Relevant bibliographies by topics / Dominant point detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dominant point detection'

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

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Journal articles on the topic "Dominant point detection"

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Ansari, Nirwan, and Kuo-wei Huang. "Non-parametric dominant point detection." Pattern Recognition 24, no. 9 (1991): 849–62. http://dx.doi.org/10.1016/0031-3203(91)90004-o.

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Chau, C. P., and W. C. Siu. "New nonparametric dominant point detection algorithm." IEE Proceedings - Vision, Image, and Signal Processing 148, no. 5 (2001): 363. http://dx.doi.org/10.1049/ip-vis:20010576.

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Carmona-Poyato, A., N. L. Fernández-García, R. Medina-Carnicer, and F. J. Madrid-Cuevas. "Dominant point detection: A new proposal." Image and Vision Computing 23, no. 13 (2005): 1226–36. http://dx.doi.org/10.1016/j.imavis.2005.07.025.

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Wu, Wen-Yen. "A dynamic method for dominant point detection." Graphical Models 64, no. 5 (2002): 304–15. http://dx.doi.org/10.1016/s1077-3169(02)00008-4.

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Wu, Wen-Yen. "Dominant point detection using adaptive bending value." Image and Vision Computing 21, no. 6 (2003): 517–25. http://dx.doi.org/10.1016/s0262-8856(03)00031-3.

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Wu, W.-Y. "A simple method for dominant point detection." Imaging Science Journal 49, no. 3 (2001): 125–34. http://dx.doi.org/10.1080/13682199.2001.11784375.

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Nguyen, Thanh Phuong, and Isabelle Debled-Rennesson. "A discrete geometry approach for dominant point detection." Pattern Recognition 44, no. 1 (2011): 32–44. http://dx.doi.org/10.1016/j.patcog.2010.06.022.

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Garrido, A., N. Pérez de la blanca, and M. Garcia-Silvente. "Boundary simplification using a multiscale dominant-point detection algorithm." Pattern Recognition 31, no. 6 (1998): 791–804. http://dx.doi.org/10.1016/s0031-3203(97)00104-0.

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Held, A., K. Abe, and C. Arcelli. "Towards a hierarchical contour description via dominant point detection." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 6 (1994): 942–49. http://dx.doi.org/10.1109/21.293514.

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Sarkar, Biswajit, Sanghamitra Roy, and Debranjan Sarkar. "Hierarchical representation of digitized curves through dominant point detection." Pattern Recognition Letters 24, no. 15 (2003): 2869–82. http://dx.doi.org/10.1016/s0167-8655(03)00145-4.

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Dissertations / Theses on the topic "Dominant point detection"

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Favier, Eric. "Contribution de l'analyse multi-résolution à la description des contours et des textures." Saint-Etienne, 1994. http://www.theses.fr/1994STET4020.

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Cette thèse s'attache à l'étude multirésolution des contours discrets et des images à niveaux de gris de texture. Le but est de fournir une description de ces objets à différentes échelles d'étude et d'essayer de déterminer la ou les échelles d'étude les plus appropriées pour l'analyse de ceux-ci. La première partie de ce travail se rapporte à l'étude des images binaires et plus particulièrement à l'étude des contours discrets. La notion d'échelle d'étude d'un contour est définie ainsi que des algorithmes permettant de la choisir. Pour chaque contour, on détermine la (ou les) échelle(s) d'étude permettant de le décrire de manière optimale. Des algorithmes de calculs de courbures en chaque point du contour, de détermination de points dominants en fonction de l'échelle d'étude choisie sont décrits. Une définition de la convexité est donnée en fonction des études choisies ainsi que la notion de t enveloppe convexe. Il est également présenté une distance sur l'ensemble des contours discrets. Cette dernière est une distance de convexité à une échelle d'étude donnée qui permet de comparer deux contours indépendamment de leur taille sur un critère de convexité. De plus, un lien est tissé entre ces différents algorithmes et les opérations de granulométrie ou d'ouvertures connues dans la morphologie mathématique. La seconde partie de cette thèse aborde l'étude des images en niveaux de gris multitexturées, là aussi le but est de décrire ces images en fonction de l'échelle d'étude et de trouver les bons paramètres pour l'analyse de ce type d'images. Les méthodes sont d'ordre statistique et les processus mis en oeuvre sont liés aux approches multirésolutions. Un modèle gaussien d'images de texture est présenté. Chaque image est étudiée à différentes échelles d'étude, et le choix de la meilleure échelle d'étude est abordé ce qui permet de proposer des méthodes automatiques de détection des zones de texture semblable. Des résultats sont présentés pour des exemples d'images multitexturées et une analyse des résultats montre que nos méthodes permettent de segmenter de manière très satisfaisante certaines images qui posent des problèmes à de nombreux algorithmes existants
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Pham, The Anh. "Détection robuste de jonctions et points d'intérêt dans les images et indexation rapide de caractéristiques dans un espace de grande dimension." Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4023/document.

