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Journal articles on the topic 'Hue classification'

Author: Grafiati

Published: 23 September 2022

Last updated: 31 July 2025

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1

Zhao, Yan, and Shuai Liu. "Robust Image Hashing Based on Cool and Warm Hue and Space Angle." Security and Communication Networks 2021 (July 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/3803481.

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Image hashing has attracted more and more attention in the field of information security. In this paper, a novel hashing algorithm using cool and warm hue information and three-dimensional space angle is proposed. Firstly, the original image is preprocessed to get the opposite color component and the hue component H in HSV color space. Then, the distribution of cool and warm hue pixels is extracted from hue component H. Blocks the hue component H, according to the proportion of warm hue and cool hue pixels in each small block, combined with the quaternion and opposite color component, construc

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Shewfelt, R. L., J. K. Brecht, and C. N. Thai. "CLASSIFICATION OF TOMATO RIPENESS AND MATURITY BY FOOD COLORIMETRY." HortScience 27, no. 6 (1992): 651b—651. http://dx.doi.org/10.21273/hortsci.27.6.651b.

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Tomato ripeness is currently assessed by a subjective visual classification scheme based on external color while maturity of green fruit is based on a destructive evaluation of internal locule development. In an effort to develop an objective method of tomato maturity and ripeness classification, external color measurements were performed on fresh, sized (6×7) `mature-green' tomatoes (cv “Sunny') initially and through ripening using a Gardner XL-845 colorimeter. Hue angle (tan-1 b/a, designated θ) provided the best objective means of ripeness classification with proposed ranges for mature-gree

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Bible, Bernard B., and Richard J. McAvoy. "A CIELAB Color Classification Scheme for Poinsettias." HortScience 32, no. 3 (1997): 456F—456. http://dx.doi.org/10.21273/hortsci.32.3.456f.

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Forty-two poinsettia cultivars were grown as a 15-cm single-plant pinched crop at 21/16.5°C (day/night) temperatures during Fall 1995 with standard commercial practices for irrigating, fertilizing, and pest control. On 7 Dec., 156 consumers rated the cultivars for their overall appeal. On 11 Dec., color coordinate (CIELAB) readings for bracts and leaves were taken with a Minolta 200b colorimeter. The colorimeter was set to illuminate C and has a 8-mm aperture. Bracts and leaves were placed on a white tile background for colorimetric readings. In 1996, a similar evaluation was conducted with 55

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Diop, Papa Moussa, Naoki Oshiro, Morikazu Nakamura, Jin Takamoto, and Yuji Nakamura. "Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species." AgriEngineering 5, no. 2 (2023): 1005–19. http://dx.doi.org/10.3390/agriengineering5020063.

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This paper presents a machine learning approach to automatically classifying post-harvest vegetal species. Color images of vegetal species were applied to convolutional neural networks (CNNs) and support vector machine (SVM) classifiers. We focused on okra as the target vegetal species and classified it into two quality types. However, our approach could also be applied to other species. The machine learning solution consists of several components, and each design process and its combinations are essential for classification quality. Therefore, we carefully investigated their effects on classi

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Pradeep M and Dr. M Siddappa. "CLASSIFICATION OF RICE USING CONVOLUTIONAL NEURAL NETWORK (CNN)." international journal of engineering technology and management sciences 7, no. 5 (2023): 455–63. http://dx.doi.org/10.46647/ijetms.2023.v07i05.056.

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This paper describes the technique for automatic recognition and classification of different rice grain samples using neural network classifier. The Red Green Blue (RGB), Hue Saturation Intensity (HSI) and Hue Saturation Value (HSV) color models of the image were considered for extracting 18 color features. The classification was carried out using color and texture features separately. The color image was converted to Gray scale image and the Gray Level Co-occurrence Matrixes (GLCM) for four different directions was calculated. A total of eight texture features were calculated from the Co-occu

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Miller, David L. "Over the rainbow: The classification of unique hues." Behavioral and Brain Sciences 20, no. 2 (1997): 204–5. http://dx.doi.org/10.1017/s0140525x97431424.

