Relevant bibliographies by topics / Partial object matching

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

Academic literature on the topic 'Partial object matching'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Partial object matching"

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Ying Shan, H. S. Sawhney, B. Matei, and R. Kumar. "Shapeme histogram projection and matching for partial object recognition." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 4 (April 2006): 568–77. http://dx.doi.org/10.1109/tpami.2006.83.

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Fan, Huijie, Yang Cong, and Yandong Tang. "Object detection based on scale-invariant partial shape matching." Machine Vision and Applications 26, no. 6 (June 27, 2015): 711–21. http://dx.doi.org/10.1007/s00138-015-0693-y.

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Arhid, K., F. R. Zakani, M. Bouksim, B. Sirbal, M. Aboulfatah, and T. Gad. "A novel approach for partial shape matching and similarity based on data envelopment analysis." Computer Optics 43, no. 2 (April 2019): 316–23. http://dx.doi.org/10.18287/2412-6179-2019-43-2-316-323.

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Due to the growing number of 3D objects in digital libraries, the task of searching and browsing models in an extensive 3D database has been the focus of considerable research in the area. In the last decade, several approaches to retrieve 3D models based on shape similarity have been proposed. The majority of the existing methods addresses the problem of similarity between objects as a global matching problem. Consequently, most of these techniques do not support a part of the object as a query, in addition to their poor performance for classes with globally non-similar shape models and also for articulated objects. The partial matching technique seems to be a suitable solution to these problems. In this paper, we address the problem of shape matching and retrieval. We propose a new approach based on partial matching in which each 3D object is segmented into its constituent parts, and shape descriptors are computed from these elements to compare similarities. Several experiments investigated that our technique enables fast computing for content-based 3D shape retrieval and significantly improves the results of our method based on Data Envelopment Analysis descriptor for global matching.
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BHANDARKAR, SUCHENDRA M. "A SURFACE FEATURE ATTRIBUTED HYPERGRAPH REPRESENTATION FOR 3-D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 869–909. http://dx.doi.org/10.1142/s0218001495000365.

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A surface feature hypergraph (SFAHG) representation is proposed for the recognition and localization of three-dimensional objects. The hypergraph representation is shown to be viewpoint independent thus resulting in substantial savings in terms of memory for the object model database. The resulting hypergraph matching algorithm integrates both, relational and the rigid pose constraint in a consistent unified manner. The matching algorithm is also shown to have a polynomial order of complexity even in multiple-object scenes with instances of objects partially occluding each other. An algorithm for incrementally constructing the hypergraph representation of an object model from range images of the object taken from different viewpoints is also presented. The hypergraph matching and the hypergraph construction algorithms are shown to be capable of correcting errors in the initial segmentation of the range image. The hypergraph construction algorithm and the matching algorithm are tested on range images of scenes containing multiple three-dimensional objects with partial occlusion.
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Eckes, Christian, Jochen Triesch, and Christoph von der Malsburg. "Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images." Neural Computation 18, no. 6 (June 2006): 1441–71. http://dx.doi.org/10.1162/neco.2006.18.6.1441.

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We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.
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Wang, Hong Shen, Lin Zhang, and Yong Gui Zhang. "Partial Matching of 3D CAD Models with Attributed Graph." Applied Mechanics and Materials 528 (February 2014): 302–9. http://dx.doi.org/10.4028/www.scientific.net/amm.528.302.

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This paper presents an algorithm for matching similar sub-parts of 3D CAD models. The matching process is based on the attributed graphs. Each model in target object database is represented by attributed graph. The sub-part, which is to be retrieved, is selected interactively by users from a 3D CAD model described by boundary representation (B-Rep) and its attributed graph is automatically generated. For each target CAD model in the database, its surfaces which are similar to those surfaces of the query sub-part are identified by means of the attributes. Then, other irrelevant surfaces of the target object are deleted from the model’s attributed graph. Thus, the parts that are possibly similar to the query sub-part are separated from the target model. A method for calculating the similarity coefficient between the separated part and the sub-part, which uses an optimal matching algorithm for bi-graphs, is proposed. Experimental results show that this algorithm can efficiently achieve partial retrieval.
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Zulkoffli, Z., and Elmi Abu Bakar. "Partial pose estimation of 3D rigid object system using outer box method." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 790. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp790-798.

