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Relevant bibliographies by topics / Model-based recognition

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Academic literature on the topic 'Model-based recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Model-based recognition"

1

Tran, Chi-Kien. "Face Recognition Based on similarity Feature-Based Selection and Classification Algorithms and Wrapper Model." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 357–62. http://dx.doi.org/10.18178/ijmlc.2019.9.3.810.

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Wang, Yi Qiang, Rui Jian Huang, Tian Yi Xu, and Ke Hong Tang. "Vehicle Model Recognition Based on Fuzzy Pattern Recognition Method." Advanced Materials Research 383-390 (November 2011): 4799–802. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4799.

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The method based on the theory of Fuzzy Pattern Recognition is divided into three parts. Firstly, use Hough transformation to extract the feature points of vehicles, and use the ratio between two absolute distance of adjacent feature points as the characteristic values of vehicles; secondly, use Fuzzy C-mean Classification to handle feature data of 75 car model, then establish a degree of membership matrix as the sample space; thirdly, consider the classification algorithm based on fuzzy approach degree and the credibility of the vehicle feature to propose a weighted close- degree recognition algorithm. This recognition method has a good effect.

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Imamverdiyev, Yadigar, and Lyudmila Sukhostat. "DIALECTS RECOGNITION BASED ON ACOUSTIC MODEL." Problems of Information Technology 07, no. 2 (July 19, 2016): 34–38. http://dx.doi.org/10.25045/jpit.v07.i2.04.

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Russo, Mladen, Maja Stella, Marjan Sikora, and Vesna Pekić. "Robust Cochlear-Model-Based Speech Recognition." Computers 8, no. 1 (January 1, 2019): 5. http://dx.doi.org/10.3390/computers8010005.

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Accurate speech recognition can provide a natural interface for human–computer interaction. Recognition rates of the modern speech recognition systems are highly dependent on background noise levels and a choice of acoustic feature extraction method can have a significant impact on system performance. This paper presents a robust speech recognition system based on a front-end motivated by human cochlear processing of audio signals. In the proposed front-end, cochlear behavior is first emulated by the filtering operations of the gammatone filterbank and subsequently by the Inner Hair cell (IHC) processing stage. Experimental results using a continuous density Hidden Markov Model (HMM) recognizer with the proposed Gammatone Hair Cell (GHC) coefficients are lower for clean speech conditions, but demonstrate significant improvement in performance in noisy conditions compared to standard Mel-Frequency Cepstral Coefficients (MFCC) baseline.

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Ahn, J. S., and B. Bhanu. "Model-based recognition of articulated objects." Pattern Recognition Letters 23, no. 8 (June 2002): 1019–29. http://dx.doi.org/10.1016/s0167-8655(02)00033-8.

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Song, Mingliang. "Vehicle Model Recognition Based on SURF." Journal of Information and Computational Science 12, no. 17 (November 20, 2015): 6249–56. http://dx.doi.org/10.12733/jics20107056.

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KIM, SUNGHO, GIJEONG JANG, WANG-HEON LEE, and IN SO KWEON. "COMBINED MODEL-BASED 3D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 07 (November 2005): 839–52. http://dx.doi.org/10.1142/s0218001405004368.

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This paper presents a combined model-based 3D object recognition method motivated by the robust properties of human vision. The human visual system (HVS) is very efficient and robust in identifying and grabbing objects, in part because of its properties of visual attention, contrast mechanism, feature binding, multiresolution and part-based representation. In addition, the HVS combines bottom-up and top-down information effectively using combined model representation. We propose a method for integrating these aspects under a Monte Carlo method. In this scheme, object recognition is regarded as a parameter optimization problem. The bottom-up process initializes parameters, and the top-down process optimizes them. Experimental results show that the proposed recognition model is feasible for 3D object identification and pose estimation.

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Chin, Roland T., and Charles R. Dyer. "Model-based recognition in robot vision." ACM Computing Surveys 18, no. 1 (March 1986): 67–108. http://dx.doi.org/10.1145/6462.6464.

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Lamdan, Y., J. T. Schwartz, and H. J. Wolfson. "Affine invariant model-based object recognition." IEEE Transactions on Robotics and Automation 6, no. 5 (1990): 578–89. http://dx.doi.org/10.1109/70.62047.

