwiązywania problemów naukowych. Dotyczy to w szczególności nauk o Ziemi i środowisku, w tym geomorfologii, geologii strukturalnej i sedymentologii. Obok kwestii sprzętowo-technicznych, równoległy rozwój programów fotogrametrycznych oraz systemów służących do zwiększania precyzji pomiarów przebiegu lotu dronów, przyczyniają się do możliwości tworzenia zarówno map terenu, jak i modeli 3D o coraz większej dokładności. W niniejszej pracy opisano możliwości wykorzystania dronów wyposażonych w niemetryczną kamerę RGB lub sensor termalny do badania przemian morfologicznych terenu na podstawie analizy zdjęć, ortofotomap i modeli 3D. Badania testowe przeprowadzono w dolinach Warty i Pilicy w rejonie sztucznych zbiorników oraz na wysoczyznach morenowych i płaskowyżach – głównie w kopalniach odkrywkowych. Wynika z nich, że odpowiednie zaplanowanie lotu i konfiguracja pracy kamery statku powietrznego gwarantują uzyskanie właściwych danych niezbędnych do późniejszej obróbki w oprogramowaniu, np. GIS. Do najważniejszych parametrów zaliczono: określenie obszaru planowanego nalotu, wysokość lotu nad poziomem gruntu, prędkość statku powietrznego, prędkość powtarzalności kamery oraz dokładność generowanego materiału.
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Hu, Tiantian, Wenbo Wang, Jinan Gu, Zilin Xia, Jian Zhang, and Bo Wang. "Research on Apple Object Detection and Localization Method Based on Improved YOLOX and RGB-D Images." Agronomy 13, no. 7 (July 8, 2023): 1816. http://dx.doi.org/10.3390/agronomy13071816.
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The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively.
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Ropelewska, Ewa, Kadir Sabanci, Muhammet Fatih Aslan, and Afshin Azizi. "A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters." Horticulturae 8, no. 5 (May 11, 2022): 431. http://dx.doi.org/10.3390/horticulturae8050431.
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The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.
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Sedano-Cibrián, Javier, Julio Manuel de Luis-Ruiz, Rubén Pérez-Álvarez, Raúl Pereda-García, and Jonathan Daniel Tapia-Espinoza. "4D Models Generated with UAV Photogrammetry for Landfill Monitoring Thermal Control of Municipal Solid Waste (MSW) Landfills." Applied Sciences 13, no. 24 (December 11, 2023): 13164. http://dx.doi.org/10.3390/app132413164.
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The management of the increasing volume of municipal solid waste is an essential activity for the health of the environment and of the population. The organic matter of waste deposited in landfills is subject to aerobic decomposition processes, bacterial aerobic decomposition, and chemical reactions that release large amounts of heat, biogas, and leachates at high temperatures. The control of these by-products enables their recovery, utilization, and treatment for energy use, avoiding emissions to the environment. UAVs with low-cost thermal sensors are a tool that enables the representation of temperature distributions for the thermal control of landfills. This study focuses on the development of a methodology for the generation of 3D thermal models through the projection of TIR image information onto a 3D model generated from RGB images and the identification of thermal anomalies by means of photointerpretation and GIS analysis. The novel methodological approach was implemented at the Meruelo landfill for validation. At the facility, a 4D model (X,Y,Z-temperature) and a 13.8 cm/px GSD thermal orthoimage were generated with a thermal accuracy of 1.63 °C, which enabled the identification of at least five areas of high temperatures associated with possible biogas emissions, decomposing organic matter, or underground fires, which were verified by on-site measurements and photointerpretation of the RGB model, in order to take and assess specific corrective measures.
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Kushwaha, S. K. P., K. R. Dayal, A. Singh, and K. Jain. "BUILDING FACADE AND ROOFTOP SEGMENTATION BY NORMAL ESTIMATION FROM UAV DERIVED RGB POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 173–77. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-173-2019.
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Abstract. Point cloud segmentation is a significant process to organise an unstructured point cloud. In this study, RGB point cloud was generated with the help of images acquired from an Unmanned Aerial Vehicle (UAV). A dense urban area was considered with varying planar features in the built-up environment along with buildings with different floors. Initially, using Cloth Simulation Filter (CSF) filter, the ground and the non-ground features in the Point Cloud Data (PCD) were segmented, with non-ground features comprising trees and buildings and ground features comprising roads, ground vegetation, and open land. Subsequently, using CANUPO classifier the trees and building points were classified. Noise filtering removed the points which have less density in clusters. Point cloud normals were generated for the building points. For segmentation building elements, normal vector components in different directions (X component, Y component and Z component) were used to segment out the facade, and the roof points of the buildings as the surface normals corresponding to the roof will have a higher contribution in the z component of the normal vector. The validation of the segmentation is done by comparing the results with manually identified roof points and façade points in the point cloud. Overall accuracies obtained for building roof and building facade segmentation are 90.86 % and 84.83 % respectively.