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Saunders & van Brakel's analysis of the phenomenal categorization and subsequent experimental research in unique hues fails to include contemporary methodological improvements. Alternative strategies are offered from the author's research that rely less on language and world knowledge and provide strong evidence for the general theoretical constructs of elemental hue, nonbasic, and basic color terms.

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Kim, Taehyeong, Dae-Hyun Lee, Kyoung-Chul Kim, Taeyong Choi, and Jun Myoung Yu. "Tomato Maturity Estimation Using Deep Neural Network." Applied Sciences 13, no. 1 (2022): 412. http://dx.doi.org/10.3390/app13010412.

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In this study, we propose a tomato maturity estimation approach based on a deep neural network. Tomato images were obtained using an RGB camera installed on a monitoring robot and samples were cropped to generate a dataset with which to train the classification model. The classification model is trained using cross-entropy loss and mean–variance loss, which can implicitly provide label distribution knowledge. For continuous maturity estimation in the test stage, the output probability distribution of four maturity classes is calculated as an expected (normalized) value. Our results demonstrate

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Park, DaEun, HaeRyung Hong, and YungKyung Park. "Fine Classification of Korean Skin Color by Tone and Hue." Journal of Korea Society of Color Studies 33, no. 3 (2019): 36–44. http://dx.doi.org/10.17289/jkscs.33.3.201908.36.

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Kim, Dong Sub, Da Uhm Lee, Jeong Ho Lim, Steven Kim, and Jeong Hee Choi. "Agreement Between Visual and Model-Based Classification of Tomato Fruit Ripening." Transactions of the ASABE 63, no. 3 (2020): 667–74. http://dx.doi.org/10.13031/trans.13812.

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Highlights Human visual classification and predictive models often disagree when only color indices are used. The degree of agreement is improved significantly when predictive models are cultivar-specific. The degree of agreement can be improved when firmness and carotenoid contents are considered. Abstract. Traditionally, the ripening stage of tomato fruit is determined by the observed percentage of red color on the fruit surface based on color charts provided by USDA standards. However, multiple observers can assign different ripening stages to the same tomato fruit due to subjectivity and/o

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S, Jeyalakshmi, and Radha R. "An effective approach to feature extraction for classification of plant diseases using machine learning." Indian Journal of Science and Technology 13, no. 32 (2020): 3295–314. https://doi.org/10.17485/IJST/v13i32.827.

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Abstract <strong>Objectives:</strong> To make automatic classification of diseased potato and grape leaf from normal potato and grape leaf. <strong>Methods:</strong> Experimental sample size of 3000 and 4270 Potato and Grape leaf images were used respectively. The diseased and healthy leaf image samples were taken from PlantVillage dataset. The color features viz., average Red, Green, Blue and Hue intensities of Lesion region were calculated. Features namely Contrast, Dissimilarity, Homogeneity, Energy, Correlation, ASM, and Entropy were extracted from hue lesion region. Also, h

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Lazaro, Antonio, Marti Boada, Ramon Villarino, and David Girbau. "Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor." Sensors 19, no. 7 (2019): 1741. http://dx.doi.org/10.3390/s19071741.

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This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are co

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Smith, Stacey D. "Quantifying Color Variation: Improved Formulas for Calculating Hue with Segment Classification." Applications in Plant Sciences 2, no. 3 (2014): 1300088. http://dx.doi.org/10.3732/apps.1300088.

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Nguyen, Thi Hong Hai, and Catherine Cheung. "The classification of heritage tourists: a case of Hue City, Vietnam." Journal of Heritage Tourism 9, no. 1 (2013): 35–50. http://dx.doi.org/10.1080/1743873x.2013.818677.

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Thoriq, Adhe Irham, Muhamad Haris Zuhri, Purwanto Purwanto, Pujiono Pujiono, and Heru Agus Santoso. "Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method." Journal of Development Research 6, no. 1 (2022): 11–15. http://dx.doi.org/10.28926/jdr.v6i1.200.

