Journal articles: 'Image-based positioning'

1

Zhang, Wei Guo, and Chuan You Zhang. "Fast Image Positioning Based on Mark." Advanced Materials Research 756-759 (September 2013): 4090–94. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4090.

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MARK tag is a common method in industrial image location. In allusion to the disadvantages of conventional MARK located, such as large numbers of templates training set,no scale and rotation ,harsh conditions and slow processing speed ,a improved matching strategy based on NCC is proposed in this paper. The method search and correct the sole MARK in the template, extract 25 grids normalized feature sequence. Then matching with all contour feature sequences extracted in the target image and finally locate the target MARK. The experiment results show that , the method has much robustness to scale and rotation and can meet the real-time processing requirements

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Klein, Kristina, Franziska Völckner, Hernán A. Bruno, Henrik Sattler, and Pascal Bruno. "Brand Positioning Based on Brand Image–Country Image Fit." Marketing Science 38, no. 3 (May 2019): 516–38. http://dx.doi.org/10.1287/mksc.2019.1151.

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Li, X., and J. Wang. "EVALUATING PHOTOGRAMMETRIC APPROACH OF IMAGE-BASED POSITIONING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences I-2 (July 13, 2012): 183–88. http://dx.doi.org/10.5194/isprsannals-i-2-183-2012.

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Liu, Xun, He Huang, and Bo Hu. "Indoor Visual Positioning Method Based on Image Features." Sensors and Materials 34, no. 1 (January 31, 2022): 337. http://dx.doi.org/10.18494/sam3562.

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Fioravanti, Duccio, Benedetto Allotta, and Andrea Rindi. "Image based visual servoing for robot positioning tasks." Meccanica 43, no. 3 (November 29, 2007): 291–305. http://dx.doi.org/10.1007/s11012-007-9095-1.

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Lin, Jian Hua, Gang Fu, and Xue Lu Yao. "An Accurate Positioning Method of Image Target Based on Sub-Pixel." Applied Mechanics and Materials 644-650 (September 2014): 1274–77. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1274.

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The conventional image of the mark is mainly targeting the video image within the target pixel level positioning, the positioning accuracy is poor, and is easily affected by noise. To this end, the introduction of a Canny edge detection and Zernike moments method of combining precise positioning mark targets for sub-pixel edge detection, and for the oval shape characteristics mark the target, using the least squares fitting to mark the target oval central location positioning . Experimental results show that the higher positioning accuracy of this method, anti-interference ability, with high robustness, and can meet the requirements of real-time video image processing.

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Zhang, Huiqing, and Yueqing Li. "LightGBM Indoor Positioning Method Based on Merged Wi-Fi and Image Fingerprints." Sensors 21, no. 11 (May 25, 2021): 3662. http://dx.doi.org/10.3390/s21113662.

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Smartphones are increasingly becoming an efficient platform for solving indoor positioning problems. Fingerprint-based positioning methods are popular because of the wide deployment of wireless local area networks in indoor environments and the lack of model propagation paths. However, Wi-Fi fingerprint information is singular, and its positioning accuracy is typically 2–10 m; thus, it struggles to meet the requirements of high-precision indoor positioning. Therefore, this paper proposes a positioning algorithm that combines Wi-Fi fingerprints and visual information to generate fingerprints. The algorithm involves two steps: merged-fingerprint generation and fingerprint positioning. In the merged-fingerprint generation stage, the kernel principal component analysis feature of the Wi-Fi fingerprint and the local binary pattern features of the scene image are fused. In the fingerprint positioning stage, a light gradient boosting machine (LightGBM) is trained with mutually exclusive feature bundling and histogram optimization to obtain an accurate positioning model. The method is tested in an actual environment. The experimental results show that the positioning accuracy of the LightGBM method is 90% within a range of 1.53 m. Compared with the single-fingerprint positioning method, the accuracy is improved by more than 20%, and the performance is improved by more than 15% compared with other methods. The average locating error is 0.78 m.

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Sun, Senzhen, Guangyun Li, Yangjun Gao, and Li Wang. "Robust Dynamic Indoor Visible Light Positioning Method Based on CMOS Image Sensor." Photogrammetric Engineering & Remote Sensing 88, no. 5 (May 1, 2022): 333–42. http://dx.doi.org/10.14358/pers.21-00077r3.