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Les caractéristiques locales sont essentielles dans de nombreux domaines de l’analyse d’images comme la détection et la reconnaissance d’objets, la recherche d’images, etc. Ces dernières années, plusieurs détecteurs dits locaux ont été proposés pour extraire de telles caractéristiques. Ces détecteurs locaux fonctionnent généralement bien pour certaines applications, mais pas pour toutes. Prenons, par exemple, une application de recherche dans une large base d’images. Dans ce cas, un détecteur à base de caractéristiques binaires pourrait être préféré à un autre exploitant des valeurs réelles. En effet, la précision des résultats de recherche pourrait être moins bonne tout en restant raisonnable, mais probablement avec un temps de réponse beaucoup plus court. En général, les détecteurs locaux sont utilisés en combinaison avec une méthode d’indexation. En effet, une méthode d’indexation devient nécessaire dans le cas où les ensembles de points traités sont composés de milliards de points, où chaque point est représenté par un vecteur de caractéristiques de grande dimension<br>Local features are of central importance to deal with many different problems in image analysis and understanding including image registration, object detection and recognition, image retrieval, etc. Over the years, many local detectors have been presented to detect such features. Such a local detector usually works well for some particular applications but not all. Taking an application of image retrieval in large database as an example, an efficient method for detecting binary features should be preferred to other real-valued feature detection methods. The reason is easily seen: it is expected to have a reasonable precision of retrieval results but the time response must be as fast as possible. Generally, local features are used in combination with an indexing scheme. This is highly needed for the case where the dataset is composed of billions of data points, each of which is in a high-dimensional feature vector space
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Chao, Mau Kai, and 趙茂凱. "Genetic Algorithms for Dominant Point Detection." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/21362253397919874270.

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碩士<br>義守大學<br>工業工程與管理學系<br>92<br>From image grabbing, image preprocessing and pattern recognition, machine vision system has integrated a lot image processing techniques, for example: space and frequency domain processing. Many researchers has much contribution in this field, however, these processing techniques still need to be improved or optimized. Dominant point extraction of curve is the main issue in the machine vision system. Dominant point extraction utilized few but significance edge point to represent the contour of a substance. Therefore, this research employed the characteristic of genetic algorithm to: 1.Detect the dominant point curve under some circumstances, 2.Automatically capture the pattern features under no further constraint, 3.Establish the related pattern recognition system by using pattern dominant point as a template. The flowchart and parameter setting in genetic algorithm are built to satisfy above objectives. Preliminary experiment is conducted on our frequently used pattern samples by utilize dominant point extraction. As our proposed approach fitted, the dominant point extraction of curve is executed. The comparison with other researchers is also made based on these pattern samples. This study focused on three points - number of straight lines, error value and automatic extraction, and four artificial patterns - chromosome, ∞, leaf and semicircle. The examination and analyzation of 100 sample sizes describe that our research has advantage in setting genetic algorithm parameters and determining the best approximation value under finite limitation. Besides, from the distribution of different types of compress rate and sum of square error, we can find out that same type but different distribution pattern has the same trend. Moreover, the first and second types of distribution patterns are alike. For future direction, we expect to use different substances in order to examine the parameter setting in genetic algorithm approach proposed. By this way, our proposed in setting the parameter of genetic algorithm and its flowchart will proved to be well developed, since it can deal with different scenarios. In consideration to the implementation in real world, we have referred to the previous researches in this field, in order to seek for the optimal solution.
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Cheng, Ming-shun, and 鄭明順. "Dominant Point Detection Using Ant Colony Optimization." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/32545989509998974602.