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Banana or Musa Paradisiaca is one type of fruit that is often found in Southeast Asia. The most popular is the Raja banana (Musa paradisiaca L.). The advantage of the plantain is that it has a fragrant aroma and is of medium size and has a very sweet taste that is appetizing when it is fully ripe. While the drawback of plantains is that they ripen quickly, if not handled properly, it can change the nutritional value and nutrients contained in plantains. In this study, the author focuses on identifying the level of ripeness of bananas using the image of a plantain fruit that is still intact and

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Al-Windi, Basim K. M. A., Amel H. Abbas, and Mohammed Shakir Mahmood. "Using Texture Analyses and Statistical Classification for Detection Plant Leaf Diseases." Al-Mustansiriyah Journal of Science 32, no. 5 (2021): 1–4. http://dx.doi.org/10.23851/mjs.v32i5.1115.

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The proposed method is based on classifying 15 types of plant leaf disease. Hue saturation value was used to delete the background and the healthy areas to show only the affected area in the image. Texture analyses adopted in image features extractions from the R component &G component &B component and creating 3 components which are RG and RB and GB color of the RGB color space images of diseased leaves. Building image classifier using statistical method for classification.

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Abadi, F. R., R. E. Masithoh, L. Sutiarso, and S. Rahayoe. "Effect of size reduction on yellow soybean seed characterization based on colorimetry." IOP Conference Series: Earth and Environmental Science 1116, no. 1 (2022): 012063. http://dx.doi.org/10.1088/1755-1315/1116/1/012063.

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Abstract Soybean seed, which is generally yellow in color, is the primary product of soybean plants sold in Indonesian market. To characterize non-destructively, it is necessary to understand the extent to which physical treatment, including size reduction, may affects the color characteristics. Therefore, this study aimed to determine the effect of size reduction of soybean seeds on its color parameters. A completely randomized design was performed with particle size factor with five levels and variety factor with four levels. Particle size included: intact seed; >595; 595-250 μm; 250-145

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Granados-López, D., A. García-Rodríguez, S. García-Rodríguez, A. Suárez-García, M. Díez-Mediavilla, and C. Alonso-Tristán. "Pixel-Based Image Processing for CIE Standard Sky Classification through ANN." Complexity 2021 (December 20, 2021): 1–15. http://dx.doi.org/10.1155/2021/2636157.

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Digital sky images are studied for the definition of sky conditions in accordance with the CIE Standard General Sky Guide. Likewise, adequate image-processing methods are analyzed that highlight key image information, prior to the application of Artificial Neural Network classification algorithms. Twenty-two image-processing methods are reviewed and applied to a broad and unbiased dataset of 1500 sky images recorded in Burgos, Spain, over an extensive experimental campaign. The dataset comprises one hundred images of each CIE standard sky type, previously classified from simultaneous sky scann

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Widodo, Agus, Fitra Dwi Prasetya, and Hendro Nugroho. "Implementasi Metode K-Nearest Neighbors (KNN) Guna Mengetahui Klasifikasi Kematangan Stroberi." KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika 3, no. 2 (2023): 31–36. http://dx.doi.org/10.31284/j.kernel.2022.v3i2.4185.

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To determine the maturity of the Strawberry fruit can be seen in the color of the fruit. The color of ripe strawberries can be seen in red and those that are not yet ripe are green. To determine the ripeness of strawberries, classification can be carried out on the fruit using feature extraction of the waena. The feature extraction results are classified using the K-Nearest Neighbors (KNN) method. The first method of classification of strawberry ripeness is (1) feature extraction using the Hue Saturation and Value (HSV) method, and (2) KNN. From the implementation results, the success rate of

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Mausfeld, Rainer J. "Why bother about opponency? Our theoretical ideas on elementary colour coding have changed our language of experience." Behavioral and Brain Sciences 20, no. 2 (1997): 203. http://dx.doi.org/10.1017/s0140525x97411421.

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There is no natural and pretheoretical classification of colour appearances into hue, saturation, brightness, unique hues, and so on. Rather, our theoretical insights into the coding of colour have reciprocally shaped the way we talk about colour appearances. Opponency is only one of many fundamental aspects of colour coding, and we are hardly justified in ascribing some theoretical prominance to it.

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Escuredo, Olga, María Shantal Rodríguez-Flores, Sergio Rojo-Martínez, and María Carmen Seijo. "Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)." Foods 8, no. 7 (2019): 233. http://dx.doi.org/10.3390/foods8070233.