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Schweitzer, Haim, and Sanjeev R. Kulkarni. "Computational limitations of model-based recognition." International Journal of Intelligent Systems 13, no. 5 (May 1998): 431–43. http://dx.doi.org/10.1002/(sici)1098-111x(199805)13:5<431::aid-int4>3.0.co;2-n.

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Dissertations / Theses on the topic "Model-based recognition"

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Zhou, Ziheng. "Model-based gait extraction and recognition." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438517.

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Lauziere, Yves Berude. "A model-based road sign recognition system /." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=38151.

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A road sign recognition system poses a real challenge for machine vision. It must recognize a wide variety of road signs under considerable variations in illumination and imaging geometry---all in real-time. This thesis presents a modular road sign recognition system relying on modelling for both detection and recognition. It divides into three main stages of processing. The first, concerned with detection, exploits the specific colors of road signs. The color constancy problem caused by the daylight illumination variations is addressed directly with a physics-based model supplemented by a calibration stage using real data. The second stage of processing, devoted to recognizing road signs in regions of interest found in the detection phase, involves a database containing more than 400 road signs arranged in a tree structure, and uses a novel correlation-based template matching technique relying on a bitwise encoding that accounts for both color labels and affine variations in the image formation process, and which also allows to build templates that are able to represent classes of objects. The content of the database used by the recognition algorithm is generated in a deterministic and automated manner by way of geometrical modelling of the image formation process starting with only model images of the road signs to be recognized. The recognition algorithm exploits color as a first logical classification step to direct the search for a road sign in the database, with the later finer steps being driven by correlation scores obtained from template matching. At the third stage of processing, a scene understanding module exploits constraints on the position of road signs along with the spatial relationships they must have in certain cases to other road signs in the image to filter out false positives. During processing, the system incorporates top-down mechanisms that use data fed back by partial recognitions, which allow to progressively gain more information about

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Beattie, Valerie L. "Hidden Markov Model state-based noise compensation." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259519.

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Beis, Jeffrey S. "Indexing without invariants in model-based object recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq25014.pdf.

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Gales, Mark John Francis. "Model-based techniques for noise robust speech recognition." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319311.

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黃業新 and Yip-san Wong. "A two-level model-based object recognition technique." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213807.

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Wong, Yip-san. "A two-level model-based object recognition technique /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14705552.

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Correa, Telmo Luis Jr. "A model for transition-based visuospatial pattern recognition." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66411.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 87).
In my research, I designed and implemented a system for learning and recognizing visual actions based on state transitions. I recorded three training videos of each of 16 actions (approach, bounce, carry, catch, collide, drop, fly over, follow, give, hit, jump, pick, push, put, take, throw), each lasting 10 seconds and 300 frames. After using a prototype system developed by Dr. Satyajit Rao for focus and actor recognition, actions are represented as qualitative state transitions, tied together to form tens of thousands of patterns, which are then available as action classifiers. The resulting system was able to build simple, intuitive classifiers that fit the training data perfectly.
by Telmo Luis Correa Junior.
M.Eng.

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Crawford, Gordon Finlay. "Vision-based analysis, interpretation and segmentation of hand shape using six key marker points." Thesis, University of Ulster, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243732.

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Oshitani, Tohru, and Toyohide Watanabe. "Parallel map recognition based on multilayer partitioned blackboard model." IEEE, 1998. http://hdl.handle.net/2237/6916.

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Books on the topic "Model-based recognition"

1

Lanitis, Andreas. Model-based recognition of variable objects. Manchester: University of Manchester, 1995.

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Dawson, K. M. Model-based 3-D object recognition using scalartransform descriptors. Dublin: Trinity College, Department of Computer Science, 1991.

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Horton, Timothy J. Occluded model-based recognition with orientation and spatial occupancy representations. Toronto: University of Toronto, Dept. of Computer Science, 1990.

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Horton, Timothy J. Occluded model-based recognition with orientation and spatial occupancy representations. Toronto: University of Toronto, Dept. of Computer Science, 1990.

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Model-based image matching using location. Cambridge, Mass: MIT Press, 1985.

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Palm, Rainer. Model Based Fuzzy Control: Fuzzy Gain Schedulers and Sliding Mode Fuzzy Controllers. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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7

N, Nasr Hatem, ed. Selected papers on model-based vision. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1993.

