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Relevant bibliographies by topics / Automatic tracking. Image processing

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Academic literature on the topic 'Automatic tracking. Image processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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

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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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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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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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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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Dissertations / Theses on the topic "Automatic tracking. Image processing"

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Obolensky, Nicolas. "Kalman filtering methods for moving vehicle tracking." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1001174.

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Shaikh, Meher Talat. "Automatic Identification and Tracking of Retraction Fibers in Time-Lapse Microscopy." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3454.pdf.

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Doyle, Jason Emory. "Automatic Dynamic Tracking of Horse Head Facial Features in Video Using Image Processing Techniques." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87582.

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The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where the data from the algorithm is utilized to plot absolute motion vs. time, velocity vs. time, and acceleration vs. time for the head and ear motion, respectively, of a variety of horses and ponies. Two-dimensional plotting of x and y motion over time is also presented. Additionally, results of pilot work in eye detection in light colored horses is also presented. Detection of pain in horses is particularly difficult because they are prey animals and have mechanisms to disguise their pain, and these instincts may be particularly strong in the presence of an unknown human, such as a veterinarian. Current state-of-the art for detecting pain in horses primarily involves invasive methods, such as heart rate monitors around the body, drawing blood for cortisol levels, and pressing on painful areas to elicit a response, although some work has been done for humans to sort and score photographs subjectively in terms of a "horse grimace scale." The algorithms developed in this thesis are the first that the author is aware for exploiting proven image processing approaches from other applications for development of an automatic tool for detection and tracking of horse facial indicators. The algorithms were done in common open source programs Python and OpenCV, and standard image processing approaches including Canny Edge detection Hue, Saturation, Value color filtering, and contour tracking were utilized in algorithm development. The work in this thesis provides the foundational development of a non -invasive and automatic detection and tracking program for horse head and ear motion, including demonstration of the viability of this approach using videos of standing horses. This approach lays the groundwork for robust tool development for monitoring horses non-invasively and without the required presence of humans in such applications as post-operative monitoring, foaling, evaluation of performance horses in competition and/or training, as well as for providing data for research on animal welfare, among other scenarios.
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Park, Man-Woo. "Automated 3D vision-based tracking of construction entities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45782.

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In construction sites, tracking project-related entities such as construction equipment, materials, and personnel provides useful information for productivity measurement, progress monitoring, on-site safety enhancement, and activity sequence analysis. Radio frequency technologies such as Global Positioning Systems (GPS), Radio Frequency Identification (RFID) and Ultra Wide Band (UWB) are commonly used for this purpose. However, on large-scale congested sites, deploying, maintaining and removing such systems can be costly and time-consuming because radio frequency technologies require tagging each entity to track. In addition, privacy issues can arise from tagging construction workers, which often limits the usability of these technologies on construction sites. A vision-based approach that can track moving objects in camera views can resolve these problems. The purpose of this research is to investigate the vision-based tracking system that holds promise to overcome the limitations of existing radio frequency technologies for large-scale, congested sites. The proposed method use videos from static cameras. Stereo camera system is employed for tracking of construction entities in 3D. Once the cameras are fixed on the site, intrinsic and extrinsic camera parameters are discovered through camera calibration. The method automatically detects and tracks interested objects such as workers and equipment in each camera view, which generates 2D pixel coordinates of tracked objects. The 2D pixel coordinates are converted to 3D real-world coordinates based on calibration. The method proposed in this research was implemented in .NET Framework 4.0 environment, and tested on the real videos of construction sites. The test results indicated that the methods could locate construction entities with accuracy comparable to GPS.

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Katta, Pradeep. "Integrating depth and intensity information for vision-based head tracking." abstract and full text PDF (UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1456416.

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Preussner, Jonathan J. "Multiple target tracker and human classifier for radar application." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0009821.

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Clark, Daniel S. "Object detection and tracking using a parts-based approach /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1167.

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Tissainayagam, Prithiviraj 1967. "Visual tracking : development, performance evaluation, and motion model switching." Monash University, Dept. of Electrical and Computer Systems Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8944.