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Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showe

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ROKUTANDA, Chie, and Takeshi NAKAGAWA. "THE CLASSIFICATION OF THE ARCHITECTURAL STYLES IN HUE NGUYEN DYNASTY ARCHITECTURAL REMAINS." Journal of Architecture and Planning (Transactions of AIJ) 78, no. 688 (2013): 1409–14. http://dx.doi.org/10.3130/aija.78.1409.

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Song, Lin, Huixuan Zhao, Zongfang Ma, and Qi Song. "A new method of construction waste classification based on two-level fusion." PLOS ONE 17, no. 12 (2022): e0279472. http://dx.doi.org/10.1371/journal.pone.0279472.

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The automatic sorting of construction waste (CW) is an essential procedure in the field of CW recycling due to its remarkable efficiency and safety. The classification of CW is the primary task that guides automatic and precise sorting. In our work, a new method of CW classification based on two-level fusion is proposed to promote classification performance. First, statistical histograms are used to obtain global hue information and local oriented gradients, which are called the hue histogram (HH) and histogram of oriented gradients (HOG), respectively. To fuse these visual features, a bag-of-

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Yanto, Vito Dwi, and Irma Handayani. "Implementation of The K-Means Clustering Algorithm in Determining The Rate of Indramayu Mango Fruit." Journal of Scientific Research, Education, and Technology (JSRET) 3, no. 4 (2024): 1929–38. https://doi.org/10.58526/jsret.v3i4.609.

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This research aims to classify the ripeness levels of Indramayu mangoes using the K-Means Clustering algorithm based on HSV (Hue, Saturation, Value) color features. The process begins with capturing mango images, followed by preprocessing steps such as normalization and resizing to enhance image quality. Next, color feature extraction is conducted, focusing on the Hue value as an indicator of color changes that characterize ripeness levels. The optimal number of clusters is determined using the Elbow method, resulting in two clusters: ripe mangoes and unripe mangoes. The clustering quality eva

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Przybył, Krzysztof, Piotr Boniecki, Krzysztof Koszela, Łukasz Gierz, and Mateusz Łukomski. "Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process." Czech Journal of Food Sciences 37, No. 2 (2019): 135–40. http://dx.doi.org/10.17221/427/2017-cjfs.

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The research methodology consists of several stages to develop a noninvasive method of identifying the turgor of potato tubers during the storage. During the first stage, a graphic database (set of training data) has been created for selected varieties of potatoes. As a next step, special proprietary software called ’PID system’ was used together with a commercial MATLAB package to extract parameters defining the digital image descriptors. This included: hue space models, shape coefficient and image texture. Thirdly, Artificial Neural Network (ANN) training was conducted with the use of Statis

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Efendi, Ayu Mahriza Agustin, Sriani Sriani, and Muhammad Siddik Hasibuan. "Classification of Watermelon Ripeness Levels Using HSV Color Space Transformation and K-Nearest Neighbor Method." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 3 (2024): 934–48. http://dx.doi.org/10.47709/cnahpc.v6i3.3999.

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Watermelons had high appeal due to their sweet taste, refreshing nature, and numerous benefits. However, consumers often faced difficulties in selecting suitable fruit because of the subtle differences between fully ripe and half-ripe watermelons. One important indicator of a watermelon’s ripeness was the yellowish pattern on its skin. In this study, the proposed use of digital image processing methods, specifically the HSV Color Space Transformation, was aimed at extracting watermelon images and employing the K-Nearest Neighbor (K-NN) method to classify them into two categories: "Ripe" and "H

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Jirsa, Ondřej, and Ivana Polišenská. "Identification of Fusarium damaged wheat kernels using image analysis." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 5 (2011): 125–30. http://dx.doi.org/10.11118/actaun201159050125.

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Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (D

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Purnamasari, Detty, Koko Bachrudin, Dede Herman Suryana, and Robert Robert. "Classification of meat using the convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1845–53. https://doi.org/10.11591/ijai.v12.i4.pp1845-1853.

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Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image for the tes

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Memon, Mehak Maqbool, Manzoor Ahmed Hashmani, Aisha Zahid Junejo, Syed Sajjad Rizvi, and Adnan Ashraf Arain. "A Novel Luminance-Based Algorithm for Classification of Semi-Dark Images." Applied Sciences 11, no. 18 (2021): 8694. http://dx.doi.org/10.3390/app11188694.

