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Journal articles on the topic 'Automatic tracking. Image processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Qu, Ming, Frank Shih, Ju Jing, and Haimin Wang. "Automatic Solar Flare Tracking Using Image-Processing Techniques." Solar Physics 222, no. 1 (July 2004): 137–49. http://dx.doi.org/10.1023/b:sola.0000036879.72274.68.

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2

Khalighi, Bahram, and Yong H. Lee. "Particle tracking velocimetry: an automatic image processing algorithm." Applied Optics 28, no. 20 (October 15, 1989): 4328. http://dx.doi.org/10.1364/ao.28.004328.

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3

Tang, Li Fang, and Chuan Jin Wang. "Vision Control System of Pipe Welding Robot." Advanced Materials Research 756-759 (September 2013): 509–13. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.509.

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The author of this article designs a non-track automatic pipe welding robot, which mainly studies the image processing system of visual welding tracking. With the requirement of various interference noise and tracking accuracy in the welding process, this study adopts structure light CCD sensor checking system and image acquisition card processing images of computer software, in which sample filtering, edge checking, contour tracking, laser centerlines selection and checking of its characteristics. This processing method has the advantages of good effect and speedy processing that is able to meet the timely requirement of tracking system.
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Palampatla, Hrithik Roshan. "Automatic Number Plate Recognition Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 25, 2021): 2394–400. http://dx.doi.org/10.22214/ijraset.2021.36889.

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Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their registration number issued by government. ANPR is often used in the detection of stolen vehicles, traffic surveillance system. Our project presents a model in which the vehicle license plate image is obtained by the digital cameras and the image is processed to get the number plate information. A vehicle image is captured and processed using various methods. Vehicle number plate region is extracted using the deep neural networks. Optical character recognition is implemented using certain machine learning algorithms for the character recognition. The system is implemented using deep neural network model, machine learning algorithms and is simulated in python, and its performance is tested on real images. It is observed that the developed model successfully detects the license plate region and recognizes the individual characters. There are various recognition strategies that have been produced and number plate recognition systems are today used in different movement and security applications, such as access and border control, parking, or tracking of stolen vehicles.
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KITAI, Kayoko. "AUTOMATIC TRACKING SYSTEM OF PEDESTRIANS USING IMAGE PROCESSING METHOD." Journal of Architecture and Planning (Transactions of AIJ) 62, no. 493 (1997): 195–200. http://dx.doi.org/10.3130/aija.62.195_2.

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6

Fisenko, V. T., V. I. Mozheĭko, and T. Yu Fisenko. "Automatic tracking of objects in computerized image-processing systems." Journal of Optical Technology 74, no. 11 (November 1, 2007): 752. http://dx.doi.org/10.1364/jot.74.000752.

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Ogawa, Yoji. "Image Processing for Automatic Welding in Turbid Water." Journal of Robotics and Mechatronics 11, no. 2 (April 20, 1999): 129–34. http://dx.doi.org/10.20965/jrm.1999.p0129.

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Wet underwater welding is a key to improving offshore construction and maintenance cost performance. Automatic wet welding with local dry space provided by a water curtain provides high-quality mechanical properties on weld metal. The most important factor in maintaining high weld quality is precise nozzle positioning. Light cutting by slit laser beam shows potential for shape recognition in air. Waterproof containers for the laser emitter and video camera are used for underwater seam tracking of weld line in turbid water. Acrylic windows on the laser emitter and video camera shortened the total light path length in turbid water and improved image quality. Laser light was dimmed by scattering in turbid water but detected groove geometry, enabling good seam tracking. A trial to get a clear image used a fiberscope camera in the welding nozzle and detected root gap width precisely. Welding conditions such as molten pool geometry were also detectable.
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Rao, Yutai, and Fan Yang. "Research on Path Tracking Algorithm of Autopilot Vehicle Based on Image Processing." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (August 26, 2019): 2054013. http://dx.doi.org/10.1142/s0218001420540130.

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Smart cars are the result of the combination of the latest technological achievements in the fields of artificial intelligence, sensors, control science, computer, and network technology with the modern automobile industry. Intelligent cars usually have functions, such as automatic shifting, automatic driving, and automatic road condition recognition. The research of intelligent car technology involves many disciplines. This thesis focuses on the field of smart car visual navigation, focusing on image denoising, image information recognition, extraction, and pattern recognition control algorithms. The traditional trajectory tracking algorithm is mainly used in industrial computer or high-performance computer. The computational complexity leads to poor real-time control, and it is easily interfered by external complex terrain environment and internal disordered electromagnetic environment during vehicle driving. In general, on a regular basis, by the image analysis of the driver or the driver information, the image information is proposed using way trace processing technology, vehicle tracking control method and automatic driving rules. The simulation and experimental results show that the proposed control methods and rules used to carry out automatic driving vehicle are feasible. The algorithm reduces the complexity of the algorithm, improves the real-time and stability of the control and finally achieves a good trajectory tracking effect of the car on high-speed automatic driving.
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Xie, Zheng Yang, Tao Chen, Ning Liu, Shi Bing Liu, and Ji Ming Chen. "A Design of Algorithm for Excimer Laser Coaxial Observation Automatic Tracking Processing System." Advanced Materials Research 706-708 (June 2013): 589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.589.