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<p><span>This article introduces a novel approach for identify partial pose estimation using template matching method. The algorithms performs 2D correlation matching on tested image to CAD database by using regional shape representation in order to get the similar object pose in CAD database. The descriptor named outer box method, it is useful for rescale or aligning object size in both different images of tested image and CAD database image and also provide interest point for segmentation in image registration stage. The proposed algorithm were experimentally shown to be robust to apply on scale changes, various complex shape, unstructured CAD database and mixed CAD model database. Last part, the identified pose and its retrieved pose angle was calculated and achieved high accuracyin range ±0.388˚ to ±1.471˚.</span></p>
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CHAUDHURY, SANTANU, S. SUBRAMANIAN, and GUTURU PARTHASARATHY. "RECOGNITION OF PARTIAL PLANAR SHAPES IN LIMITED MEMORY ENVIRONMENTS." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 04 (December 1990): 603–28. http://dx.doi.org/10.1142/s0218001490000344.

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Industrial vision systems should be capable of recognising noisy objects, partially occluded objects and randomly located and/or oriented objects. This paper considers the problem of recognition of partially occluded planar shapes using contour segment-based features. None of the techniques suggested in the literature for solving the above problem guarantee reliable results for problem instances which require memory in excess of what is available. In this paper, a heuristic search-based recognition algorithm is presented, which guarantees reliable recognition results even when memory is limited. This algorithm identifies an object, the maximum portion of whose contour is visible in a conglomerate of objects. For increasing efficiency of the method, a two-stage recognition scheme has been designed. In the first phase, a relevant subset of the known model shapes is chosen and in the second stage, matching between the unknown shape and elements of the relevant subset is attempted using the above approach. The technique is general in the sense that it can be used with any kind of contour features. To evaluate the efficiency of the method, experimentation was carried out using polygonal approximations of the object contours. Results are cited for establishing the effectiveness of the approach.
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WU, YOUFU, and MO DAI. "DETECTION AND ANALYSIS OF MOVING OBJECTS FOR VIDEO SURVEILLANCE." International Journal of Information Acquisition 02, no. 03 (September 2005): 227–39. http://dx.doi.org/10.1142/s0219878905000623.

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In this paper, we address the problem of detection and analysis of moving objects in a video stream obtained by a fixed camera. To detect the moving objects, the tradition method is to create a fixed image first, which includes all the motionless parts of the scene, known as the background model. The difficulty of this approach lies mainly in two aspects: The first relates to the fact that a slow moving object can leave a visible trace in background model. The latter comes from the variation of illumination in the course of time so it cannot obtain a reasonable background model. To overcome these difficulties, we propose a multiple background model. At the exit of the detection of moving objects, the tracking (matching) of a moving object extracted in the successive images is necessary to analyze its behavior. After the matching of mobile objects, a series of analysis methods are presented. The proposed tracking and analysis methods allow dealing with partial occlusions, stopping and going motion, moving directions, crossing of moving object in very challenging situations. The experiment and comparison results are reported for different real sequences, which show better performance of our methods.
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Elboushaki, Abdessamad, Rachida Hannane, Karim Afdel, and Lahcen Koutti. "A robust approach for object matching and classification using Partial Dominant Orientation Descriptor." Pattern Recognition 64 (April 2017): 168–86. http://dx.doi.org/10.1016/j.patcog.2016.11.004.

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Dissertations / Theses on the topic "Partial object matching"

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Van, Wyk Frans-Pieter. "Simutaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33323.

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Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images.
Dissertation (MEng)--University of Pretoria, 2013.
gm2014
Electrical, Electronic and Computer Engineering
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Gao, Q. H., Tao Ruan Wan, W. Tang, and L. Chen. "Object registration in semi-cluttered and partial-occluded scenes for augmented reality." 2018. http://hdl.handle.net/10454/16671.