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Tanase, Cristina-Madalina. "Multi-person tracking system for complex outdoor environments." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-245082.

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The thesis represents the research in the domain of modern video tracking systems and presents the details of the implementation of such a system. Video surveillance is a high point of interest and it relies on robust systems that interconnect several critical modules: data acquisition, data processing, background modeling, foreground detection and multiple object tracking. The present work analyzes different state of the art methods that are suitable for each module. The emphasis of the thesis is on the background subtraction stage, as the final accuracy and performance of the person tracking dramatically dependent on it. The experimental results show the performance of four different foreground detection algorithms, including two variations of self-organizing feature maps for background modeling, a machine learning technique. The undertaken work provides a comprehensive view of the actual state of the research in the foreground detection field and multiple object tracking and offers solution for common problems that occur when tracking in complex scenes. The chosen data set for experiments covers extremely different and complex scenes (outdoor environments) that allow a detailed study of the appropriate approaches and emphasize the weaknesses and strengths of each algorithm. The proposed system handles problems like: dynamic backgrounds, illumination changes, camouflage, cast shadows, frequent occlusions and crowded scenes. The tracking obtains a maximum Multiple Object Tracking Accuracy of 92,5% for the standard video sequence MWT and a minimum of 32,3% for an extremely difficult sequence that challenges every method.

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Neumann, Markus. "Automatic multimodal real-time tracking for image plane alignment in interventional Magnetic Resonance Imaging." Phd thesis, Université de Strasbourg, 2014. http://tel.archives-ouvertes.fr/tel-01038023.

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Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient's body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based workflow is proposed and refined for automatic image plane alignment. An automatic tracking workflow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking workflow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the workflow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the workflow was evaluated with both computer simulations and experiments using an MR compatible testbed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor.

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Books on the topic "Automatic tracking. Image processing"

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International Conference on Information Fusion (3rd : 2000 Paris, France). FUSION 2000: Proceedings of the Third International Conference on Information Fusion : July 10-13, 2000, Cité des Sciences et de l'Industrie, Paris, France. Sunnyvale, USA: International Society of Information Fusion, 2000.

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Automatic generation of morphological set recognition algorithms. New York: Springer-Verlag, 1989.

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Schachter, Bruce J. Automatic target recognition. Bellingham, Washington: SPIE, 2016.

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Leppäjärvi, Seppo. Image segmentation and analysis for automatic color correction. Lappeenranta, Finland: Lappeenranta University of Technology, 1999.

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Powers, John P. Automatic particle sizing from rocket motor holograms. Monterey, Calif: Naval Postgraduate School, 1990.

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Essig, Kai. Vision-based image retrieval (VBIR): A new eye-tracking based approach to efficient and intuitive image retrieval. Saarbrücken: VDM Verlag Dr. Müller, 2008.

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Otsu, N. Multiple regression analysis approach to automatic design of adaptive image processing systems. Ottawa: National Research Council of Canada, Division of Electrical Engineering, 1987.

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Gini, Fulvio. Knowledge based radar detection, tracking, and classification. Hoboken, NJ: Wiley, 2008.

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Drummond, Oliver E. Signal and data processing of small targets 2010: 5-8 April 2010, Orlando, Florida, United States. Edited by SPIE (Society). Bellingham, Wash: SPIE, 2010.

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Drummond, Oliver E. Signal and data processing of small targets 2011: 23-25 August 2011, San Diego, California, United States. Edited by SPIE (Society). Bellingham, Wash: SPIE, 2011.

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Book chapters on the topic "Automatic tracking. Image processing"

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Maharjan, Samee, Dag Bjerketvedt, and Ola Marius Lysaker. "An Image Processing Framework for Automatic Tracking of Wave Fronts and Estimation of Wave Front Velocity for a Gas Experiment." In Representations, Analysis and Recognition of Shape and Motion from Imaging Data, 45–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19816-9_4.