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This paper proposed a design of algorithm of an excimer laser coaxial observation processing system which can automatically track preset line. With using of the Visual C + + image processing technology and the least square method, the XY axis in both directions of processing platform can obtain accurate processing displacement, and stability control platform for real-time tracking a straight line center. Processing system can acquire the coordinate of processing place where can get the distance from the centre of image in order to achieve real-time tracking.
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Qi, Jie, Bin Lyu, Abdulmohsen AlAli, Gabriel Machado, Ying Hu, and Kurt Marfurt. "Image processing of seismic attributes for automatic fault extraction." GEOPHYSICS 84, no. 1 (January 1, 2019): O25—O37. http://dx.doi.org/10.1190/geo2018-0369.1.

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Along with horizon picking, fault identification and interpretation is one of the key components for successful seismic data interpretation. Significant effort has been invested in accelerating seismic fault interpretation over the past three decades. Seismic amplitude data exhibiting good resolution and a high signal-to-noise ratio are key to identifying structural discontinuities using coherence or other edge-detection attributes, which in turn serve as inputs for automatic fault extraction using image processing or machine learning techniques. Because seismic data exhibit not only structural reflectors but also seismic noise, we have developed a fault attribute workflow that contains footprint suppression, structure-oriented filtering, attribute computation, “unconformity” suppression, and our new iterative energy-weighted directional Laplacian of a Gaussian (LoG) operator. In general, tracking faults that exhibit a finite offset through a suite of conformal reflectors is relatively easy. Instead, we evaluate the effectiveness of this workflow by tracking faults through an incoherent mass-transport deposit, where the low-frequency contribution of multispectral coherence provides a good fault image. Multispectral coherence also reduces the “stair-step” fault artifacts seen on broadband data. Application of statistical filtering can preserve the discontinuity’s boundaries and reject incoherent backgrounds. Finally, iterative application of an energy-weighted directional LoG operator provides improved fault image by sharpening low-coherence anomalies perpendicular and smoothing low-coherence anomalies parallel to fault surfaces, while at the same time attenuating locally nonplanar anomalies.
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Lee, Jong Pyo, Qian Qian Wu, Min Ho Park, Cheol Kyun Park, and Ill Soo Kim. "A Study on Modified Hough Algorithm for Image Processing in Weld Seam Tracking System." Advanced Materials Research 1088 (February 2015): 824–28. http://dx.doi.org/10.4028/www.scientific.net/amr.1088.824.

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In modern market, achieving mechanical and automatic arc welding process is the key issue to be solved in welding industries. Because of the high complexity of the welding environment, manual detection of the weld line information is hard to be successful and time consuming. Therefore, this study aim at developing a new image processing algorithm for seam tracking system in Gas Metal Arc (GMA) welding by modified Hough algorithm based on the laser vision system. Firstly, noises in the captured weld seam images by CCD camera were effectively removed by noise filtering algorithm and then weld joint position were detected by the modified Hough algorithm to realize the automatic weld seam tracking. To verify the efficiency of the developed image processing model, a common image processing method was employed and the processed results were compared with the proposed algorithm. Statistical results proved that the modified Hough algorithm was able to acquire the weld information precisely with less computing time and memory cost, which also capable for industrial application.
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KOBAYASHI, Toshio, Telsuo SAGA, and Shigeki SEGAWA. "An automatic velocity measurement using image processing technique and its particle tracking." JOURNAL OF THE FLOW VISUALIZATION SOCIETY OF JAPAN 8, no. 30 (1988): 301–4. http://dx.doi.org/10.3154/jvs1981.8.301.

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KOBAYASHI, Toshio, Tetuso SAGA, and Shigeki SEGAWA. "An Automatic Velocity Measurement using Image Processing Technique and its Particle Tracking." JOURNAL OF THE FLOW VISUALIZATION SOCIETY OF JAPAN 8, Supplement (1988): 73–76. http://dx.doi.org/10.3154/jvs1981.8.supplement_73.

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14

Li, Zhao Yu, and Xiang Dong Gao. "Study on Regression Model of Measuring Weld Position." Applied Mechanics and Materials 511-512 (February 2014): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.514.

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Seam tracking technology is an important area of research automatic arc welding, precise seam tracking is crucial to achieve high quality welds. In order to achieve precise seam tracking, seam deviation ( the weld center arc deviation) detection is a key. Unlike the conventional method by image processing techniques to obtain the seam deviation information directly, but selected image processing area (including the distal end portion of the molten pool welds and the front end of the pool), and analyzed as a pool image centroid characteristic parameters of the weld deviation. Study these parameters to create a new method for visual weld deviation measurement model, establish the linear regression model between pool image centroid deviation and the weld based on regression analysis theory.
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Li, Zhao Yu, and Xiang Dong Gao. "Visual Technique for Detecting Weld Position Based on Neural Network Technology." Applied Mechanics and Materials 530-531 (February 2014): 209–12. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.209.

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Seam tracking technology is an important area of research automatic arc welding, precise seam tracking is crucial to achieve high quality welds. In order to achieve precise seam tracking, seam deviation (the weld center arc deviation) detection is a key. Unlike the conventional method by image processing techniques to obtain the seam deviation information directly, but selected image processing area (including the distal end portion of the molten pool welds and the front end of the pool), and analyzed as a pool image centroid characteristic parameters of the weld deviation. Study these parameters to create a new method for visual weld deviation measurement model, establish the weld center neural network model using artificial neural network modeling technology.
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16

Ay, Chyung, Chao Wang Young, M. S. Hung, and C. S. Hsu. "Automatic Tracking and Manipulation for Cell Lysis System Development." Applied Mechanics and Materials 195-196 (August 2012): 1147–52. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.1147.