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Brown, N., M. R. Jackson, and R. M. Parkin. "Automatic gravure print feature determination at production speeds." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 217, no. 8 (August 1, 2003): 1101–10. http://dx.doi.org/10.1177/095440540321700807.
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This paper describes the development of a non-contact system for measuring the colour of printed material at web speeds, in addition to gravure-printed dot feature recognition. The system proposed uses a non-contact spectrophotometer based on a holographic grating, in conjunction with a conventional monochrome area scan camera, from which colour spectral data are extracted, while a xenon flash is used to illuminate colour samples. Dot features are captured by a magnification lens, in conjunction with a progressive scan camera. Software and hardware details of the system are given, together with the underlying mathematics for colour space conversion and measurement. Conversion equations from X, Y, Z chromaticity coordinates to the RGB system are presented, and also equations to convert from the L* a* b* colour space to X, Y, Z chromaticity coordinates. Experimental results are presented whereby the non-contact spectral system is shown to perform to a colour tolerance exceeding that of conventional colour video systems, and where performance of the dot feature system is comparable with traditional static examination methods using a microscope.
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Fatima, H. Sundus, Ammar N. Abbas, Faraz Bawany, Huma Zia, Syed Adnan Yusuf, and Muhammad Khurram. "Autonomous vehicle navigation using high-definition maps through CARLA-ROS simulator bridge." Journal of Physics: Conference Series 2330, no. 1 (August 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2330/1/012016.
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Abstract Autonomous vehicles (AV) have garnered significant interest in recent years due to its potential for controlling thousands of accidents that happen yearly due to human error. However, AV bring with it very complex and sophisticated requirements and challenges related to extensive testing of the algorithms and hardware in the physical world. The evolution of automotive simulation tools provides an opportunity to fully test and validate AV architectures without the risk of creating hazardous situations in real world. This research demonstrates the application of HD (High Definition) maps in autonomous vehicle navigation using ROS interface integrated with the CARLA (CAR Learning to Act) simulator. The sensor data includes Light Detection and Ranging (LiDAR), RGB-Depth (RGB-D), and vehicle odometry. HD maps play an important role in robustness of the autonomous systems where due to sensor obstruction or weather conditions the vehicle is unable to perceive the information ahead. It also aids the vehicle to sense its environment even outside from the sensor’s field of view. The research is divided into the three fundamental concepts of Simultaneous Localization and Mapping (SLAM) approach that is, (i) mapping, (ii) localization, and (iii) navigation. Two ROS tools are used for mapping, (a) OctoMap mapping, and (ii) Real-Time Appearance-Based Mapping (RTAB-Map). We demonstrate the effectiveness of localization using RTAB-Map and compare actual path, position and orientation to their estimated equivalents. Our results show acceptable error in XY axes and exemplifies the error accumulated in Z axis.
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Migliori, G., A. Siemiginowska, C. C. Cheung, A. Celotti, M. Giroletti, G. Giovannini, A. Paggi, and E. Liuzzo. "Discovery of a bright extended X-ray jet in RGB J1512+020A." Monthly Notices of the Royal Astronomical Society 512, no. 3 (February 24, 2022): 4639–59. http://dx.doi.org/10.1093/mnras/stac514.
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ABSTRACT We report the discovery of a bright, extended X-ray jet in the quasar RGB J1512+020A (z = 0.22). Chandra observations show the X-ray core and 13 arcsec (∼45 kpc projected) extended emission coincident with the radio jet. The jet stands out as one of brightest X-ray jets (LX ∼ 7 × 1043 erg s−1) at low redshift (z < 0.5) discovered so far, with remarkably large X-ray to radio luminosity ratios (LX/Lr up to ∼50). We identified four main components, two unresolved knots and two extended structures, one being the jet brightest feature (JBF). All jet features are also detected in ALMA archival observations. The radio, sub-mm to X-ray spectra of the two knots can be modelled with a single synchrotron component. For the two resolved structures, the ALMA data unveil a turnover of the low-energy continuum at ∼460 GHz. External Comptonization of cosmic microwave background photons can account for the observed X-ray emission if the jet speed remains highly relativistic, with bulk motion Γbulk > 15, up to tens of kiloparsec from the core. However, the comparison with the spectral energy distribution of similar X-ray detected jets shows that the alternative hypothesis of synchrotron emission from an additional population of ultra-high-energy electrons is also possible. We report a tentative (≳2σ) optical detection of the JBF in images of the Dark Energy Camera Legacy Survey. If confirmed, the optical emission should be either the low-energy tail of the radiative component responsible for the X-ray emission or a third, separate component.