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A real-time imaging recognition and positioning method based on visible light communication flat light source is proposed. This method images the visible light communication flat light source through the rolling shutter effect of the complementary metal-oxide semiconductor imaging sensor and obtains the rectangular area outline of the light source. The light and dark stripe information of image with the digital image processing method realizes light source matching recognition by defining the concept, the autocorrelation sequence, which can be used to obtain the identity of the light source, and the rectangular vertex coordinate information of flat light source achieves high-precision vision positioning on the basis of inertial measurement unit attitude sensor-assisted imaging. Simultaneously, the corresponding positioning module is developed for positioning testing. The test results indicate that the plane positioning error is less than 4.5 cm, and the positioning frequency is greater than 10 Hz, which provides a high-precision visual positioning solution for indoor positioning.

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Bai, Nan, Guangzhu Chen, Rui Hou, and Feng Ying. "A novel WiFi signal and RGB image fusion positioning method for manufacturing workshop." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3229–40. http://dx.doi.org/10.3233/jifs-191647.

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With the sustainable development of mobile communication technology and the increasing demand for indoor services, Location-based Service (LBS) is attracting more and more attention. Determining the mobile target’s location is a core problem of LBS. The traditional WiFi signal fingerprint-based positioning technology mainly determines the location information of the mobile target by received RSS, which has high real-time positioning but low positioning accuracy. The fingerprint-based positioning technology using image mainly determines the location information of the mobile target by matching the features of the foreground images, which has the high positioning accuracy but low real-time positioning. This paper presents an indoor positioning method fusing information of the WiFi signal and RGB image to improve the positioning performance. The WiFi signal is transformed into the W-image according to indoor space and correction radius parameters, then the W-image and RGB image information are fused with LBP feature by the uniform-LBP algorithm. A fusion positioning model based on the sparse representation is established and solved using Lasso and BPDN positioning method. The positioning methods are tested in manufacturing workshop, and the experimental results show that the proposed method can reduce the complexity of the positioning method and achieve the higher positioning accuracy under same conditions.

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Qiu, Yijin, Xingjie Chen, and Zhaomin Lv. "Rail Fastener Positioning Based on Double Template Matching." Complexity 2020 (October 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/8316969.

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For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

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Ma, Lin, He Dong, and Bin Wang. "Mutual Positioning Method in Unknown Indoor Environment Based on Visual Image Semantics." Wireless Communications and Mobile Computing 2021 (March 6, 2021): 1–15. http://dx.doi.org/10.1155/2021/5527148.

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In our society, realizing intelligent positioning in indoor environments is important to build a smart city. Currently, mutual positioning requirements in the unknown indoor environment are growing fast. However, in such environment, we can obtain neither outdoor radio signal nor the indoor images in advance for online positioning. Therefore, how to achieve mutual positioning becomes an interesting problem. In this paper, we propose a vision-based mutual positioning method in an unknown indoor environment. First, two users take images of the unknown indoor environment, use semantic segmentation network to identify the semantic targets contained in the images, and upload the generated semantic sequence to the user shared database in real time. Then, every time two users reupload a semantic sequence due to a change of location, it is necessary to retrieve whether another user has uploaded the same semantic sequence in the shared database. If the retrieval is successful, it means that two users have seen the same scene. Finally, two users select a target from the two user images taken based on the same scene to establish a three-dimensional coordinate system, respectively, calculate their own position coordinates in this coordinate system, and realize mutual positioning through position coordinate sharing. Experiment results show that our proposed method can successfully realize mutual positioning between two users in an unknown indoor environment, while ensuring high positioning accuracy.

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Guan, Weipeng, Xinjie Zhang, Yuxiang Wu, Zekun Xie, Jingyi Li, and Jieheng Zheng. "High Precision Indoor Visible Light Positioning Algorithm Based on Double LEDs Using CMOS Image Sensor." Applied Sciences 9, no. 6 (March 25, 2019): 1238. http://dx.doi.org/10.3390/app9061238.