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An automatic platform for cell localization using image processing and electroosmotic flow technology was developed and used the in vivo human leukemic cells (U937) experiments. The in vivo cells were located successfully in the assigned area by aluminum electrode with lithography process. The cells were lysised with electroporation then DNA was collected in this study. First, a CCD was used to take video from microscope and then a PCI image card acquired the image data to computer. The program was designed to find cell location and trace the in vivo cell and then the cell driving mechanism is started through the voltage or time of electrode controlled with fuzzy logic method by LabVIEW package software. In addition, the XY platform is automatically controlled to keep the cell within the field of view. When the in vivo cell enters the assigned lysis area, the cell will be electroporated by the electrode. The monitoring software was developed to track and manipulate single in vivo cell to lysis successfully in this study. It can control cell move to the assigned location fatly.
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17

Boyun, V. P., P. Yu Sabelnikov, and Yu A. Sabelnikov. "Video Processing Device for Automated Tracking of the Object Identified in Image by the Operator." Science and innovation 12, no. 2 (May 30, 2016): 25–34. http://dx.doi.org/10.15407/scine12.02.025.

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18

Boyun, V. P., P. Yu Sabelnikov, and Yu A. Sabelnikov. "Video Processing Device for Automatic Object Tracking Defined in the Image by Operator." Nauka ta innovacii 12, no. 2 (March 23, 2016): 29–39. http://dx.doi.org/10.15407/scin12.02.029.

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19

Hsu, Chih‐Yang, Yimei Li, Yuanyuan Han, Lucas Elijovich, Noah D. Sabin, Tarek Abuelem, Radmehr Torabi, et al. "Automatic image processing pipeline for tracking longitudinal vessel changes in magnetic resonance angiography." Journal of Magnetic Resonance Imaging 50, no. 4 (March 7, 2019): 1063–74. http://dx.doi.org/10.1002/jmri.26699.

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Song, Wei, Yan Xiao Chen, Long Ji Zhang, and Shu Yuan Qin. "Design of Automatic Tracking System Based on FPGA and Binocular Vision." Applied Mechanics and Materials 397-400 (September 2013): 1469–72. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1469.

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An intelligent system with automatic tracking function based on FPGA and binocular vision was designed. The system can be divided into five parts, and they are the image acquisition and processing module, moving target detection module, motion control module, moving target tracking module, and the depth information computing module. The basic design theory and the structure of each component were analyzed from software and hardware.
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21

H. Borse, Janhavi, Dipti D. Patil, and Vinod Kumar. "Tracking Keypoints from Consecutive Video Frames Using CNN Features for Space Applications." Tehnički glasnik 15, no. 1 (March 4, 2021): 11–17. http://dx.doi.org/10.31803/tg-20210204161210.

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Hard time constraints in space missions bring in the problem of fast video processing for numerous autonomous tasks. Video processing involves the separation of distinct image frames, fetching image descriptors, applying different machine learning algorithms for object detection, obstacle avoidance, and many more tasks involved in the automatic maneuvering of a spacecraft. These tasks require the most informative descriptions of an image within the time constraints. Tracking these informative points from consecutive image frames is needed in flow estimation applications. Classical algorithms like SIFT and SURF are the milestones in the feature description development. But computational complexity and high time requirements force the critical missions to avoid these techniques to get adopted in real-time processing. Hence a time conservative and less complex pre-trained Convolutional Neural Network (CNN) model is chosen in this paper as a feature descriptor. 7-layer CNN model is designed and implemented with pre-trained VGG model parameters and then these CNN features are used to match the points of interests from consecutive image frames of a lunar descent video. The performance of the system is evaluated based on visual and empirical keypoints matching. The scores of matches between two consecutive images from the video using CNN features are then compared with state-of-the-art algorithms like SIFT and SURF. The results show that CNN features are more reliable and robust in case of time-critical video processing tasks for keypoint tracking applications of space missions.
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KONWAR, LAKHYADEEP, ANJAN KUMAR TALUKDAR, and KANDARPA KUMAR SARMA. "Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (August 6, 2021): 93–98. http://dx.doi.org/10.37394/232014.2021.17.13.

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Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our system can easily detect and track humans in poor lightening conditions, color, size, shape, and clothing due to the use of HOG feature descriptor and particle filter. We use graph cut based segmentation technique, therefore our system can handle occlusion at about 88%. Due to the use of HOG to extract features our system can properly work in indoor as well as outdoor environments with 97.61% automatic human detection and 92% automatic human detection and tracking accuracy of multiple human
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Anai, T., T. Sasaki, H. Otani, K. Osaragi, and N. Kochi. "Aerial photogrammetry procedure optimized for micro uav." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 5, 2014): 41–46. http://dx.doi.org/10.5194/isprsarchives-xl-5-41-2014.