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Ropelewska, Ewa, Wioletta Popińska, Kadir Sabanci, and Muhammet Fatih Aslan. "Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision." European Food Research and Technology 248, no. 3 (December 23, 2021): 893–98. http://dx.doi.org/10.1007/s00217-021-03935-3.
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AbstractThe aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.
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Bonatto, Charles, Ana L. Chies-Santos, Paula R. T. Coelho, Jesús Varela, Søren S. Larsen, A. Javier Cenarro, Izaskun San Roman, et al. "J-PLUS: A wide-field multi-band study of the M 15 globular cluster." Astronomy & Astrophysics 622 (February 2019): A179. http://dx.doi.org/10.1051/0004-6361/201732441.
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Context. As a consequence of internal and external dynamical processes, Galactic globular clusters (GCs) have properties that vary radially. Wide-field observations covering the entire projected area of GCs out to their tidal radii (rtidal) can therefore give crucial information on these important relics of the Milky Way formation era. Aims. The Javalambre Photometric Local Universe Survey (J-PLUS) provides wide field-of-view (2 deg2) images in 12 narrow, intermediate and broad-band filters optimized for stellar photometry. Here we have applied J-PLUS data for the first time for the study of Galactic GCs using science verification data obtained for the very metal-poor ([Fe/H] ≈−2.3) GC M 15 located at ~10 kpc from the Sun. Previous studies based on spectroscopy found evidence of multiple stellar populations (MPs) through their different abundances of C, N, O, and Na. Our J-PLUS data provide low-resolution spectral energy distributions covering the near-UV to the near-IR, allowing us to instead search for MPs based on pseudo-spectral fitting diagnostics. Methods. We have built and discussed the stellar radial density profile (RDP) and surface brightness profiles (SBPs) reaching up to rtidal. Since J-PLUS FoV is larger than M 15’s rtidal, the field contamination can be properly taken into account. We also demonstrated the power of J-PLUS unique filter system by showing colour-magnitude diagrams (CMDs) using different filter combinations and for different cluster regions. Results. J-PLUS photometric quality and depth are good enough to reach the upper end of M 15’s main-sequence. CMDs based on the colours (u − z) and (J0378 − J0861) are found to be particularly useful to search for splits in the sequences formed by the upper red giant branch (RGB) and asymptotic giant branch (AGB) stars. We interpret these split sequences as evidence for the presence of MPs. Furthermore, we show that the (u − z) × (J0378 − g) colour–colour diagram allows us to distinguish clearly between field and M 15 stars, which is important to minimize the sample contamination. Conclusions. The J-PLUS filter combinations (u − z) and (J0378 − J0861), which are sensitive to metal abundances, are able to distinguish different sequences in the upper RGB and AGB regions of the CMD of M 15, showing the feasibility of identifying MPs without the need of spectroscopy. This demonstrates that the J-PLUS survey will have sufficient spatial coverage and spectral resolution to perform a large statistical study of GCs through multi-band photometry in the coming years.
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Gmira, Faiq, Wafae Sabbar, and Said Hraoui. "A Low Computational Cost RGB Color Image Encryption Scheme Process based on PWLCM Confusion, Z/nZ Diffusion and ECBC Avalanche Effect." Journal of ICT Research and Applications 16, no. 3 (December 31, 2022): 244–59. http://dx.doi.org/10.5614/itbj.ict.res.appl.2022.16.3.4.
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In this work, three sub-processes are serially integrated into just one process in order to construct a robust new image encryption scheme for all types of images, especially color images. This integration architecture aims to create a robust avalanche effect property while respecting the constraints of confusion and diffusion that have been identified by Claude Shannon as properties required of a secure encryption scheme. The performance of the proposed encryption scheme is measured and discussed with several analyses, including computational cost analysis, key space analysis, randomness metrics analysis, histogram analysis, adjacent pixel correlation, and entropy analysis. The experimental results demonstrated and validated the performance and robustness of the proposed scheme.
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Peng, Zhao, Li Yue, and Ning Xiao. "Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme." International Journal of Optics 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7049523.
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Wood grading and wood price are mainly connected with the wood defect and wood species. In this paper, a wood defect quantitative detection scheme and a wood species qualitative identification scheme are proposed simultaneously based on 3D laser scanning point cloud. First, an Artec 3D scanner is used to scan the wood surface to get the 3D point cloud. Each 3D point contains its X, Y, and Z coordinate and its RGB color information. After preprocessing, the Z coordinate value of current point is compared with the set threshold to judge whether it is a defect point (i.e., cavity, worm tunnel, and crack). Second, a deep preferred search algorithm is used to segment the retained defect points marked with different colors. The integration algorithm is used to calculate the surface area and volume of every defect. Finally, wood species identification is performed with the wood surface’s color information. The color moments of scanned points are used for classification, but the defect points are not used. Experiments indicate that our scheme can accurately measure the surface areas and volumes of cavity, worm tunnel, and crack on wood surface with measurement error less than 5% and it can also reach a wood species recognition accuracy of 95%.