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Visible Light Positioning (VLP) is widely recognized as a cost-effective solution for indoor positioning with increasing demand. However, the nonlinearity and highly complex relationship between three-dimensional world coordinate and two-dimensional image coordinate hinders the good performance of image-sensor-based VLP. Therefore, there is a need to develop effective VLP algorithms to locate the positioning terminal using image sensor. Besides, due to the high computational cost of image processing, most existing VLP systems do not achieve satisfactory performance in terms of real-time ability and positioning accuracy, both of which are significant for the performance of indoor positioning system. In addition, the accurate identification of the ID information of each LED (LED-ID) is important for positioning, because if the LED-ID is not recognized well, the positioning can only be achieved in a particular positioning unit and cannot be applied to a large scene with many LEDs. Therefore, an effective image-sensor-based double-light positioning system is proposed in this paper to solve the above problems. We also set up relevant experiments to test the performance of the proposed system, which utilizes the rolling shutter mechanism of the Complementary Metal Oxide Semiconductor (CMOS) image sensor. Machine learning was used to identify the LED-ID for better results. Simulation results show that the proposed double-light positioning system could deliver satisfactory performance in terms of both the real-time ability and the accuracy of positioning. Moreover, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which has never been considered before. Experiments confirmed that the proposed double-light positioning system can provide an accuracy of 3.85 cm with an average computing time of 56.28 ms, making it a promising candidate for future indoor positioning applications.

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Zhao, Quan Ke, Xiao Lin Zhang, Yu Zhang, and Jian Ping Peng. "Image Based Positioning of the Freightcar’s Running Gear Springs." Applied Mechanics and Materials 130-134 (October 2011): 3826–29. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3826.

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Freightcar’s running gear springs are easily ruptured in the processes of manufacturing and using, losing the role of relaxing vehicle vibration, and resulting in accident. It is time to study the automatic recognition of springs defects based on image processing technology. Defects recognition is on the premise of accurate spring image location. An image location algorithm for the freightcar’s running gear springs is proposed in this paper, which is based on the structure characteristics of springs themselves. The algorithm mainly includes graying the image, correcting non-uniform illumination by wavelet homomographic filtering, taking binaryzation on image based on the iterative threshold method and locating springs’ boundary location, which could be obtained by image scanning column by column and feature analysis. This algorithm can achieve the automatic positioning of springs and has a good result to locate springs in the running gear images in case of different illumination; the accuracy rate has reached 96 percent. The study of this paper lays a foundation on the realization of automatic recognition of springs defects and has good application prospects.

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Liu, Duanfeng, and Gaili Gao. "Research on precise positioning based on image recognition technology." Highlights in Science, Engineering and Technology 1 (June 14, 2022): 232–37. http://dx.doi.org/10.54097/hset.v1i.467.

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Aiming at the problem that it is difficult to determine the picking location for the picking robot, precise positioning of picking grapes based on Mask-RCNN network and Cannylines detector is proposed in this paper. Firstly, the grape images under shading, direct sunlight and backlight conditions are collected, and grape images are marked with masks to make grape data sets. Then, based on the Mask-RCNN network training data set, the trained model is used to identify the grapes, and the box of framed grape cluster and the mask approximately equal to the size of grape cluster are obtained. Then, the AOI of main stem and centroid of grape cluster are calculated according to the obtained box and mask, the obtained AOI is equalized. Finally, the Cannylines detector based on Bilateral filtering is used to detect the line segment of main stem in the AOI, and the picking location is obtained according to the minimum distance between the centroid and the line segment. 150 images from the test set were used for the experiment, with 50 images selected from each type of light, including shading, direct sunlight and backlight. Experiments show that the accuracies of picking positioning under the three types of light conditions, are 0.900, 0.920, and 0.860, respectively. This method can provide precise picking location information for the grape picking robot.

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Komorita, Satoshi, Suwichaya Suwanwimolkul, and Jianfeng Xu. "Image-based Positioning Technology: The Research, Development and Demonstration." Journal of The Institute of Image Information and Television Engineers 76, no. 1 (2022): 129–34. http://dx.doi.org/10.3169/itej.76.129.

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Shao, Wenhua, Haiyong Luo, Fang Zhao, Yan Ma, Zhongliang Zhao, and Antonino Crivello. "Indoor Positioning Based on Fingerprint-Image and Deep Learning." IEEE Access 6 (2018): 74699–712. http://dx.doi.org/10.1109/access.2018.2884193.

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Cai, Aiping, and Taotao Wang. "A video image detection approach based on cooperative positioning." International Journal of High Performance Systems Architecture 9, no. 2/3 (2020): 70. http://dx.doi.org/10.1504/ijhpsa.2020.10033736.

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Cai, Aiping, and Taotao Wang. "A video image detection approach based on cooperative positioning." International Journal of High Performance Systems Architecture 9, no. 2/3 (2020): 70. http://dx.doi.org/10.1504/ijhpsa.2020.111555.

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SAMANT, S., A. PARRA, and J. WU. "Novel feature based image registration for automated patient positioning." International Journal of Radiation OncologyBiologyPhysics 60 (September 2004): S578. http://dx.doi.org/10.1016/s0360-3016(04)01873-5.