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Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sop

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Wang, Fu Juan. "Chinese Date Grading Based on Computer Vision." Advanced Materials Research 838-841 (November 2013): 3283–86. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.3283.

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In order to implement the accuracy and robust classification of Chinese dates according to size and color based on computer vision techniques on line, the method of classification according to size and color for Chinese date was studied. Taking the black rollers as background, the Chinese date images were pre-segmented by double thresholds in RGB color space. Through morphological operation, contour trace and region fill, the whole Chinese date target was obtained. the maximum diameter value was used to be the character value for size classification. The difference of saturation and hue of per

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Purnamasari, Detty, Koko Bachrudin, Dede Herman Suryana, and Robert Robert. "Classification of meat using the convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1845. http://dx.doi.org/10.11591/ijai.v12.i4.pp1845-1853.

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<span>Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image

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Mandal, Satyendra Nath, Sanket Dan, Pritam Ghosh, et al. "Pig Breeds Classification using Neuro-Statistic Model." Science & Technology Journal 7, no. 2 (2019): 78–88. http://dx.doi.org/10.22232/stj.2019.07.02.10.

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Image classification using fully connected neural network is not efficient due to huge number of parameters in each layer. In this paper, we propose a Neuro-Statistic model for classification of five different pig breeds from pig images. The model consists of four sub modules which work together as a layered structure. We captured multiple individual pig images of five different pig breeds from different organized farms to conduct this research, segmented the captured pig images using hue based segmentation algorithm and then calculated the statistical properties like entropy, standard deviati

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Lee, Sang Hwa, and Jung-Yoon Kim. "Classification of the Era Emotion Reflected on the Image Using Characteristics of Color and Color-Based Classification Method." International Journal of Software Engineering and Knowledge Engineering 29, no. 08 (2019): 1103–23. http://dx.doi.org/10.1142/s0218194019400114.

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Paintings convey the composition and characteristics of artists; therefore, it is possible to feel the intended style of painting and emotion of each artist through their paintings. In general, basic elements that constitute traditional paintings are color, texture, and composition (formative elements constituting the paintings are color and shape); however, color is the most crucial element expressing the emotion of a painting. In particular, traditional colors manifest the color containing historicity of the era, so the color shown in painting images is considered a representative color of t

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TIUAJ, Yanto, and Michael Hizkia Wicaksono. "A Preliminary Survey of Color Discrimination Among Indonesia Female Subjects Using Farnsworth-Munsell Hue Color Test." Jurnal Elektro 15, no. 2 (2024): 78–82. http://dx.doi.org/10.25170/jurnalelektro.v16i2.5138.

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This study aimed to conduct a preliminary survey of the color discrimination test among Indonesian samples. The study involved 26 participants of university students and office workers. To test the ability of color discrimination, the Farnsworth-Munsell (FM) 100 Hue Color Vision Test was used. Farnsworth-Munsell test scoring software was also used to obtain the total score of each participant. Using the FM 100-hue test, the participants were classified into superior, average and low color discrimination ability. The results showed that that 19.2% female participants were classified as superior

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Lezoray, Olivier, and Michel Lecluse. "AUTOMATIC SEGMENTATION AND CLASSIFICATION OF CELLS FROM BRONCHO ALVEOLAR LAVAGE." Image Analysis & Stereology 26, no. 3 (2011): 111. http://dx.doi.org/10.5566/ias.v26.p111-119.

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Broncho alveolar lavage is the most commonly used diagnostic tool for confirming alveolar hemorrhage. Golde has introduced a ranking score, based on the hemosiderin content of macrophages which enables ranking cells from 0 to 4 based on the degree of Prussian blue stain. We propose a complete image analysis scheme to automatically perform both the extraction of the cellular objects and the ranking of each cell according to the Golde score. The image analysis techniques used mainly involve clustering and mathematical morphology. A 2D histogram is clustered to extract the main cellular component

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López Camelo, Andrés F., and Perla A. Gómez. "Comparison of color indexes for tomato ripening." Horticultura Brasileira 22, no. 3 (2004): 534–37. http://dx.doi.org/10.1590/s0102-05362004000300006.