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This paper proposes the automatic aerial photogrammetry procedure optimized for Micro UAV that has ability of autonomous flight. The most important goal of our proposed method is the reducing the processing cost for fully automatic reconstruction of DSM from a large amount of image obtained from Micro UAV. For this goal, we have developed automatic corresponding point generation procedure using feature point tracking algorithm considering position and attitude information, which obtained from onboard GPS-IMU integrated on Micro UAV. In addition, we have developed the automatic exterior orientation and registration procedure from the automatic generated corresponding points on each image and position and attitude information from Micro UAV. Moreover, in order to reconstruct precise DSM, we have developed the area base matching process which considering edge information.<br><br> In this paper, we describe processing flow of our automatic aerial photogrammetry. Moreover, the accuracy assessment is also described. Furthermore, some application of automatic reconstruction of DSM will be desired.
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Ivanov, D. I. "METHODS OF IMAGE RECOGNITION IN A VIDEO STREAM." Applied Mathematics and Fundamental Informatics 8, no. 1 (2021): 042–49. http://dx.doi.org/10.25206/2311-4908-2021-8-1-42-49.

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The article examines the problem of automatic object recognition using a video stream as a digital image. Algorithms for recognizing and tracking objects in the video stream are considered, methods used in video processing are analyzed, and the use of machine learning tools in working with video is described.The main approaches to solving the problem of recognizing moving objects in a video stream are investigated: the detection-based approach and the tracking-based approach. Arguments are made in favor of the tracking-based approach, and, in addition, modern methods of tracking objects in the video stream are considered. In particular, the algorhythms: Online Boosting Tracker - one of the first object tracking algorithms with high tracking accuracy, MIL Tracker (Multiple Instance Learning Tracker), which is a development of the idea of learning with a teacher and the Online Boosting algorithm and the KCF Tracker algorithm (Kernelized Correlation Filters Tracker) - a method that uses the mathematical properties of overlapping areas of positive examples.As a result, the advantages and disadvantages of the considered methods and algorithms for recognizing and tracking objects for various applications are highlighted.
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Song, Wei, Shu Yuan Qin, Yan Xiao Chen, and Long Ji Zhang. "Design of Real-Time Image Data Acquisition and Processing System Based on SOPC." Applied Mechanics and Materials 373-375 (August 2013): 650–53. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.650.

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A real-time image data acquisition and processing system based on SOPC in intelligent cart with automatic tracking function was designed. In this paper, the basic design theory and the system structure was analyzed. The Quartus II and Nios II software was used to make the system with real-time image acquisition function. And the data in SDRAM of two CMOS cameras based on SOPC was read. The design was tested with the system, and results showed that this module had the characters of design flexibility, fast image processing and expansibility.
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Yin, Fan, Chao Li, Haibin Wang, and Fan Yang. "Automatic Acoustic Target Detecting and Tracking on the Azimuth Recording Diagram with Image Processing Methods." Sensors 19, no. 24 (December 6, 2019): 5391. http://dx.doi.org/10.3390/s19245391.

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Passive acoustic target detection has been a hot research topic for a few decades. Azimuth recording diagram is one of the most promising techniques to estimate the arrival direction of the interested signal by visualizing the sound wave information. However, this method is challenged by the random ambient noise, resulting in low reliability and short effective distance. This paper presents a real-time postprocessing framework for passive acoustic target detection modalities by using a sonar array, in which image processing methods are used to automate the target detecting and tracking on the azimuth recording diagram. The simulation results demonstrate that the proposed approach can provide a higher reliability compared with the conventional ones, and is suitable for the constraints of real-time tracking.
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27

INOUE, Makito, and Takashi YOSHIMI. "A study of Automatic Camera Tracking System for Construction Machines by Multiple Image Processing." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 1P2—C02. http://dx.doi.org/10.1299/jsmermd.2017.1p2-c02.

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28

Wang, Weixi, Satoshi Yamane, Tomoyuki Koike, Jiro Touma, Kazumichi Hosoya, Toru Nakajima, and Hikaru Yamamoto. "Image processing method for automatic tracking of the weld line in plasma robotic welding." International Journal of Advanced Manufacturing Technology 86, no. 5-8 (January 12, 2016): 1865–72. http://dx.doi.org/10.1007/s00170-015-8311-8.

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Zin, Thi Thi, Moe Zet Pwint, Pann Thinzar Seint, Shin Thant, Shuhei Misawa, Kosuke Sumi, and Kyohiro Yoshida. "Automatic Cow Location Tracking System Using Ear Tag Visual Analysis." Sensors 20, no. 12 (June 23, 2020): 3564. http://dx.doi.org/10.3390/s20123564.

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Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track specific data for individual cows such as body condition score, genetic abnormalities, etc. Specifically, a four-digit identification number is used, so that a farm can accommodate up to 9999 cows. In our proposed system, we develop an individual cow tracker to provide effective management with real-time upgrading enforcement. For this purpose, head detection is first carried out to determine the cow’s position in its related camera view. The head detection process incorporates an object detector called You Only Look Once (YOLO) and is then followed by ear tag detection. The steps involved in ear tag recognition are (1) finding the four-digit area, (2) digit segmentation using an image processing technique, and (3) ear tag recognition using a convolutional neural network (CNN) classifier. Finally, a location searching system for an individual cow is established by entering the ID numbers through the application’s user interface. The proposed searching system was confirmed by performing real-time experiments at a feeding station on a farm at Hokkaido prefecture, Japan. In combination with our decision-making process, the proposed system achieved an accuracy of 100% for head detection, and 92.5% for ear tag digit recognition. The results of using our system are very promising in terms of effectiveness.
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Atkočiūnas, E., R. Blake, A. Juozapavičius, and M. Kazimianec. "Image Processing in Road Traffic Analysis." Nonlinear Analysis: Modelling and Control 10, no. 4 (October 25, 2005): 315–32. http://dx.doi.org/10.15388/na.2005.10.4.15112.