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N, Nasr Hatem, Larson Rodney M, and Society of Photo-optical Instrumentation Engineers., eds. Model-based vision: 19-20 November 1992, Boston, Massachusetts. Bellingham, Wash., USA: SPIE, 1993.

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M, Larson Rodney, Nasr Hatem N, and Society of Photo-optical Instrumentation Engineers., eds. Model-based vision development and tools: 14-15 November 1991, Boston, Massachusetts. Bellingham, Wash: SPIE, 1992.

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Nosofsky, Robert M., and Thomas J. Palmeri. An Exemplar-Based Random-Walk Model of Categorization and Recognition. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.7.

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In this chapter, we provide a review of a process-oriented mathematical model of categorization known as the exemplar-based random-walk (EBRW) model (Nosofsky & Palmeri, 1997a). The EBRW model is a member of the class of exemplar models. According to such models, people represent categories by storing individual exemplars of the categories in memory, and classify objects on the basis of their similarity to the stored exemplars. The EBRW model combines ideas ranging from the fields of choice and similarity, to the development of automaticity, to response-time models of evidence accumulation and decision-making. This integrated model explains relations between categorization and other fundamental cognitive processes, including individual-object identification, the development of expertise in tasks of skilled performance, and old-new recognition memory. Furthermore, it provides an account of how categorization and recognition decision-making unfold through time. We also provide comparisons with some other process models of categorization.

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Book chapters on the topic "Model-based recognition"

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Vuilleumier Stückelberg, Marc, and David Doermann. "Model-Based Graphics Recognition." In Graphics Recognition Recent Advances, 121–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-40953-x_10.

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Sun, Min, and Silvio Savarese. "Model-Based Object Recognition." In Computer Vision, 488–92. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_334.

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Yam, Chew-Yean, and Mark S. Nixon. "Gait Recognition, Model-Based." In Encyclopedia of Biometrics, 633–39. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_37.

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Yam, Chew-Yean, and Mark Nixon. "Gait Recognition, Model-Based." In Encyclopedia of Biometrics, 799–805. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_37.

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Haindl, Michal. "Model-Based Pattern Recognition." In Pattern Recognition and String Matching, 219–54. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0231-5_9.

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Yam, Chew-Yean, and Mark Nixon. "Gait Recognition, Model-Based." In Encyclopedia of Biometrics, 1–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27733-7_37-3.

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Guo, Yanqing, Ran He, Wei-Shi Zheng, and Xiangwei Kong. "Active Shape Model Based on Sparse Representation." In Biometric Recognition, 94–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_12.

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Huang, Weihua, Chew Lim Tan, and Wee Kheng Leow. "Model-Based Chart Image Recognition." In Graphics Recognition. Recent Advances and Perspectives, 87–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25977-0_8.

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Plötz, Thomas, and Gernot A. Fink. "Markov Model Based Handwriting Recognition." In SpringerBriefs in Computer Science, 27–45. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2188-6_4.

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Sun, Wenhui, Jucheng Yang, Ying Xie, Shanshan Fang, and Na Liu. "Finger-Vein Recognition Based on an Enhanced HMAX Model." In Biometric Recognition, 263–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_29.

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Conference papers on the topic "Model-based recognition"

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Miller, R. J., and D. J. Shephard. "Model-Based Aircraft Recognition." In 2006 International Radar Symposium. IEEE, 2006. http://dx.doi.org/10.1109/irs.2006.4338138.

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Maraee, Azzam, and Mira Balaban. "Efficient recognition of finite satisfiability in UML class diagrams: Strengthening by propagation of disjoint constraints." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031714.

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Noorit, Nattapon, Nikom Suvonvorn, and Montri Karnchanadecha. "Model-based human action recognition." In Second International Conference on Digital Image Processing. SPIE, 2010. http://dx.doi.org/10.1117/12.853223.

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Martins, Pedro, and Jorge Batista. "Accurate single view model-based head pose estimation." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813369.

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Li, Bingcheng. "Simple linear regression model based data clustering." In Automatic Target Recognition XXIX, edited by Timothy L. Overman and Riad I. Hammoud. SPIE, 2019. http://dx.doi.org/10.1117/12.2518037.