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Yes
This paper proposes a stable and accurate object registration pipeline for markerless augmented reality applications. We present two novel algorithms for object recognition and matching to improve the registration accuracy from model to scene transformation via point cloud fusion. Whilst the first algorithm effectively deals with simple scenes with few object occlusions, the second algorithm handles cluttered scenes with partial occlusions for robust real-time object recognition and matching. The computational framework includes a locally supported Gaussian weight function to enable repeatable detection of 3D descriptors. We apply a bilateral filtering and outlier removal to preserve edges of point cloud and remove some interference points in order to increase matching accuracy. Extensive experiments have been carried to compare the proposed algorithms with four most used methods. Results show improved performance of the algorithms in terms of computational speed, camera tracking and object matching errors in semi-cluttered and partial-occluded scenes.
Shanxi Natural Science and Technology Foundation of China, grant number 2016JZ026 and grant number 2016KW-043).
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Van, Wyk Frans Pieter. "Simultaneous real-time object recognition and pose estimation for artificial systems operating in dynamic environments." Diss., 2013. http://hdl.handle.net/2263/41506.

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Recent advances in technology have increased awareness of the necessity for automated systems in people’s everyday lives. Artificial systems are more frequently being introduced into environments previously thought to be too perilous for humans to operate in. Some robots can be used to extract potentially hazardous materials from sites inaccessible to humans, while others are being developed to aid humans with laborious tasks. A crucial aspect of all artificial systems is the manner in which they interact with their immediate surroundings. Developing such a deceivingly simply aspect has proven to be significantly challenging, as it not only entails the methods through which the system perceives its environment, but also its ability to perform critical tasks. These undertakings often involve the coordination of numerous subsystems, each performing its own complex duty. To complicate matters further, it is nowadays becoming increasingly important for these artificial systems to be able to perform their tasks in real-time. The task of object recognition is typically described as the process of retrieving the object in a database that is most similar to an unknown, or query, object. Pose estimation, on the other hand, involves estimating the position and orientation of an object in three-dimensional space, as seen from an observer’s viewpoint. These two tasks are regarded as vital to many computer vision techniques and and regularly serve as input to more complex perception algorithms. An approach is presented which regards the object recognition and pose estimation procedures as mutually dependent. The core idea is that dissimilar objects might appear similar when observed from certain viewpoints. A feature-based conceptualisation, which makes use of a database, is implemented and used to perform simultaneous object recognition and pose estimation. The design incorporates data compression techniques, originally suggested by the image-processing community, to facilitate fast processing of large databases. System performance is quantified primarily on object recognition, pose estimation and execution time characteristics. These aspects are investigated under ideal conditions by exploiting three-dimensional models of relevant objects. The performance of the system is also analysed for practical scenarios by acquiring input data from a structured light implementation, which resembles that obtained from many commercial range scanners. Practical experiments indicate that the system was capable of performing simultaneous object recognition and pose estimation in approximately 230 ms once a novel object has been sensed. An average object recognition accuracy of approximately 73% was achieved. The pose estimation results were reasonable but prompted further research. The results are comparable to what has been achieved using other suggested approaches such as Viewpoint Feature Histograms and Spin Images.
Dissertation (MEng)--University of Pretoria, 2013.
gm2014
Electrical, Electronic and Computer Engineering
unrestricted
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4

XU, YUAN-ZHONG, and 許元中. "A partial-matching method to recognize 2-D overlapped objects." Thesis, 1986. http://ndltd.ncl.edu.tw/handle/97125547119269204914.

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Xu, Yuan-Zhong, and 許元中. "A partial-matching method to recognize 2-D overlapped objects." Thesis, 1986. http://ndltd.ncl.edu.tw/handle/60122975196798538811.

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Chen, Ting-Yi, and 陳亭亦. "Object tracking based on template matching and particle filter." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/33848967779158853982.

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碩士
國立東華大學
資訊工程學系
103
Visual tracking plays an important role in many applications such as intelligent video surveillance, human-computer interaction, traffic monitoring, and so on. In re-cent years, even though many approaches have been successfully made on this topic, it is still a very challenging problem such as large appearance changes, illumination changes, occlusion, real-time, scale variation, scene change, cluttered background, and similar appearance. In this thesis, we propose an object tracking using template matching and particle filter to solve some issues in visual tracking. This method contains three major parts: feature extraction, template matching and particles weighting. Except for some chal-lenging sequences such as sudden appearance change or illumination change of object, the object can be successfully tracked by template matching. To compensate for tem-plate matching, particle filter with Speeded Up Robust Features (SURF) is used in the failed tracking. Experimental results with challenging video sequences are presented to demon-strate the effectiveness and robustness of the proposed method. The comparative per-formance of the proposed method with other existing techniques is shown as well.
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Yang, Hui-Guo, and 楊惠國. "Shape Matching of 3D Objects with Partial Surface Using Potential Fields." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/92474832915290668676.