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Voronin, V., M. Zhdanova, E. Semenishchev, A. Zelensky, and S. Agaian. "An active contour model for medical image segmentation using a quaternion framework." Electronic Imaging 2020, no. 10 (January 26, 2020): 62–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-062.
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This paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. The experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods.
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Roman-Rivera, Luis-Rogelio, Jesus Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguín, Efrén Gorrostieta-Hurtado, and Saúl Tovar-Arriaga. "A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC." Mathematics 11, no. 4 (February 17, 2023): 1023. http://dx.doi.org/10.3390/math11041023.
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Three-dimensional vision cameras, such as RGB-D, use 3D point cloud to represent scenes. File formats as XYZ and PLY are commonly used to store 3D point information as raw data, this information does not contain further details, such as metadata or segmentation, for the different objects in the scene. Moreover, objects in the scene can be recognized in a posterior process and can be used for other purposes, such as camera calibration or scene segmentation. We are proposing a method to recognize a basketball in the scene using its known dimensions to fit a sphere formula. In the proposed cost function we search for three different points in the scene using RANSAC (Random Sample Consensus). Furthermore, taking into account the fixed basketball size, our method differentiates the sphere geometry from other objects in the scene, making our method robust in complex scenes. In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. Results show our methodology converges in finding the basketball in the scene and the center precision improves using z-score, the proposed method obtains a significant improvement by reducing outliers in scenes with noise from 1.75 to 8.3 times when using RANSAC alone. Experiments show our method has advantages when comparing with novel deep learning method.
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Ramírez-Rojas, Christian, Cecilia Beatriz Peña-Valdivia, Antonio García-Esteva, and Daniel Padilla-Chacón. "Fenotipo de plantas de maíz con efecto del herbicida mesotrione." Revista Mexicana de Ciencias Agrícolas 13, no. 8 (December 13, 2022): 1399–410. http://dx.doi.org/10.29312/remexca.v13i8.2886.
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Mesotrione es un herbicida que se emplea para el control de un amplio espectro de malezas durante la pre y pos-emergencia en el cultivo de maíz (Z. mays L). El objetivo del presente estudio fue evaluar los efectos de la mesotrione en el crecimiento, pigmentación con imágenes en el espectro visible (rojo, verde y azul, RGB) y fluorescencia (Fv/Fm) en los cultivares de maíz Cacahuacintle, HS-2 y Vitamaíz. Las plantas fueron cultivadas en invernadero, en el Colegio de Postgraduados, Campus Montecillo durante 2020, se analizaron en la etapa vegetativa V3-V4 con un diseño de bloques completos al azar y con arreglo factorial. Los tratamientos evaluados fueron: testigos (agua y coadyuvante sin mesotrione) y mesotrione 1X y 2X. Diez días después de la aplicación, se obtuvieron imágenes de las plantas, las cuales se analizaron con el programa LemnaGrid. Ninguna de las dosis de mesotrione alteró el crecimiento de los cultivares; aunque sí hubo efectos en el color de las plantas. La pérdida de color verde (clorosis) se presentó en más de 50% de la base de la lámina foliar y con manchas en el ápice. Las imágenes de la fluorescencia de la clorofila y el índice Fv/Fm en fragmentos de hojas, indicaron que la dosis mayor de mesotrione (2X) en los cultivares Cacahuacintle y Vitamaíz mantuvieron valores similares a los testigos; en contraste, esos valores en HS-2 disminuyeron. Los resultados del presente estudio demostraron la utilidad del fenotipo no invasivo, con imágenes RGB y de la fluorescencia de la clorofila para evaluar el efecto de herbicidas en los cultivos.
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Ropelewska, Ewa. "Effect of boiling on classification performance of potatoes determined by computer vision." European Food Research and Technology 247, no. 4 (January 30, 2021): 807–17. http://dx.doi.org/10.1007/s00217-020-03664-z.