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Juneja, Sagar, Saurav Kochar, and Sachin Dhiman. "Intelligent Algorithm for Automatic Multistoried Parking System Using Image Processing with Vehicle Tracking and Monitoring from Different Locations in the Building." In Advances in Intelligent Systems and Computing, 73–84. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6614-6_8.

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Ferilli, Stefano. "Image Processing." In Automatic Digital Document Processing and Management, 113–43. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-198-1_4.

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Burger, Wilhelm, and Mark J. Burge. "Automatic Thresholding." In Principles of Digital Image Processing, 5–50. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-84882-919-0_2.

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Tascini, Guido, Paolo Puliti, and Primo Zingaretti. "Multi-polygonal object tracking." In Image Analysis and Processing, 465–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_299.

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Fuentes, Luis M., and Sergio A. Velastin. "Tracking People for Automatic Surveillance Applications." In Pattern Recognition and Image Analysis, 238–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_28.

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Gobbetti, Enrico, Riccardo Scateni, and Gianluigi Zanetti. "Head and Hand Tracking Devices in Virtual Reality." In 3D Image Processing, 287–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-59438-0_26.

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Corridoni, J. M., and A. Bimbo. "Automatic video segmentation through editing analysis." In Image Analysis and Processing, 178–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_255.

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Jiang, Hao, Wilson Tiu, Shinji Yamamoto, and Shun-ichi Iisaku. "Automatic recognition of spicules in mammograms." In Image Analysis and Processing, 396–403. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63508-4_148.

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Yu, Shih-Hao, Jun-Wei Hsieh, Yung-Sheng Chen, and Wen-Fong Hu. "An Automatic Traffic Surveillance System for Vehicle Tracking and Classification." In Image Analysis, 379–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_52.

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Conference papers on the topic "Automatic tracking. Image processing"

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Kamada, R., and T. Hayashi. "Silhouette-based Object Tracking with Automatic Correction of Tracking-Error." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.710-018.

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Dao, Trung-Kien, Thi-Lan Le, David Harle, Paul Murray, Christos Tachtatzis, Stephen Marshall, Craig Michie, and Ivan Andonovic. "Automatic cattle location tracking using image processing." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362862.

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Wang, Zhen, Zhiling Long, Ghassan AlRegib, Amin Asjad, and Mohamed A. Deriche. "Automatic fault tracking across seismic volumes via tracking vectors." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7026182.

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Majumder, Anima, Laxmidhar Behera, and Venkatesh K. Subramanian. "Novel Techniques for Robust Lip Segmentations, Automatic Features Initialization and Tracking." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.759-006.

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Majumder, Anima, Laxmidhar Behera, and Venkatesh K. Subramanian. "Novel Techniques for Robust Lip Segmentations, Automatic Features Initialization and Tracking." In Signal and Image Processing. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.759-006.

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Yiu, B. W. S., K. Y. K. Wong, F. Y. L. Chin, and R. H. Y. Chung. "Explicit contour model for vehicle tracking with automatic hypothesis validation." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530122.

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De Senneville, B. Denis, P. Desbarats, M. Ries, C. T. W. Moonen, and N. Grenier. "Automatic Region Tracking for MR Glomerular Filtration Rate Analysis." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312999.

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Stamou, G. N., M. Krinidis, N. Nikolaidis, and I. Pitas. "A monocular system for automatic face detection and tracking." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631548.

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Gladman, Darrin, Jehu Osegbe, Wookjin Choi, and Joon Suk Lee. "Automatic motion tracking system for analysis of insect behavior." In Applications of Digital Image Processing XLIII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2020. http://dx.doi.org/10.1117/12.2568804.

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Price, Steven R., Stanton R. Price, Carey D. Price, and Clay B. Blount. "Pre-screener for automatic detection of road damage in SAR imagery via advanced image processing techniques." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2305052.

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Reports on the topic "Automatic tracking. Image processing"

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Van Nevel, Alan, Larry Peterson, and Charles Kenney. Image Processing for LADAR Automatic Target Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada389111.

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Acton, Scott T., and Nilanjan Ray. Clutter-Reducing, Scalable Image Processing Methods for Target Acquisition and Tracking. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada424575.

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