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The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the speed, and to recognize number plates of a car. Software developed was applied in and approved with video monitoring system, based on standard CCTV cameras connected to wide area network computers.
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Park, B. J., Seok Cheol Kim, D. H. Lee, Hyun Joo Son, K. C. Nam, K. Takatori, M. Aihara, and Jong Chul Park. "Computer-Assisted Image Processing Techniques for Quantitative Analysis of Cell Migrations on Collagen-Coated Glass." Key Engineering Materials 288-289 (June 2005): 503–6. http://dx.doi.org/10.4028/www.scientific.net/kem.288-289.503.

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In this study, a computer-assisted cell tracking system including an automatic image processing program for rapid and precise analysis of cell migration in various conditions was self-designed and L-929 cell migration on the glass coated with type I collagen was examined using this cell tracking system. Furthermore, computer-based image processing software, with the capture program to choose the capture interval and period, and analysis techniques were developed for quantitative analysis of the cell migration on extracellular matrices. The results showed that the migration speed of L-929 cells on the collagen-coated glass was significantly (p < 0.05) increased compared to the non-coated control. On the morphological observations, it was showed that the cells on the collagen-coated glass looked much healthier than those on the control. These results suggested that this cell tracking system would provide tools for the analysis of cell migration in various in vitro conditions and might be effective enough to evaluate various biological events including embryonic development as well as physiological and pathological tissue reorganization.
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Shariff, Aabid, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn, and Robert F. Murphy. "Automated Image Analysis for High-Content Screening and Analysis." Journal of Biomolecular Screening 15, no. 7 (May 20, 2010): 726–34. http://dx.doi.org/10.1177/1087057110370894.

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The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentation, tracing, tracking), (2) spatial transformation to bring images to a common reference frame (registration), (3) computation of image features, and (4) machine learning for modeling and interpretation of data. An overview of these image analysis tools is presented here, along with brief descriptions of a few applications.
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Guo, Pengyu, Shaowen Ding, Hongliang Zhang, and Xiaohu Zhang. "A Real-Time Optical Tracking and Measurement Processing System for Flying Targets." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/976590.

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Optical tracking and measurement for flying targets is unlike the close range photography under a controllable observation environment, which brings extreme conditions like diverse target changes as a result of high maneuver ability and long cruising range. This paper first designed and realized a distributed image interpretation and measurement processing system to achieve resource centralized management, multisite simultaneous interpretation and adaptive estimation algorithm selection; then proposed a real-time interpretation method which contains automatic foreground detection, online target tracking, multiple features location, and human guidance. An experiment is carried out at performance and efficiency evaluation of the method by semisynthetic video. The system can be used in the field of aerospace tests like target analysis including dynamic parameter, transient states, and optical physics characteristics, with security control.
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Barz, Michael, and Daniel Sonntag. "Automatic Visual Attention Detection for Mobile Eye Tracking Using Pre-Trained Computer Vision Models and Human Gaze." Sensors 21, no. 12 (June 16, 2021): 4143. http://dx.doi.org/10.3390/s21124143.

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Processing visual stimuli in a scene is essential for the human brain to make situation-aware decisions. These stimuli, which are prevalent subjects of diagnostic eye tracking studies, are commonly encoded as rectangular areas of interest (AOIs) per frame. Because it is a tedious manual annotation task, the automatic detection and annotation of visual attention to AOIs can accelerate and objectify eye tracking research, in particular for mobile eye tracking with egocentric video feeds. In this work, we implement two methods to automatically detect visual attention to AOIs using pre-trained deep learning models for image classification and object detection. Furthermore, we develop an evaluation framework based on the VISUS dataset and well-known performance metrics from the field of activity recognition. We systematically evaluate our methods within this framework, discuss potentials and limitations, and propose ways to improve the performance of future automatic visual attention detection methods.
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Nishino, Koichi, Nobuhide Kasagi, and Masaru Hirata. "Three-Dimensional Particle Tracking Velocimetry Based on Automated Digital Image Processing." Journal of Fluids Engineering 111, no. 4 (December 1, 1989): 384–91. http://dx.doi.org/10.1115/1.3243657.

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A three-dimensional particle tracking velocimeter for measuring an instantaneous distribution of all the three velocity components in a liquid flow is developed using an automated digital image processing technique. The measurement system consists of three TV cameras, a digital image processor, a laser disk recorder and a 16-bit microcomputer. Motions of neutrally buoyant tracer particles introduced into the flow field are observed by the cameras, and the three-dimensional displacement of each particle is calculated from consecutive TV frames recorded on the laser disk. Instantaneous velocity profiles in an unsteady laminar Couette flow between two concentric cylinders are measured. The results are in good agreement with predictions within the measurement uncertainties evaluated systematically. Furthermore, the measurement of a decaying turbulence in a stirred water tank demonstrates that the present technique is applicable to turbulent flows.
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Zhao, Ying, Yongxiang Sui, Jinsen Hou, Qun Sun, and Chong Wang. "Design of a Vehicle Chassis Inspection Robot Based on WiFi Network." Open Electrical & Electronic Engineering Journal 11, no. 1 (August 15, 2017): 154–64. http://dx.doi.org/10.2174/1874129001711010154.