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AbstractThe aim of this study was to evaluate the effect of potato boiling on the correctness of cultivar discrimination. The research was performed in an objective, inexpensive and fast manner using the image analysis technique. The textures of the outer surface of slice images of raw and boiled potatoes were calculated. The discriminative models based on a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z, U, V, S), textures selected for color spaces and textures selected for individual color channels were developed. In the case of discriminant analysis of raw potatoes of cultivars ‘Colomba’, ‘Irga’ and ‘Riviera’, the accuracies reached 94.33% for the model built based on a set of textures selected from all color channels, 94% for Lab and XYZ color spaces, 92% for color channel b and 92.33% for a set of combined textures selected from channels B, b, and Z. The processed potatoes were characterized by the accuracy of up to 98.67% for the model including the textures selected from all color channels, 98% for RGB color space, 95.33% for color channel b, 96.67% for the model combining the textures selected from channels B, b, and Z. In the case of raw and processed potatoes, the cultivar ‘Irga’ differed in 100% from other potato cultivars. The results revealed an increase in cultivar discrimination accuracy after the processing of potatoes. The textural features of the outer surface of slice images have proved useful for cultivar discrimination of raw and processed potatoes.
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Possoch, M., S. Bieker, D. Hoffmeister, A. Bolten, J. Schellberg, and G. Bareth. "MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 991–98. http://dx.doi.org/10.5194/isprs-archives-xli-b1-991-2016.
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Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).
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Possoch, M., S. Bieker, D. Hoffmeister, A. Bolten, J. Schellberg, and G. Bareth. "MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 991–98. http://dx.doi.org/10.5194/isprsarchives-xli-b1-991-2016.
Full textAbstract:
Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).
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Kumari, Manisha, and Sarun Murikkoli. "High-Temperature Impedance, Modulus Spectroscopy, and Conductivity Study of Nd Modified Barium Stannate Titanate Electroceramics." ECS Transactions 107, no. 1 (April 24, 2022): 11497–508. http://dx.doi.org/10.1149/10701.11497ecst.
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(Nd1−2x/3Bax)(Ti0.95Sn0.05)O3: NBTS electroceramics with different concentrations of Nd doping, i.e. x = 0.00 - 1.5 mol % are prepared. Structural characterization reveals the formation of single-phase tetragonal symmetry. Highly dense microstructures are observed in FE-SEM micrographs. Impedance, modulus, and conductivity studies are done at 350-500°C temperature. Negative temperature coefficient: NTC resistance nature is confirmed from the Nyquist analysis. Impedance spectra analysis reveals that grain resistance (Rg ) and grain boundary resistance (Rgb ) are dependent on temperature and Nd concentration in the NBTS ceramics. The frequency-dependent imaginary part of impedance (Z′′ ) and electrical modulus (M′′ ) reveal the localization of relaxation in NBTS ceramics at high temperatures. The activation energy obtained from the relaxation peak and the scaling formalism reveals that the conductivity is attributed to the oxygen ion vacancies. Conductivity study suggests the variation of frequency exponent, which follows correlated barrier hopping (CBH) model. These studies suggest that NBTS ceramics are potential candidates for future power electronics applications at high temperatures.
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Gudrun Kalmbach H.E. "4-dimensional lattice models for the quantum range." World Journal of Advanced Research and Reviews 12, no. 1 (October 30, 2021): 175–81. http://dx.doi.org/10.30574/wjarr.2021.12.1.0003.
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Some finite subspace models L are presented for quantum structures which replace the use of countable infinite Hilbert space H dimensions. A maximal Boolean sublattice, called block, is 24, where its four atoms directly above 0εL, base vectors of H in 24 are drawn as four points on an interval. Blocks can overlap in one or two atoms. Different kinds of operators can map one block onto another and interpretations are given such that subspaces can carry on their base vector tuple real, complex or quaternionic numbers, energies, symmetries and generate coordinate lines. Describing states of physical systems is done using L and its applications for dynamical modelling. They don‘t need the infinte dimensional vectors of H. L has in the first model 11 blocks and 24 atoms (figure 1). They correspond to the 24 elements of the tetrahedral S4 symmetry. S4 arises from a spin-line rgb-graviton whirl operator with center at the tip of a tetrahedron and a nucleon triangle base with three quarks as vertices. The triangles factor group D3 of S4 is due to the CPT Klein normal subgroup Z2 x Z2 of S4 . It has a strong interaction SI rotor for the nucleons inner dynamics which is used for integrating functions, exchanging energies of nucleon with its environment and setting barycentrical coordinates in the triangle. At their intersection B as barycenter sets a Higgs boson or field the rescaled quark mass of a nucleon. Each factor class of one element from D3 assigns to it a color charge, a coordinate, an energy vector and a symmetry. Symmetries attached can be different according to interactions involved. Every atom of L has then a specific character with different properties.Three characters are added to octonian base vectors, listed by their indices as n = 0,1,…,7, and named for the atoms of L as na, nb, nc. The structure and element attributes of the finite subspace lattices L are desribed in many examples and models which technical constructed run macroscopically. Several models are described below. Example, the color charge whirl as rgb-graviton projection operator maps the block 2c3b5a6a to 0a1a2a3a. The symmetries change dimension from 3x3- to 2x2-matrices. From SU(3) are λ1 on 3b mapped to the SU(2) x-coordinate Pauli matrix σ1, from λ2 on 5a to σ2 y-coordinate and from λ3 on 6a to σ3 z-coordinate of real Euclidean space R³. The SU(3) matrices have complex w3 = z +ict, w2 = (iy,f), w1 = (x,m) coordinates. In figure 3 is shown how a rotation of two proton tetrahedrons for fusion changes the two linearly independent wj vectors to the 1-dimensional x,y,z base vectors. In deuteron then on one coordinate line sit with Cooper paire u-d-quarks at the ends the Heisenberg coupled energy or space vector rays 15 (x,m), m mass measured in kg, x in meter, 23 (iy,E(rot)), E(rot) rotational energy measured in Joule J, y in meter, 46 (ict,f), t time measured in seconds, f = 1/∆t frequency s inverse time interval measured in Hz. The six color charges are red r on +x as octonian coordinate 1, green g on +y as 2 , blue b on -z as 6, turquoise on -x as 5, magenta on -y as 3, yellow on +z as 4..