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Samant, S., A. Parra, and J. Wu. "Novel feature based image registration for automated patient positioning." International Journal of Radiation Oncology*Biology*Physics 60, no. 1 (September 2004): S578. http://dx.doi.org/10.1016/j.ijrobp.2004.07.569.

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Li, Shuang, Baoguo Yu, Yi Jin, Lu Huang, Heng Zhang, and Xiaohu Liang. "Image-Based Indoor Localization Using Smartphone Camera." Wireless Communications and Mobile Computing 2021 (July 5, 2021): 1–9. http://dx.doi.org/10.1155/2021/3279059.

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With the increasing demand for location-based services such as railway stations, airports, and shopping malls, indoor positioning technology has become one of the most attractive research areas. Due to the effects of multipath propagation, wireless-based indoor localization methods such as WiFi, bluetooth, and pseudolite have difficulty achieving high precision position. In this work, we present an image-based localization approach which can get the position just by taking a picture of the surrounding environment. This paper proposes a novel approach which classifies different scenes based on deep belief networks and solves the camera position with several spatial reference points extracted from depth images by the perspective- n -point algorithm. To evaluate the performance, experiments are conducted on public data and real scenes; the result demonstrates that our approach can achieve submeter positioning accuracy. Compared with other methods, image-based indoor localization methods do not require infrastructure and have a wide range of applications that include self-driving, robot navigation, and augmented reality.

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Zhang, F., W. Ke, H. Ouyang, and S. Qiu. "INDOOR VISIBLE LIGHT LOCALIZATION METHOD BASED ON EMBEDDED ARTIFICIAL INTELLIGENCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 255–61. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-255-2022.

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Abstract. This paper proposes an indoor visible light location method based on embedded platform and optical frequency image recognition technology with artificial intelligence, which can effectively improve the location effect in complex indoor environment. By transplanting the artificial intelligence (AI) based image classification algorithm into the embedded platform, this method uses a forward neural network to analyse the position information coming from the coded optical frequency image received by a camera, and then the positioning results can be obtained. In view of the "motion state" and "occlusion state" that are most likely to fail in traditional visible image localization, we specially supplement the training set of relevant characteristic optical frequency images to enhance the robustness of the method. According to the track and positioning results of the moving platform receiver, the proposed method can provide accurate and reliable navigation and positioning and has stronger anti-interference ability compared with the traditional light intensity or light image positioning methods.

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Li, Wen Jie, Ke Lun Tian, and Jie Zhang. "The License Plate Image Processing Based on MATLAB." Advanced Materials Research 756-759 (September 2013): 3511–16. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3511.

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This paper realizes the license plate image preprocessing, segmentation and positioning using MATLAB software. By image processing, it indicates the convenient, simple, and effectiveness of MATLAB recognition, achieving the desired effect.

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Wang, Runzhi, Wenhui Wan, Kaichang Di, Ruilin Chen, and Xiaoxue Feng. "A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction." Remote Sensing 11, no. 21 (November 1, 2019): 2572. http://dx.doi.org/10.3390/rs11212572.

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High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.

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Yang, Lei, and Weimin Lei. "Computer Vision Positioning and Local Obstacle Avoidance Optimization Based on Neural Network Algorithm." Computational Intelligence and Neuroscience 2022 (April 1, 2022): 1–11. http://dx.doi.org/10.1155/2022/3061910.

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Due to the rapid development of social computerization and smart devices, there is an increasing demand for indoor positioning of mobile robots in the robotics field, so it is very important to realize the autonomous navigation of mobile robots. However, in indoor scenes, due to factors such as dark walls, the global positioning system cannot effectively locate, and the broadband and wired positioning technologies used indoors have problems such as base station laying and delay. Computer vision positioning technology has greatly improved the camera hardware due to its simple equipment and low cost. Compared with other sensor cameras, it is less affected by environmental changes, so visual positioning has received extensive attention. Image matching has become the most critical link in visual positioning. The accuracy, speed, and robustness of image matching directly determine the results of visual positioning, so image matching has become the main topic of this study. In this study, the neural network algorithm is systematically optimized, especially for the robot’s local obstacle avoidance, and an obstacle data acquisition method based on VGG16 and fast RCNN is proposed. In order to solve the problem that the semantic image segmentation algorithm based on AlexNet and ResNet is difficult to accurately obtain the information of multiple objects, and an image semantic segmentation algorithm combined with VGG16 is designed to classify the background and road in the image at the pixel level and capture the path boundary line. The collection of robot obstacle path information improves the speed and accuracy of highly automated local obstacle avoidance. This study uses neural network algorithms to systematically optimize computer vision positioning and also studies the accuracy optimization of local obstacle avoidance, aiming to promote its better development.