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Background: The vehicle chassis inspection robot introduced in this paper is capable of realizing automatic tracking, obstacle avoidance, and video image acquisition. The robot can be connected to a computer, mobile phone or other terminals through a WiFi network built within the robot, so as to achieve real time control of the robot motion, and to display videos or images collected by the robot on the computer screen. The system is simple and easy to operate, with high stability, high flexibility, precise directional control, and can satisfy the requirements in harsh environment. Methods and Materials: This design adopts a STC11F32XE microcontroller as the core, uses an ultrasonic sensor to detect the road objects and calculate the distance to the objects, anticipates and avoids obstacle during processing. The camera performs image acquisition and returns the picture to help easy detection of automotive chassis and manual robot control. The robot uses an infrared sensor to realize automatic obstacle avoidance, and it controls the travel speed as well as automatic stop by changing the PWM duty cycle. Conclusion: Through this research, an intelligent vehicle parking inspection system has been developed.
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FATIKOW, SERGEJ, CHRISTIAN DAHMEN, TIM WORTMANN, and ROBERT TUNNELL. "VISUAL FEEDBACK METHODS FOR NANOHANDLING AUTOMATION." International Journal of Information Acquisition 06, no. 03 (September 2009): 159–69. http://dx.doi.org/10.1142/s0219878909001898.

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This paper presents different image processing methods and algorithms, which are needed to enable the reliable automation of nanohandling processes. These applications use the scanning electron microscope (SEM) as a visual sensor. SEMs are widespread and powerful tools for manipulations on the nanoscale. Due to the timing constraints in automated setups, the trade-off between SEM scanning speed and image quality is a concern for algorithm development. Tasks to be fulfilled on image data provided by the SEM include object recognition, object tracking and depth estimation. A selection of algorithms that have been applied in automated setups for nanomanipulation is discussed and validated.
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Sanchez-Tecuatl, Marcial, Ajelet Vargaz-Guadarrama, Juan Ramirez-Cortes, Pilar Gomez-Gil, Francesco Moccia, and Roberto Berra-Romani. "Automated Intracellular Calcium Profiles Extraction from Endothelial Cells Using Digital Fluorescence Images." International Journal of Molecular Sciences 19, no. 11 (November 2, 2018): 3440. http://dx.doi.org/10.3390/ijms19113440.

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Endothelial cells perform a wide variety of fundamental functions for the cardiovascular system, their proliferation and migration being strongly regulated by their intracellular calcium concentration. Hence it is extremely important to carefully measure endothelial calcium signals under different stimuli. A proposal to automate the intracellular calcium profiles extraction from fluorescence image sequences is presented. Digital image processing techniques were combined with a multi-target tracking approach supported by Kalman estimation. The system was tested with image sequences from two different stimuli. The first one was a chemical stimulus, that is, ATP, which caused small movements in the cells trajectories, thereby suggesting that the bath application of the agonist does not generate significant artifacts. The second one was a mechanical stimulus delivered by a glass microelectrode, which caused major changes in cell trajectories. The importance of the tracking block is evidenced since more accurate profiles were extracted, mainly for cells closest to the stimulated area. Two important contributions of this work are the automatic relocation of the region of interest assigned to the cells and the possibility of data extraction from big image sets in efficient and expedite way. The system may adapt to different kind of cell images and may allow the extraction of other useful features.
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Ma, Manzeng, Dan Liu, and Ruirui Zhang. "An Infrared Image Target Segmentation Based on Improved Threshold Method." International Journal of Circuits, Systems and Signal Processing 15 (July 30, 2021): 820–28. http://dx.doi.org/10.46300/9106.2021.15.90.

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In recent years, infrared images have been applied in more and more extensive fields and the current research of infrared image segmentation and recognition can’t satisfy the needs of practical engineering applications. The interference of various factors on infrared detectors result in the targets detected presenting the targets of low contrast, low signal-to-noise ratio (SNR) and fuzzy edges on the infrared image, thus increasing the difficulty of target detection and recognition; therefore, it is the key point to segment the target in an accurate and complete manner when it comes to infrared target detection and recognition and it has great importance and practical value to make in-depth research in this respect. Intelligent algorithms have paved a new way for infrared image segmentation. To achieve target detection, segmentation, recognition and tracking with infrared imaging infrared thermography technology mainly analyzes such features as the grayscale, location and contour information of both background and target of infrared image, segments the target from the background with the help of various tools, extracts the corresponding target features and then proceeds recognition and tracking. To seek the optimal threshold of an image can be seen as to find the optimum value of a confinement problem. As to seek the threshold requires much computation, to seek the threshold through intelligent algorithms is more accurate. This paper proposes an automatic segmentation method for infrared target image based on differential evolution (DE) algorithm and OTSU. This proposed method not only takes into consideration the grayscale information of the image, but also pays attention to the relevant information of neighborhood space to facilitate more accurate image segmentation. After determining the scope of the optimal threshold, it integrates DE’s ability of globally searching the optimal solution. This method can lower the operation time and improve the segmentation efficiency. The simulation experiment proves that this method is very effective.
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Wang, Hui, Cheng Wu Liang, and Ying Li. "Implementation of Intelligent Video Analysis System and it’s Algorithm Optimization Based on DSP." Advanced Materials Research 403-408 (November 2011): 217–22. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.217.