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Song, Fen, Yan Li, Tao Wu, Adriano Pietrinferni, Helen Poon, and Yi Xie. "The Influence of the Metal Mass Fraction Z, Age, and Mixing-length Parameter on the RGB Bump Magnitude for the M4 Cluster." Astrophysical Journal 869, no. 2 (December 17, 2018): 109. http://dx.doi.org/10.3847/1538-4357/aaecd3.
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Ropelewska, Ewa. "Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)." Foods 11, no. 22 (November 11, 2022): 3589. http://dx.doi.org/10.3390/foods11223589.
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The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage.
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Lee, Sang-Hee, Cheol-Min Park, and UJin Choi. "A New Measure to Characterize the Degree of Self-Similarity of a Shape and Its Applicability." Entropy 22, no. 9 (September 22, 2020): 1061. http://dx.doi.org/10.3390/e22091061.
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We propose a new measure (Γ) to quantify the degree of self-similarity of a shape using branch length similarity (BLS) entropy which is defined on a simple network consisting of a single node and its branches. To investigate the properties of this measure, we computed the Γ values for 70 object groups (20 shapes in each group) in the MPEG-7 shape database and performed grouping on the values. With relatively high Γ values, identical groups had visually similar shapes. On the other hand, the identical groups with low Γ values had visually different shapes. However, the aspect of topological similarity of the shapes also warrants consideration. The shapes of statistically different groups exhibited significant visual difference from each other. Also, in order to show that the Γ can have a wide variety of applicability when properly used with other variables, we showed that the finger gestures in the (Γ, Z) space are successfully classified. Here, the Z means a correlation coefficient value between entropy profiles for gesture shapes. As shown in the applications, Γ has a strong advantage over conventional geometric measures in that it captures the geometrical and topological properties of a shape together. If we could define the BLS entropy for color, Γ could be used to characterize images expressed in RGB. We briefly discussed the problems to be solved before the applicability of Γ can be expanded to various fields.
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Migliori, Giulia. "Tracing the high energy emission of γ-ray detected radio loud quasars." Proceedings of the International Astronomical Union 10, S313 (September 2014): 225–30. http://dx.doi.org/10.1017/s1743921315002227.
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AbstractWe present a multiwavelength study of the core and relativistic jet of the radio loud (RL) quasar RGB J1512+020A (z=0.20). We report the discovery of a bright, 13” extended X-ray jet with a short Chandra observation. We discuss the origin of the jet X-ray emission and its properties in comparison with sample of X-ray quasar jets. The broadband core spectrum is contributed by the emission of the central quasar, by a blazar component, responsible for the γ-ray emission detected by Fermi, and by the host galaxy. We model the non-thermal blazar spectral energy distribution (SED) and constrain the total jet power. The jet power inferred from the blazar SED modeling is in agreement with the values obtained from the total radio power, pointing to a jet that efficiently carries its power up to kiloparsec scales. The quasar emission appears intrinsically weak in the optical-UV band. The disk luminosity estimated from the broad emission lines is lower than the jet power, in agreement with recent results from observations and theory.
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Chen, Kai-Yu, Li-Wei Chou, Hui-Min Lee, Shuenn-Tsong Young, Cheng-Hung Lin, Yi-Shu Zhou, Shih-Tsang Tang, and Ying-Hui Lai. "Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model—An Example of Forward Reaching." Sensors 22, no. 1 (December 31, 2021): 292. http://dx.doi.org/10.3390/s22010292.
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Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s2) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.
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Dorman, Ben, and Robert W. O'Connell. "The Ultraviolet Spectra of Old Stellar Populations." Symposium - International Astronomical Union 171 (1996): 367. http://dx.doi.org/10.1017/s0074180900232865.