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Sirigool, W., and R. Kesvarakul. "Particle Filter for Hector SLAM to Improve the Performance of Robot Positioning by Image Processing Based." International Journal of Machine Learning and Computing 10, no. 3 (May 2020): 490–94. http://dx.doi.org/10.18178/ijmlc.2020.10.3.962.

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Han, Guang Lin, and Li Ping Xia. "Sub-Pixel Image Features Location Technology." Applied Mechanics and Materials 303-306 (February 2013): 1009–12. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1009.

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Based on image features high precision positioning measurement research. Design of a Harris corner sub-pixel location algorithm, and makes the simulation experiment. Algorithm in image noise can be realized under the condition of larger than 0.1 pixels high precision positioning.

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Xiao, Feng, and Li Na Guo. "Sub-Pixel Edge Detection of Color Image Based on Dimensionality Reduction Technique and Curve Fitting." Applied Mechanics and Materials 433-435 (October 2013): 281–87. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.281.

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In order to solve the problem of the color characteristic information is often ignored in color image edge detection process, we propose a new color image sub-pixel edge detection method, which uses Ostu algorithm to get coarse positioning edges and extracts sub-pixel edge on the combination of curve fitting and coarse positioning edge in the projected image obtained by dimensionality reduction technique.Experimental results show that the algorithm positioning accuracy can reach 0.14 pixels,it is able to extract color image edge information effectively.

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Huang, Hui Fen. "A New Fragile Watermarking Based on Chaos." Applied Mechanics and Materials 401-403 (September 2013): 1772–75. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1772.

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For existing airspace fragile authentication watermarking algorithm positioning accuracy and security issues, this paper presents a fully-fragile watermarking based on chaos for the integrity of the image content authentication and tamper localization. The algorithm uses the original image block, the calculation of each pixel in the image block high-bit gray value as the image feature watermark information. The chaotic sequence is encrypted and determines the position of the watermark bit is embedded watermark information, tampering with the positioning accuracy of an image block of 2 × 2 pixels. Experimental results show that the algorithm is simple, safe, with good practice.

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Cao, Xinyu, Zhaojiang Wan, Jianpeng Dai, Jiayi Jiang, Xingyu Chen, and Hao Long. "Research on Autonomous Picking Algorithm for Table Tennis Balls based on Machine Vision." Frontiers in Science and Engineering 2, no. 9 (September 30, 2022): 1–7. http://dx.doi.org/10.54691/fse.v2i9.2226.

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The identification, positioning and grabbing path planning of table tennis balls are the preconditions for the robot to pick up table tennis balls autonomously. A method of table tennis identification, positioning and grabbing path planning based on monocular vision image processing is proposed. By analyzing the characteristics of table tennis images, a table tennis image segmentation method based on color and contour features is designed. The color and contour features of table tennis images are extracted, and HSV is used for image preprocessing. Image segmentation is processed by binarization, and Hough circle is used to detect contour recognition, and the segmentation image of table tennis balls is obtained by color combination. Convert the camera imaging coordinates to obtain the pixel position of the table tennis ball in the image. A ball picking path planning based on ant colony algorithm is designed. This paper provides a theoretical reference for the identification, positioning and path planning of autonomous ball picking.

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Wan, Qilong, Haiming Zhao, Jie Li, and Peng Xu. "Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image." Sensors 21, no. 2 (January 9, 2021): 426. http://dx.doi.org/10.3390/s21020426.

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Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

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Wan, Qilong, Haiming Zhao, Jie Li, and Peng Xu. "Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image." Sensors 21, no. 2 (January 9, 2021): 426. http://dx.doi.org/10.3390/s21020426.

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Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

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Dymarchuk, R., G. Borshehova, and Ye D. Yarmolchuk. "Procedure for determining the accuracy of satellite image-based positioning." Kosmìčna nauka ì tehnologìâ 9, no. 1s (2003): 57–63. http://dx.doi.org/10.15407/knit2003.01s.057.

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Guan Yang, 管扬, 孙德栋 Sun Dedong, 殷树刚 Yin Shugang, 辛军 Xin Jun, 魏凌 Wei Ling, 张洪明 Zhang Hongming, and 宋健 Song Jian. "High Precision Visible Light Indoor Positioning Based on Image Communication." Chinese Journal of Lasers 43, no. 12 (2016): 1206001. http://dx.doi.org/10.3788/cjl201643.1206001.