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Color in tomato is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer's purchase decision. Degree of ripening is usually estimated by color charts. Colorimeters, on the other hand, express colors in numerical terms along the L*, a* and b* axes (from white to black, green to red and blue to yellow, respectively) within the CIELAB color sphere which are usually mathematically combined to calculate the color indexes. Color indexes and their relationship to the visual color classification of tomato fruits vine ripened were comp

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Harel, B., P. Kurtser, Y. Parmet, and Y. Edan. "Sweet pepper maturity evaluation." Advances in Animal Biosciences 8, no. 2 (2017): 167–71. http://dx.doi.org/10.1017/s2040470017001236.

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This paper focuses on maturity evaluation derived by a color camera for a sweet pepper robotic harvester. Different color and morphological features for sweet pepper maturity were evaluated. Side view and bottom view of sweet paper were analyzed and compared for their ability to classify into 4 maturity classes. The goal of this study was to differentiate between the two center classes which are difficult to separate. Statistical analysis of 13 different features in reliance to the maturity classification and the views indicated the best features for classification. The results show that the f

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Aravena, Ricardo A., Mitchell B. Lyons, Adam Roff, and David A. Keith. "A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia." Remote Sensing 13, no. 13 (2021): 2544. http://dx.doi.org/10.3390/rs13132544.

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To facilitate the simplification, visualisation and communicability of satellite imagery classifications, this study applied visual analytics to validate a colourimetric approach via the direct and scalable measurement of hue angle from enhanced false colour band ratio RGB composites. A holistic visual analysis of the landscape was formalised by creating and applying an ontological image interpretation key from an ecological-colourimetric deduction for rainforests within the variegated landscapes of south-eastern Australia. A workflow based on simple one-class, one-index density slicing was de

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Wandi, Dede, Fauziah Fauziah, and Nur Hayati. "Deteksi Kelayuan Pada Bunga Mawar dengan Metode Transformasi Ruang Warna Hue Saturation Intensity (HSI) dan Hue Saturation Value (HSV)." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (2021): 308. http://dx.doi.org/10.30865/mib.v5i1.2562.

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The rose is a plant of the genus Rosa. The rose consists of more than 100 species with various colors. In selecting and sorting roses, roses are often found that are still fresh and wilted. Based on the problems faced in roses, a system design is carried out that can detect the wilting condition of roses. By applying the HSI and HSV methods to image processing applications, it is hoped that it can help in choosing the condition of roses. With research methods through observation and literature study. To see the conditions, roses can be divided into wilted flowers and fresh flowers. In its impl

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Areni, Intan Sari, Indrabayu Amirullah, and Nurhikma Arifin. "Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV." Jurnal Penelitian Enjiniring 23, no. 2 (2019): 113–16. http://dx.doi.org/10.25042/jpe.112019.03.

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Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image.

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Solís, Martín, Erick Muñoz-Alvarado, and María Carmen Pegalajar. "The Transformation of RGB Images to Munsell Soil-Color Charts." Uniciencia 36, no. 1 (2022): 1–10. http://dx.doi.org/10.15359/ru.36-1.36.

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[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% f

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Garcia-Lamont, Farid, Matias Alvarado, and Jair Cervantes. "Systematic segmentation method based on PCA of image hue features for white blood cell counting." PLOS ONE 16, no. 12 (2021): e0261857. http://dx.doi.org/10.1371/journal.pone.0261857.

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Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukoc

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Gao Qiang, 高强, 马瑞青 Ma Ruiqing та 强彦 Qiang Yan. "色相测验和色盲检查镜对异常色觉的检测和分类". Laser & Optoelectronics Progress 60, № 9 (2023): 0933002. http://dx.doi.org/10.3788/lop220936.

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Zhang, Xuanhan. "Research on Colour Matching in Art Design Based on Neural Network Mathematics Models." Mathematical Problems in Engineering 2022 (March 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/3873213.