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This paper aims to implement an distributed intelligent video analysis system based on TI multimedia Digital Signal Processor (DSP) TMS320DM642, at the same time, the algorithm optimization is also described. The intelligent video analysis system we proposed provides users with fast and precise video analysis services. The video image is transmitted to the image processing board by analog channels and IP cameras, then the DSP processing the video flow. In this system, target detection, segmentation, feature extraction, alarm and automatic tracking can be implemented. In this system, several optimization techniques are also used in algorithms, including the algorithm level optimization (ALO), the program level optimization (PLO) and the instruction level optimization (ILO). Experimental results show the exellent result in reducing the CPU load.
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Xu, Guangzhi, Xiaohui Ma, Ping Chang, and Lin Wang. "Image-processing-based atmospheric river tracking method version 1 (IPART-1)." Geoscientific Model Development 13, no. 10 (September 30, 2020): 4639–62. http://dx.doi.org/10.5194/gmd-13-4639-2020.

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Abstract. Automated detection of atmospheric rivers (ARs) has been heavily relying on magnitude thresholding on either the integrated water vapor (IWV) or integrated vapor transport (IVT). Magnitude-thresholding approaches can become problematic when detecting ARs in a warming climate, because of the increasing atmospheric moisture. A new AR detection method derived from an image-processing algorithm is proposed in this work. Different from conventional thresholding methods, the new algorithm applies threshold to the spatiotemporal scale of ARs to achieve the detection, thus making it magnitude independent and applicable to both IWV- and IVT-based AR detection. Compared with conventional thresholding methods, it displays lower sensitivity to parameters and a greater tolerance towards a wider range of water vapor flux intensities. A new method of tracking ARs is also proposed, based on a new AR axis identification method and a modified Hausdorff distance that gives a measure of the geographical distances of AR axes pairs.
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Penchev, Daniel. "Selection of Proper Cells Using Connected Components Tracking Algorithms." Applied Mechanics and Materials 245 (December 2012): 90–96. http://dx.doi.org/10.4028/www.scientific.net/amm.245.90.

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This paper describes an approach of automation selecting the proper male sperm cells to be used for female egg cell injection. Connected components analyze is used for image processing and micro object tracking.
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Raeva, P., and K. Pavelka Jr. "OPTIMIZED POST-PROCESSING OF MULTIPLE UAV IMAGES FOR FORESTRY INSPECTIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 15–19. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-15-2020.

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Abstract. The following paper discusses possible optimized post-processing and data tracking of UAV imagery for forestry inspection. The survey took place in the National Natural Reserve Božídarské rašeliniště – The Wetland of Božídar from 2015 till now. The purpose of this study is to provide with a suitable post-processing method of UAV images in a protected area with no necessity of human interaction with the species. The authors used UAV imagery from RGB and multispectral sensors. The focus of the paper is the post-processing which relies solely on open-source tools. The results of the paper are a script for automatic computation of vegetation indices, a script for canopy height model in a certain part of the mapped area a possible GIS solution for storing and tracking the data.
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Shih, Frank Y., and Xin Zhong. "Automated Counting and Tracking of Vehicles." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (September 17, 2017): 1750038. http://dx.doi.org/10.1142/s0218001417500380.

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A robust traffic surveillance system is crucial in improving the control and management of traffic systems. Vehicle flow processing primarily involves counting and tracking vehicles; however, due to complex situations such as brightness changes and vehicle partial occlusions, traditional image segmentation methods are unable to segment and count vehicles correctly. This paper presents a novel framework for vision-based vehicle counting and tracking, which consists of four main procedures: foreground detection, feature extraction, feature analysis, and vehicles counting/tracking. Foreground detection intends to generate regions of interest in an image, which are used to produce significant feature points. Vehicles counting and tracking are achieved by analyzing clusters of feature points. As for testing on recorded traffic videos, the proposed framework is verified to be able to separate occluded vehicles and count the number of vehicles accurately and efficiently. By comparing with other methods, we observe that the proposed framework achieves the highest occlusion segment rate and the counting accuracy.
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Kulkarni, Narayan, and Ashok V. Sutagundar. "Detection of Human Facial Parts Using Viola-Jones Algorithm in Group of Faces." International Journal of Applied Evolutionary Computation 10, no. 1 (January 2019): 39–48. http://dx.doi.org/10.4018/ijaec.2019010103.

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Face detection is an image processing technique used in computer system to detect face in digital image. This article proposes an approach to detect faces and facial parts from an image of a group of people using the Viola Jones algorithm. Face detection is used in face recognition and identification systems. Automatic face detection and recognition is most challenging and a fast-growing research area in real-time applications like CC TV surveillance, video tracking, facial expression recognition, gesture recognition, human computer interaction, computer vision, and gender recognition. For face detection purposes various techniques and methods are applied in a computer system. In proposed system, a Viola Jones algorithm is implemented for multiple faces and facial parts and detected with a high rate of accuracy.
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Abbas, Ayman, and Khaled El-Geneidy. "Application of Image Processing and Fuzzy Logic to Mobile Robots Providing Assistance to Fire Fighters." Applied Mechanics and Materials 389 (August 2013): 740–46. http://dx.doi.org/10.4028/www.scientific.net/amm.389.740.