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Stache, N. C., J. Dieckelmann, J. Gedicke, A. Olowinsky, and T. Aach. "Image-based creation and positioning of welds for accelerated prototyping." Journal of Laser Applications 22, no. 4 (November 2010): 137–43. http://dx.doi.org/10.2351/1.3519044.

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Ma, Hongjie, Edward Smart, Adeel Ahmed, and David Brown. "Radar Image-Based Positioning for USV Under GPS Denial Environment." IEEE Transactions on Intelligent Transportation Systems 19, no. 1 (January 2018): 72–80. http://dx.doi.org/10.1109/tits.2017.2690577.

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Zhang, Zhenyu, and Qiuling Zhao. "The Image-Based Positioning Technique Using Inter-Pixel Relation Network." IEEE Access 8 (2020): 86246–50. http://dx.doi.org/10.1109/access.2020.2992770.

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Milliken, Barrett D., Steven J. Rubin, Russell J. Hamilton, L. Scott Johnson, and George T. Y. Chen. "Performance of a video-image-subtraction-based patient positioning system." International Journal of Radiation Oncology*Biology*Physics 38, no. 4 (July 1997): 855–66. http://dx.doi.org/10.1016/s0360-3016(97)00081-3.

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39

Li, Danjie, Zhanhang Wei, Ganhong Yang, Yi Yang, Jingwen Li, Mingyang Yu, Puxi Lin, et al. "Deep Learning-Based Robust Visible Light Positioning for High-Speed Vehicles." Photonics 9, no. 9 (September 2, 2022): 632. http://dx.doi.org/10.3390/photonics9090632.

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Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles.

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Jiang, Wu Xue, Zhi Ming Wang, and Jian Feng Luo. "Research of Image Recognition and Tracking Algorithm Based on PID Fuzzy Control." Advanced Materials Research 722 (July 2013): 472–77. http://dx.doi.org/10.4028/www.scientific.net/amr.722.472.

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Video surveillance system with image recognition and image tracking capabilities has a wide range of applications in the field of city protection, social security and public safety. In this paper, based on the analysis of image detection and tracking principles, image recognition and tracking algorithm based PID fuzzy control is proposed. First, the image on the basis of the gray-scale image is acquired by the video processor and the linearization processing, and then through the expansion operation of mathematical morphology the candidate region of the image recognition is taken, and then the final image region is obtained through the vertical positioning and horizontal positioning of the region Finally, through the PID control algorithm it is the image of the track. Simulation results show that the PID fuzzy control-based image recognition and tracking algorithm possesses good vehicle license plate image recognition and tracking performance and good practicability.

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Li, Ming, Ruizhi Chen, Xuan Liao, Bingxuan Guo, Weilong Zhang, and Ge Guo. "A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras." Remote Sensing 12, no. 5 (March 8, 2020): 869. http://dx.doi.org/10.3390/rs12050869.

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Indoor visual positioning is a key technology in a variety of indoor location services and applications. The particular spatial structures and environments of indoor spaces is a challenging scene for visual positioning. To address the existing problems of low positioning accuracy and low robustness, this paper proposes a precision single-image-based indoor visual positioning method for a smartphone. The proposed method includes three procedures: First, color sequence images of the indoor environment are collected in an experimental room, from which an indoor precise-positioning-feature database is produced, using a classic speed-up robust features (SURF) point matching strategy and the multi-image spatial forward intersection. Then, the relationships between the smartphone positioning image SURF feature points and object 3D points are obtained by an efficient similarity feature description retrieval method, in which a more reliable and correct matching point pair set is obtained, using a novel matching error elimination technology based on Hough transform voting. Finally, efficient perspective-n-point (EPnP) and bundle adjustment (BA) methods are used to calculate the intrinsic and extrinsic parameters of the positioning image, and the location of the smartphone is obtained as a result. Compared with the ground truth, results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm. In addition, experiments show that the proposed method is more robust and efficient than the baseline method in a real scene. In the case where sufficient indoor textures are present, it has the potential to become a low-cost, precise, and highly available indoor positioning technology.

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Darwis, Dedi, Akmal Junaidi, Dewi Asiah Shofiana, and Wamiliana. "A New Digital Image Steganography Based on Center Embedded Pixel Positioning." Cybernetics and Information Technologies 21, no. 2 (June 1, 2021): 89–104. http://dx.doi.org/10.2478/cait-2021-0021.