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Sawhney, Harpreet S., Rakesh Kumar, Allen R. Hanson, and Edward M. Riseman. "Landmark-based navigation: model extension and refinement." In Applied Imaging Pattern Recognition, edited by Jane Harmon. SPIE, 1993. http://dx.doi.org/10.1117/12.142785.

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Bargmann, Robert, Volker Blanz, and Hans-Peter Seidel. "A nonlinear viseme model for triphone-based speech synthesis." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813362.

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Boom, B. J., L. J. Spreeuwers, and R. N. J. Veldhuis. "Model-based reconstruction for illumination variation in face images." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813364.

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Prokaj, Jan, and Gerard Medioni. "3-D model based vehicle recognition." In 2009 Workshop on Applications of Computer Vision (WACV 2009). IEEE, 2009. http://dx.doi.org/10.1109/wacv.2009.5403032.

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Hui Chen, Jiangdong Li, Fengjun Zhang, Yang Li, and Hongan Wang. "3D model-based continuous emotion recognition." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298793.

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Reports on the topic "Model-based recognition"

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Jacobs, D. W., and T. D. Alter. Uncertainty Propagation in Model-Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada295642.

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Shvaytser, Haim, and Sanjeev R. Kulkarni. Computational Limitations of Model Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada459522.

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Gindi, Gene. Neural Networks for Model-Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada277375.

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Moses, Yael, and Shimon Ullman. Limitations of Non Model-Based Recognition Schemes. Fort Belvoir, VA: Defense Technical Information Center, May 1991. http://dx.doi.org/10.21236/ada241162.

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Weiss, Isaac. Model-Based Recognition of 3D Curves from One View. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada305201.

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Grimson, W. E., and Daniel P. Huttenlocher. On the Verification of Hypothesized Matches in Model-Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 1989. http://dx.doi.org/10.21236/ada214718.

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Sekuler, Robert. Model-based Analysis of Associative Recognition, Temporal Context and Retrieval. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada597736.

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Moses, Randolph L., Lee C. Potter, and Inder J. Gupta. Feature Extraction Using Attributed Scattering Center Models for Model-Based Automatic Target Recognition (ATR). Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada444563.

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Raulet, Gérard. What Happens is Unimaginable! About the „Yellow Vests“. Association Inter-University Centre Dubrovnik, February 2021. http://dx.doi.org/10.53099/ntkd4303.

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The French ‘yellow vests’ movement is anything but an episodic protest movement. It questions both the liberal and the republican conception of political representation. The reason for this radicalism is that it shakes the foundations of a neo-capitalist order, for which shortterm financial sales have become more important than the long-term maintenance of the system itself. From the financial crisis of 2008, neoliberalism only seems to have learned that,despite everything, the model on which it is based holds up. This creates a profound crisis of legitimacy that reveals a break in political culture that no policy of consensus or even recognition can remedy. This essay examines the theoretical approaches that can take this phenomenon into account.

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-consuming and expensive. The main objective of this study is to develop methods and algorithms to automate the post-processing of the visual sensor data towards the extraction of three main categories of information: 1) object information such as object identity, shapes, and spatial relationships - a novel heuristic-based method is proposed to automate the detection and recognition of main structural elements of steel girder bridges in both terrestrial and unmanned aerial vehicle (UAV)-based laser scanning data. Domain knowledge on the geometric and topological constraints of the structural elements is modeled and utilized as heuristics to guide the search as well as to reject erroneous detection results. 2) structural damage information, such as damage locations and quantities - to support the assessment of damage associated with small deformations, an advanced crack assessment method is proposed to enable automated detection and quantification of concrete cracks in critical structural elements based on UAV-based visual sensor data. In terms of damage associated with large deformations, based on the surface normal-based method proposed in Guldur et al. (2014), a new algorithm is developed to enhance the robustness of damage assessment for structural elements with curved surfaces. 3) three-dimensional volumetric models - the object information extracted from the laser scanning data is exploited to create a complete geometric representation for each structural element. In addition, mesh generation algorithms are developed to automatically convert the geometric representations into conformal all-hexahedron finite element meshes, which can be finally assembled to create a finite element model of the entire bridge. To validate the effectiveness of the developed methods and algorithms, several field data collections have been conducted to collect both the visual sensor data and the physical measurements from experimental specimens and in-service bridges. The data were collected using both terrestrial laser scanners combined with images, and laser scanners and cameras mounted to unmanned aerial vehicles.

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