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We investigate the utility of UV spectra of old stellar populations as diagnostics of galaxy properties. For λ < 3800 Å, the integrated light of old stellar populations is dominated by two components: the hot stars which give rise to the UV upturn phenomenon at far-UV wavelengths, and the stars (i.e. main sequence, and subgiants) that lie closest to the turnoff. The mid-UV radiation from the turnoff varies strongly with metallicity, and less so with age; mid-UV light must be correct for the effect of the independent far-UV upturn component, but this is straightforward. Population synthesis models that account for the flux from the UV upturn can therefore determine the characteristics of the underlying stellar content from the mid-UV spectral region. The age & metallicity dependence of the far-UV 1550 Å is not well understood (see Dorman, O'Connell & Rood 1995, ApJ 442,105) since the mass loss mechanism on the RGB that produces very blue HB stars has no physical model. In contrast mid-UV indicators derive from the turnoff population which best represents the quantities we wish to measure, and are in addition visible at favourable optical wavelengths for z ≲ 1.
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Tomczyk, Szymon, Małgorzata Malkiewicz, and Małgorzata Werner. "Możliwości zastosowania oprogramowania DeltaPix InSight w procesie zliczania ziaren pyłku leszczyny, olszy i brzozy." Alergoprofil 19, no. 2 (September 14, 2023): 20–30. http://dx.doi.org/10.24292/01.ap.192140923.
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Niniejsza praca ma na celu ocenę możliwości zastosowania oprogramowania DeltaPix InSight do automatycznego rozpoznawania ziaren pyłku leszczyny (Corylus sp.), olszy (Alnus sp.) i brzozy (Betula sp.). Analiza dotyczyła danych dobowych ze stacji monitoringu we Wrocławiu i została przeprowadzona dla 2 tygodni sezonu 2023.Oprogramowanie DeltaPix wykorzystywało 5 kryteriów do rozpoznawania ziaren pyłku, tj.: barwy ustalonej za pomocą trzech rang RGB, morfologii szukanych obiektów, jako etap preprocessingu oraz progów dotyczących powierzchni, kształtu i średnicy Fereta dla wyznaczonych przez program obszarów, które odpowiadają 2 pierwszym kryteriom.Wyniki zliczania automatycznego porównano z wynikami obserwacji manualnej i podsumowano przy użyciu miar trafności prognoz. Przeprowadzona analiza wykazała, że zastosowanie 4 pierwszych kryteriów pozwala uzyskać najlepsze wyniki. Przy dołączeniu progu związanego ze średnicą Fereta oprogramowanie znacznie gorzej radziło sobie ze zliczaniem materiału pyłkowego. Rezultaty najbardziej zbliżone do wyników obserwacji manualnej udało się uzyskać przede wszystkim w czasie tygodnia, kiedy przeważał pyłek olszy oraz leszczyny. Znacznie gorsze wyniki dotyczą rozpoznawania brzozy ze względu na dużą liczbę ziaren, w tym podobnych do pyłku brzozy (np. dębu), a także zanieczyszczenia na preparacie (fragmenty roślin), co skutkowało licznymi fałszywymi sygnałami. Weryfikacja pozwoliła zidentyfikować główne zalety i wady oprogramowania oraz określić, kiedy może być ono pomocne w zliczaniu ziaren pyłku.
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Nandu, Purushottam Kumar, Samir E. Topno, V. M. Prasad, and Nini R. Kuotsu. "Effect of Salinity on Warm Season Turf Grass Species." International Journal of Plant & Soil Science 35, no. 19 (August 19, 2023): 316–26. http://dx.doi.org/10.9734/ijpss/2023/v35i193558.
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The demand for salinity tolerant turf grasses is increasing due to augmented use of effluent or low-quality water for turf irrigation. Fresh water, along with soil Stalinization in many locations, has raised the requirement for salt-tolerant turf grass screening. Physiological responses to salinity and relative salt tolerance of Five C4 turf grasses, two variety ofCynodon dactylon(Bermuda grass), Zoysia matrella (Manilla grass), Zoysia japonica(Zoysia Grass) and Paspalum notatum(Bahia grass) were investigated during the study at Department of Horticulture Sam Higginbottom University of Agriculture Technology and Science, Prayagraj. Turf grasses were planted in plastic pots filled with sand: cocopeat: vermiculite (2:1:1) and irrigated with different concentration of salinity levels (0.6, 5.0, 10.0, 15 dS m-1). Salinity tolerance was evaluated based on leaf length leaf Wirth leaf firing, shoots and root growth reduction, proline content, total chlorophyll content RGB content and relative water content was subjected to analysis of variance. Among the four turf grass species Bahia grass found to be most salt tolerant turf grass followed by Z. matrella while Zosia japonica was least tolerant turf grass against salt stress.