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Abstract In this study we propose a new approach to tackle the cropping problem in steganography which is called Center Embedded Pixel Positioning (CEPP) which is based on Least Significant Bit (LSB) Matching by setting the secret image in the center of the cover image. The evaluation of the experiment indicated that the secret image can be retrieved by a maximum of total 40% sequential cropping on the left, right, up, and bottom of the cover image. The secret image also can be retrieved if the total asymmetric cropping area is 25% that covered two sides (either left-right, left-up or right-up). In addition, the secret image can also be retrieved if the total asymmetric cropping area is 70% if the bottom part is included. If asymmetric cropping area included three sides, then the algorithm fails to retrieve the secret image. For cropping in the botom the secret image can be extracted up to 70%.

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Wang, Xinyue, and Haibao Wang. "The QR code intelligent positioning system of the LBS cloud platform in the Internet of things environment." Journal of Autonomous Intelligence 3, no. 2 (September 13, 2021): 31. http://dx.doi.org/10.32629/jai.v3i2.338.

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Aiming at the problems of long positioning time and poor positioning accuracy in traditional positioning systems, a WeChat applet QR code area positioning system based on the LBS cloud platform is proposed and designed. The overall architecture of the system is divided into three parts: LBS cloud service, central data processing, and QR code positioning terminal for small programs. The hardware is designed from the server-side module, processor and positioning module to provide a basis for system construction. In the software design, the WeChat applet QR code area image is collected, the image edge features are enhanced and filtered, the positioning target is determined according to the processed image edge features, and the WeChat applet QR code area positioning system design is completed. The experimental results show that the positioning time of the system is equivalent to 50% of the traditional system, and the positioning accuracy is always maintained above 99.5%, which has significant advantages.

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Nan, Chun Yan, and Xiao Hui Yang. "Spline Interpolation Sub Pixel Edge Detection Method Based on Improved Morphological Gradient." Advanced Materials Research 971-973 (June 2014): 1756–59. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1756.

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In order to improve the accuracy of image edge detection.A spline interpolation sub pixel edge detection method based on improved morphological gradient is proposed in the thesis.Firstly,using improved morphological gradient filter operator for image coarse positioning;Then,the cubic spline interpolation method is carried out for pixel-level edge of the image interpolation so that the image edge locates in sub-pixel level.Have a simulation experiment to improved methods by Matlab, results show that the improved method can accurately detect the edge of the image, edge detection is fine, the precision of positioning is hight and the result of detection is good.

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Dong, Y., R. Lei, D. Fan, L. Gu, and S. Ji. "A NOVEL RPC BIAS MODEL FOR IMPROVING THE POSITIONING ACCURACY OF SATELLITE IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 35–41. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-35-2020.

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Abstract. High-precision satellite image geolocation is the basis for advanced processing of satellite image data. Aiming at the optimization of the satellite image positioning accuracy based on rational polynomial coefficients (RPC), we propose an RPC image-space bias model that combines object-space information. Based on a comprehensive analysis of the full-link error of the satellite image geometric imaging process, the real object coordinates are introduced into the RPC correction to make the bias model better fit the actual error. Experiments were performed using several image datasets from the Chinese satellite TianHui-1 (TH-1) and compared with the traditional RPC bias model. The results show that our model has strong robustness and can better correct image positioning errors. Compared with traditional bias models, it can improve the accuracy of plane positioning by approximately 1 pixel.

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Wu, Xiao. "LED Chips Locating Algorithm Based on Wavelet Transformation." Advanced Materials Research 816-817 (September 2013): 1105–10. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.1105.

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LED chips position is of crucial significance in chip testing, scribing, die spreading, and die bonding. It’s a great solution to indicate electrical characteristics of chips, examine whether the chip pins are up to the standard, and distinguish LED chip quality. Concerning this, an LED chip positioning method based on wavelet transform is proposed in this paper. Firstly, CCD, light and motion control module are adopted to construct and acquire the visual system of LED chip images. Then the images are processed with lowpass filtering and normalization to obtain Hi-Q chips image, and image features are extracted by further using multi-scale wavelet transform. Lastly, high accuracy positioning of LED chips is achieved by employing high-accuracy point pattern matching algorithm. Experimental results show that the LED chip image positioning error is less than 1μm and the position speed is faster than 5 particles per second, which offer new approaches for high-accuracy chip positioning system of detection machines, sorting machines, die bonders, etc.