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Colour, an art term, is an important formal element that can influence our changing feelings, and colour matching has a very important place in art. Colour is an important artistic language in the study of art, and colour is also a more attractive representation of our real world. In this paper, we fine-tune an existing mathematics model to analyze the effect of hue, luminance, saturation, and contrast on the emotion classification of art paintings and achieve an accuracy improvement of 3.4% over the current state of the art on the public dataset Twitter image dataset. Finally, we propose a pr

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Huan, Vu Phan, Le Kim Hung, and Nguyen Hoang Viet. "Fault Classification and Location on 220kV Transmission line Hoa Khanh – Hue Using Anfis Net." Journal of Automation and Control Engineering 3, no. 2 (2015): 98–104. http://dx.doi.org/10.12720/joace.3.2.98-104.

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Napitu, Stifani, Rini Paramita Panjaitan, Putri Aisyah Nulhakim, and Muaz Khalik Lubis. "Klasifikasi Buah Jeruk Segar dan Busuk Berdasarkan RGB dan HSV Menggunakan Metode KNN." Jurnal SAINTEKOM 13, no. 2 (2023): 214–21. http://dx.doi.org/10.33020/saintekom.v13i2.420.

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Fruits are a group of agricultural commodities in Indonesia. The demand for domestic fruit commodities is quite high, this is indicated by the large number of fruits available in modern markets and traditional markets. In this research, a classification process will be carried out between fresh oranges and rotten oranges based on RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color extraction. This study uses the K-Nearest Neighbor classification algorithm with a value of k = 1; 2; 3; 4; 5; 6; and 7. The dataset used consists of 146 training data and 88 testing data. The purpose and b

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Jian Su and Honglin Li. "Optimized Art Design Model With Statistical Model with Digital Media." International Journal of Maritime Engineering 1, no. 1 (2024): 371–82. http://dx.doi.org/10.5750/ijme.v1i1.1369.

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Art design is a form of creative expression that encompasses the visual aesthetics and conceptual elements of various mediums. Art design has undergone a transformative evolution with the integration of digital media, reshaping the landscape of creative expression. In contemporary art, artists leverage digital tools and technologies to explore innovative ways of crafting visual narratives. Hence, to improve the quality of the art design this paper constructed a framework of Weighted Genetic Optimization (WGO). The proposed WGO model incorporates the statistical modeling of digital media techno

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Masruroh, A. Isatul, Sorikhi ., and Achmad Syauqi. "KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA CALIFORNIA DALAM RUANG WARNA HSV (HUE SATURATION VALUE) DENGAN ALGORITMA K-NEAREST NEIGHBORS." Jurnal Informatika dan Riset 1, no. 1 (2023): 9–14. http://dx.doi.org/10.36308/iris.v1i1.470.

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Papaya fruit is in great demand by people at home and abroad, thus proving that this one agricultural product has become a global need that is in great demand and sought after. To determine the papaya harvest based on the color of the fruit skin, the ripeness of the papaya starts from unripe, unripe (half-ripe) and overripe so that the researchers put forward an idea to answer the problem in determining the ripeness of papaya fruit, which is mostly done manually, still has some weaknesses and requires the process is quite long, has low accuracy and is inconsistent. Based on these problems, a s

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Riwanto, Yudha, and Enda Putri Atika. "Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets." Internet of Things and Artificial Intelligence Journal 4, no. 3 (2024): 526–31. http://dx.doi.org/10.31763/iota.v4i3.767.

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This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to

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Nasution, Aulia Muhammad Taufiq, and Syakir Almas Amrullah. "Simple Vision System for Apple Varieties Classification." Industria: Jurnal Teknologi dan Manajemen Agroindustri 11, no. 1 (2022): 51–63. http://dx.doi.org/10.21776/ub.industria.2022.011.01.6.

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Every variety of apple has its particular physical characteristics, which are affected by different pre-harvest factors. Manual classification of these varieties by human labor has several weaknesses, such as the inconsistency, subjectivity, fatigue and different accuracy due to different level of experience of the inspector. This study was aimed to design and evaluate a simple computer-based vision system for recognizing and grading several varieties of apples based on their physical characteristics. Images of apples were taken and were used as training data with different algorithms to extra

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Ahmad, Hanaa M., and Shrooq R. Hameed. "Eye Diseases Classification Using Back Propagation Artificial Neural Network." Engineering and Technology Journal 39, no. 1B (2021): 11–20. http://dx.doi.org/10.30684/etj.v39i1b.1363.

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A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were f

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