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Yaron, O., A. Kovetz, and D. Prialnik. "“Hot Helium Flashers” – The Road to Extreme Horizontal Branch Stars." Proceedings of the International Astronomical Union 4, S252 (April 2008): 261–62. http://dx.doi.org/10.1017/s1743921308022965.
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AbstractObservational and theoretical investigations, performed especially over the last two decades, have strongly attributed the far-UV upturn phenomenon to low-mass, small-envelope, He-burning stars in Extreme Horizontal Branch (EHB) and subsequent evolutionary phases.Using our new stellar evolution code – a code that follows through complete evolutionary tracks, Pre-MS to cooling WD – without any interruption or intervention, we are able to produce a wide array of EHB stars, lying at bluer (Teff ≥ 20,000 K) and less luminous positions on HRD, and also closely examine their post-HB evolution until the final cooling as White Dwarfs.HB morphology is a complex multiple parameter problem. Two leading players, which seem to possess the ability to affect considerably positions of HB, are those of: 1.Helium abundance, and 2.mass-loss efficiency on the first giant branch. We focus here on the latter; thus, EHB stars are produced in our calculations by increasing the mass-loss rate on the RGB, to a state where prior to reaching core He flash conditions, only a very small H-rich envelope remains. The core flash takes place at hotter positions on the HRD, sometimes while already descending on the WD cooling curve. We show preliminary results for a range of initial masses (MZAMS = 0.8 − 1.1 M⊙) and for metallicities covering both populations I and II (Z = 0.01 − 0.001). The [M,Z] combinations have been chosen such that the masses would be above and close to typical MS turnoff masses (e.g. the estimation of MTO ≃ 0.85 for NGC 2808), and also so that the ages at HB are of order of 10 ± 5 Gyr.
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Li, Yuwen, Wei Wang, Xiaohuan Guo, Xiaorong Wang, Yizhe Liu, and Daren Wang. "Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing." Agriculture 14, no. 4 (April 17, 2024): 624. http://dx.doi.org/10.3390/agriculture14040624.
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To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look Once version 7 (YOLOv7) object detection algorithm and Red Green Blue-Depth (RGB-D) sensing. Modifications to the YOLOv7 model include the integration of more efficient modules, incorporation of attention mechanisms, elimination of superfluous feature layers, and the addition of layers dedicated to the detection of smaller targets. These modifications have culminated in a lightweight and improved YOLOv7 network model. The number of parameters is only 40.3% of that of the original model. The calculation amount is reduced by 41.8% and the model size by 59.2%. The recognition speed and accuracy are also both improved. The frame rate of model recognition is increased by 19.3%, the accuracy of model recognition reaches 98.8%, and mAP@0.95 reaches 96.8%. In addition, we have developed a method for locating strawberry picking points based on strawberry geometry. The test results demonstrated that the average positioning success rate and average positioning time were 90.8% and 76 ms, respectively. The picking robot in the laboratory utilized the recognition and positioning method proposed in this paper. The error of hand–eye calibration is less than 5.5 mm on the X-axis, less than 1.6 mm on the Y-axis, and less than 2.7 mm on the Z-axis, which meets the requirements of picking accuracy. The success rate of the picking experiment was about 90.8%, and the average execution time for picking each strawberry was 7.5 s. In summary, the recognition and positioning method proposed in this paper provides a more effective method for automatically picking elevated-substrate strawberries.
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Cabra Lopez, Jose-Luis, Carlos Parra, Libardo Gomez, and Luis Trujillo. "Sex Recognition through ECG Signals aiming toward Smartphone Authentication." Applied Sciences 12, no. 13 (June 29, 2022): 6573. http://dx.doi.org/10.3390/app12136573.
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Physiological signals are strongly related to a person’s state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% ± 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem.
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Theodorou, Charalambos, Vladan Velisavljevic, and Vladimir Dyo. "Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal." Sensors 22, no. 19 (October 5, 2022): 7553. http://dx.doi.org/10.3390/s22197553.
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In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracted feature points is capable of achieving 2–4% better accuracy compared to VPS-SLAM and DS-SLAM. Static feature points such as signs and benches were used to calculate the camera position, and dynamic moving objects were eliminated by using the tracking thread. A specific custom personal dataset that includes indoor and outdoor RGB-D pictures of train stations, including dynamic objects and high density of people, ground truth data, sequence data, and video recordings of the train stations and X, Y, Z data was used to validate and evaluate the proposed method. The results show that ORB-SLAM3 with YOLOR as object detection achieves 89.54% of accuracy in dynamic indoor environments compared to previous systems such as VPS-SLAM.