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Li, Liang, Zhaomin Lv, Xingjie Chen, Yijin Qiu, Liming Li, Anqi Ma, Shubin Zheng, and Xiaodong Chai. "Research on track fastener positioning method based on local unidirectional template matching." Science Progress 104, no. 2 (April 2021): 003685042110261. http://dx.doi.org/10.1177/00368504211026131.

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Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

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Li, Qingyang, Ruofei Zhong, Cankun Yang, Ke Zhao, Chenchen Zhang, and Yue Li. "Geometric Quality Improvement Method of Optical Remote Sensing Satellite Images Based on Rational Function Model." Remote Sensing 14, no. 18 (September 6, 2022): 4443. http://dx.doi.org/10.3390/rs14184443.

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The high-precision geometric positioning of optical remote sensing satellites is the prerequisite to determine the application capability of satellite image products. Its positioning accuracy is related to the observation accuracy of each link in the imaging process, including satellite attitude, orbit measurement accuracy, time synchronization accuracy, camera measurement accuracy, and so on. Untimely and inaccurate on-orbit calibration will lead to great geometric positioning errors. To optimize the positioning accuracy of satellite images with the rational function model (RFM) under low positioning accuracy, our paper proposes an improved geometric quality model based on the reorientation of internal and external orientation elements in the RFM model of remote sensing images. By establishing the rational function positioning model, the external orientation model, and the internal orientation model, the original image can be reorientated. Then, we use the improved model to generate uniformly distributed virtual ground control points. By analyzing and verifying the relationship between each rational polynomial coefficient (RPC) and its influence on geometric positioning accuracy, we propose an RPC coefficients optimization method based on image offset correction and positioning dominant coefficients. Finally, we use the small satellite “MN200Sar-1” with low geometric accuracy for experimental verification. The results show that the model can effectively eliminate the errors of internal and external elements in the on-orbit calibration, and the positioning accuracy is improved from one hundred pixels to one pixel. At the same time, the rational polynomial dominant coefficient optimization method can improve geometric positioning accuracy without introducing additional compensation parameters.

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Bian, Jin Hong, Ji Lin Wang, Sen Xu, and Tian Zhou. "A Cable Sheath Material Thickness Measurement Method Based on Image Measurement Technology." Applied Mechanics and Materials 182-183 (June 2012): 477–81. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.477.

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Sub-pixel positioning is a key technology in geometry measurement field. Reckoning on the low accuracy of ordinary polynomial interpolation for sub-pixel positioning method because of losing the position of edge points, an improved Sobel edge detection method is first proposed, and then is applied to the thickness measurement system of cable sheath material. The experiment shoes that the edge coordinateonthe detected by our proposed interpolation method is more colse to the real edge than the original method.

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Tan, Bin. "Soccer-Assisted Training Robot Based on Image Recognition Omnidirectional Movement." Wireless Communications and Mobile Computing 2021 (August 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/5532210.

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With the continuous emergence and innovation of computer technology, mobile robots are a relatively hot topic in the field of artificial intelligence. It is an important research area of more and more scholars. The core of mobile robots is to be able to realize real-time perception of the surrounding environment and self-positioning and to conduct self-navigation through this information. It is the key to the robot’s autonomous movement and has strategic research significance. Among them, the goal recognition ability of the soccer robot vision system is the basis of robot path planning, motion control, and collaborative task completion. The main recognition task in the vision system is the omnidirectional vision system. Therefore, how to improve the accuracy of target recognition and the light adaptive ability of the robot omnidirectional vision system is the key issue of this paper. Completed the system construction and program debugging of the omnidirectional mobile robot platform, and tested its omnidirectional mobile function, positioning and map construction capabilities in the corridor and indoor environment, global navigation function in the indoor environment, and local obstacle avoidance function. How to use the local visual information of the robot more perfectly to obtain more available information, so that the “eyes” of the robot can be greatly improved by relying on image recognition technology, so that the robot can obtain more accurate environmental information by itself has always been domestic and foreign one of the goals of the joint efforts of scholars. Research shows that the standard error of the experimental group’s shooting and dribbling test scores before and the experimental group’s shooting and dribbling test results after the standard error level is 0.004, which is less than 0.05, which proves the use of soccer-assisted robot-assisted training. On the one hand, we tested the positioning and navigation functions of the omnidirectional mobile robot, and on the other hand, we verified the feasibility of positioning and navigation algorithms and multisensor fusion algorithms.

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Journal articles on the topic 'Image-based positioning'

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