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碩士<br>義守大學<br>工業工程與管理學系碩士班<br>94<br>Recently, the application of machine vision has used in our life widely. One of the applications of machine vision is object dominant point detection. In this paper, the object dominant point detection consists of four steps: (1) Image Pre-processing: thresholding is used to extract the object i need, and then noise is reduced and object contour is smoothed by closing and opening, respectively; (2) Contour Tracking: search for the contour of extracted object; (3) Break Point Detection: detect break points using freeman chain code, and (4) Dominant Point Detection: perform ant colony optimization to approximate curve. Dominant point detection can be classified into three categories according to restriction places in the problem: (1) Error-bounded problem: the error between approximated polygonal and object is fixed and the objective function is minimum the number of segment of approximated polygonal. Ant colony optimization has been successfully utilized in this problem. However, the hybrid strategy is time consuming. Here, a method which combines the concept of corner detection as well as the error-bounded constraint is used to segment the curves; (2) Segment-bounded problem: the number of segment of approximated polygonal is fixed and the objective function is minimum the error between approximated polygonal and object. Here, the farthest distance ant can go is conduced in node transition rule to satisfy the constraint in this problem. Moreover, three hybrid strategies, minimum error method, maximal distance method, and random selection method has been performed in this problem, and (3) No restriction problem: there is not any constrain. A fitness value has been proposed to suit this constrain. Moreover, three hybrid strategies, minimum error method, maximal distance method, and random selection method has been performed in this problem. The experimental results show that the hybrid strategies used in three problems are effectively and they takes less time than previous method.
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Wang, Shiann-Jang, and 王獻章. "Adaptive Dominant Point Detection via the Rated Composite Vectors." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/92524064207666312144.

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Chen, Kao-Sheng, and 陳高生. "An Improvement for the Detection of Dominant Points on Digital Curve." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/94787862207191457779.

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碩士<br>國立中山大學<br>機械與機電工程學系研究所<br>92<br>In our research, we propose two improve methods for detection of dominant points for a digitized curve. Instead of setting a fixed length of support region, it will compute the suitable length of support region for each point to find the best approximated curvature. First, the symmetric region of support for each point on curve is determined using diagonal value. The points with local maximum averaging diagonal value can be located as the dominant points on the curve. Second, the improve method uses an optimal criterion for determining the asymmetric region of support of each boundary point, and a new mechanism for selecting the dominant points.
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Book chapters on the topic "Dominant point detection"

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Ngo, Phuc, Hayat Nasser, and Isabelle Debled-Rennesson. "Efficient Dominant Point Detection Based on Discrete Curve Structure." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26145-4_11.

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del Campo, Miguel Vázquez-Martin, Hermilo Sánchez-Cruz, César Omar Jiménez-Ibarra, and Mario Alberto Rodríguez-Díaz. "Polyhedral Approximation for 3D Objects by Dominant Point Detection." In Advances in Computer Vision and Computational Biology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71051-4_14.

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Prasad, Dilip K., Chai Quek, and Maylor K. H. Leung. "A Non-heuristic Dominant Point Detection Based on Suppression of Break Points." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31295-3_32.

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Carmona-Poyato, A., N. L. Fernandez-Garcia, and R. Muñoz-Salinas. "A New Algorithm for Dominant Point Detection by Quasi-collinear Break Points Supression." In Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88458-3_43.

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Melen, Trond, and Takouhi Ozanian. "A fast algorithm for dominant point detection on chain-coded contours." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57233-3_33.

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Nguyen, Thanh Phuong, and Isabelle Debled-Rennesson. "Decomposition of a Curve into Arcs and Line Segments Based on Dominant Point Detection." In Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_74.