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The motive behind this research project is to devise a method for overcoming some of the challenges faced by fire fighters in Egypt while accomplishing their duties. This is achieved by utilizing robot vision technology as one of the approaches used for task automation. Based on a study of different methods of automation in human tracking and fire fighting applications, image processing techniques with the highest potential in a fire fighting environment were identified. A system has been developed which fusses the selected image processing algorithms with fuzzified readings from distance sensors, to extract the major blue areas in acquired images that is more likely to correspond to the uniform worn by fire fighters in Egypt. Subsequently the extracted blue area is used to identify a region of interest within the image in order to reduce the computations. The feature detection process constrains its search for a feature found on the back of the target fire fighter to the identified region of interest. Based on the location and area of this feature, the system will calculate the required velocity components to control the motion of the robot and the camera pan and tilt mechanism, in order to continue tracking the target along its path. The system has been validated by conducting an experiment which simulates the key influential factors in a fire fighting environment.
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Shobha Rani, N., Neethu O. P., and Nila Ponnath. "Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 4 (August 1, 2015): 869. http://dx.doi.org/10.11591/ijece.v5i4.pp869-878.

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Automatic detection, extraction and recognition of vehicle number plate region in traffic control systems is one of the prominent application in Computer vision. The drastic increase in number of vehicles in the current generation greatly increases the complexity in tracking the vehicles through the human visual system, manual procedure of controlling traffic and enforcement of various laws and rules is not sufficient for smooth control of traffic. This urges the need for development of technology that can automate this process. This paper mainly focuses on the development of an automatic number plate extraction and recognition algorithm by incorporating constructs like edge detection, horizontal and vertical edge processing using fixed threshold technique. The extracted number plate region is again processed using template matching algorithm for the recognition of the characters embossed on the number plate with respect to every individual piece of number plate. The algorithm developed has achieved an accuracy of around 100% and works for both front and rear images of the car.
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Chen, Bo, Chuan Bao Jia, and Ji Cai Feng. "Active Visual Sensor Based Weld Seam Tracking for Underwater Wet Welding." Advanced Materials Research 717 (July 2013): 588–91. http://dx.doi.org/10.4028/www.scientific.net/amr.717.588.

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Weld automation is the development trend of underwater welding, and underwater weld seam tracking is one of the key technologies in weld automation. This paper used active visual sensor to automatically monitor the weld seam in underwater wet weld process, and image processing algorithms were developed to automatically obtain the weld torch deviation, then the weld torch was adjusted automatically according to the deviation obtained by the image, experiment results showed that this method could be used in underwater wet welding.
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Konwar, Lakhyadeep, Anjan Kumar Talukdar, Kandarpa Kumar Sarma, Navajit Saikia, and Subhash Chandra Rajbangshi. "Segmentation and Selective Feature Extraction for Human Detection to the Direction of Action Recognition." International Journal of Circuits, Systems and Signal Processing 15 (September 8, 2021): 1371–86. http://dx.doi.org/10.46300/9106.2021.15.147.

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Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human detection and tracking technique for human action recognition toward the design of AVSS. Here we use median filter for noise removal, graph cut for segment the human images, mathematical morphology to refine the segmentation mask, extract selective feature points by sing HOG, classify human objects by using SVM with polynomial ker- nel and finally particle filter for tracking those of detected human. Due to the above mentioned combinations our system can independent to the variations of lightening conditions, color, shape, size, clothing etc. and can handle the occlusion. Our system can easily detect and track human in different indoor as well as outdoor environ- ment with a automatic multiple human detection rate of 97:61% and total multiple human detection and tracking accuracy is about 92% for AVSS. Due to the use of HOG to extract features af- ter graph cut segmentation operation, our system requires less memory for store the trained data therefore processing speed as well as accuracy of detection and tracking will be better than other techniques which can be suitable for action classification task.
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Blackmon, J. B. "Development and Performance of a Digital Image Radiometer for Heliostat Evaluation at Solar One." Journal of Solar Energy Engineering 107, no. 4 (November 1, 1985): 315–21. http://dx.doi.org/10.1115/1.3267699.

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A review is presented of the development, performance, and operation of a digital image radiometer (DIR) used to evaluate and enhance heliostat optical and tracking performance at the Solar One 10 MWe pilot plant at Daggett, Calif. The system, termed the beam characterization system (BCS), is based on digitizing, calibrating, and computer-processing video images of heliostat-reflected beams displayed on four 30- by 40-ft targets located on the tower beneath the receiver. Additionally, the radiance distribution of the sun is simultaneously recorded by a separate, specially modified solar-tracking video camera. The basic theory and analytical techniques used to determine beam centroid error (i.e., heliostat pointing errors), the actual incident beam power, spillage power off the receiver, and solar radiance distribution are described. The computer system is presented including the automatic data acquisition mode, the interface with the heliostat array controller (HAC), and the data acquisition system (DAS). Data display for plant operator purposes and additional data acquired and stored for more detailed engineering evaluations are discussed. Advanced applications of the DIR such as determination of total incident flux on a receiver from a field of heliostats, reflectance monitoring, and measurement of atmospheric attenuation are presented.
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