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Carmona-Poyato, A., N. L. Fernández-García, R. Medina-Carnicer, and F. J. Madrid-Cuevas. "A Method for Dominant Points Detection and Matching 2D Object Identification." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30125-7_53.

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Liao, Z. W., S. X. Hu, and T. Z. Huang. "Line Segments and Dominate Points Detection Based on Hough Transform." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_135.

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Sourav Saha, Saptarshi Roy, Prasenjit Dey, Soumya Pal, Tamal Chakraborty, and Priya Ranjan Sinha Mahapatra. "A Computer Vision Framework for Detecting Dominant Points on Contour of Image-Object Through Thick-Edge Polygonal Approximation." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2035-3_54.

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Nie, Hengchang, Genshui Wang, and Xianlin Dong. "New Bismuth Sodium Titanate Based Ceramics and Their Applications." In Ceramic Materials [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93921.

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Ferroelectric materials are widely investigated due to their excellent properties and versatile applications. At present, the dominant materials are lead-containing materials, such as Pb (Zr,Ti)O3 solid solutions. However, the use of lead gives rise to environmental concerns, which is the driving force for the development of alternative lead-free ferroelectric materials. (Bi0.5Na0.5)TiO3-based ceramics are considered to be one of the most promising lead-free materials to replace lead-containing ferroelectric ceramics due to their excellent ferroelectric properties, relaxation characteristics, and high Curie point. After decades of efforts, great progress has been made in the phase structure characterization and properties improvement of BNT based ceramics. However, most of the studies on BNT system mainly focuses on its piezoelectric properties and application of piezoelectric sensors and strain actuators, little attention is paid to its ferroelectric properties and related applications. In this chapter, new BNT-based ceramics via composition modification and special focuses on the ferroelectric properties, phase transition behaviors under external fields and related applications, such as application in energy storage, pulsed power supply and pyroelectric detection were proposed.
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Conference papers on the topic "Dominant point detection"

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Ansari, Nirwan, and KuoWei Huang. "Nonparametric dominant point detection." In Visual Communications, '91, Boston, MA, edited by Kou-Hu Tzou and Toshio Koga. SPIE, 1991. http://dx.doi.org/10.1117/12.50401.

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Hui, Lim Ai, and Kong Voon Pang. "Dominant point detection for planar data." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954527.

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Oral, L., and K. Ozkan. "Suboptimal optimization method for dominant point detection." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531295.

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Hui, Lim Ai, and Kong Voon Pang. "Dominant point detection by variation of best fit vectors." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2016 (ICoMEIA2016): Proceedings of the 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016. Author(s), 2016. http://dx.doi.org/10.1063/1.4965156.

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Prasad, Dilip K., and Chai Quek. "Comparison of error bounds for non-parametric dominant point detection." In 2013 9th International Conference on Information, Communications & Signal Processing (ICICS). IEEE, 2013. http://dx.doi.org/10.1109/icics.2013.6782883.

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Marston, R. E. "Polygonal approximation of outlines by scale-based dominant point detection." In Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950679.

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Jiwon Choi, Wonjun Kim, Haejung Kong, and Changick Kim. "Real-time vanishing point detection using the Local Dominant Orientation Signature." In 2011 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON 2011). IEEE, 2011. http://dx.doi.org/10.1109/3dtv.2011.5877194.

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Dinesh, R., and D. S. Guru. "Finite Automata Inspired Model for Dominant Point Detection: A Non-Parametric Approach." In 2007 International Conference on Computing: Theory and Applications (ICCTA'07). IEEE, 2007. http://dx.doi.org/10.1109/iccta.2007.63.

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Yang, Jun-Yu, Feng-Li Lian, and Jiun-Jau Lai. "Vanishing point detection using local dominant orientation signature for omnidirectional vision-based robots." In 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2012. http://dx.doi.org/10.1109/robio.2012.6491276.

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Georgakis, Christos, Petros Maragos, Georgios Evangelopoulos, and Dimitrios Dimitriadis. "Dominant spatio-temporal modulations and energy tracking in videos: Application to interest point detection for action recognition." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6466966.

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