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Books on the topic "Partial object matching"

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Schwartz, Jacob T. Identification of partially obscured objects in two dimensions by matching of noisy 'characteristic curves,'. New York: Courant Institute of Mathematical Sciences, New York University, 1985.

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Book chapters on the topic "Partial object matching"

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Shan, Y., H. S. Sawhney, B. Matei, and R. Kumar. "Partial Object Matching with Shapeme Histograms." In Lecture Notes in Computer Science, 442–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_35.

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Sfikas, Konstantinos, Ioannis Pratikakis, Anestis Koutsoudis, Michalis Savelonas, and Theoharis Theoharis. "3D Object Partial Matching Using Panoramic Views." In New Trends in Image Analysis and Processing – ICIAP 2013, 169–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41190-8_19.

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Andreevskaia, Alina, Zhuoyan Li, and Sabine Bergler. "Partial Predicate Argument Structure Matching for Entailment Determination." In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, 332–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11736790_19.

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Jia, Jin, and Keiichi Abe. "Learning and recognizing 3D objects by using partial planar curve matching method." In Computer Vision — ACCV'98, 450–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63930-6_153.

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Ahmed, Ejaz, Nik Bessis, Peter Norrington, and Yong Yue. "Managing Inconsistencies in Data Grid Environments." In Evolving Developments in Grid and Cloud Computing, 303–16. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0056-0.ch022.

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Much work has been done in the area of data access and integration using various data mapping, matching, and loading techniques. One of the main concerns when integrating data from heterogeneous data sources is data redundancy. The concern is mainly due to the different business contexts and purposes from which the data systems were originally built. A common process for accessing data from integrated databases involves the use of each data source’s own catalogue or metadata schema. In this article, the authors take the view that there is a greater chance of data inconsistencies, such as data redundancies when integrating them within a grid environment as compared to traditional distributed paradigms. The importance of improving the data search and matching process is briefly discussed, and a partial service oriented generic strategy is adopted to consolidate distinct catalogue schemas of federated databases to access information seamlessly. To this end, a proposed matching strategy between structure objects and data values across federated databases in a grid environment is presented.
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Popa, Rustem. "Melanocytic Lesions Screening through Particle Swarm Optimization." In Handbook of Research on Novel Soft Computing Intelligent Algorithms, 355–84. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4450-2.ch012.

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Early detection of malignant melanoma, which is the most dangerous skin cancer, significantly improves the chances of curing it. For this reason, dermatologists are looking for new methods for the examination of suspicious lesions that changes their shape over time. The author investigates in this chapter some algorithms which may be used for automated diagnosis of skin lesions. First algorithm performs the image segmentation by edge detection, which plays an important role in identifying borders of the lesion. Next algorithm uses the Particle Swarm Optimization (PSO) paradigm for recognizing the images of the same melanocytic nevus taken at different moments of time. The idea is that a novel view of an object can be recognized by simply matching it to combinations of known views of the same object. The main difficulty in implementing this idea is determining the parameters of the combination of views. The space of parameters is very large and we propose a PSO approach to search this space efficiently. The effectiveness of this approach is shown on a set of real images captured with a camera under different angles of view.
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Conference papers on the topic "Partial object matching"

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Chiew, K. L., and Y. C. Wang. "Shape feature representation in partial object matching." In Informatics (ICOCI). IEEE, 2006. http://dx.doi.org/10.1109/icoci.2006.5276471.

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Elboushaki, Abdessamad, Rachida Hannane, Karim Afdel, and Lahcen Koutti. "Efficient object matching using partial dominant orientation descriptor." In 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA). IEEE, 2015. http://dx.doi.org/10.1109/icta.2015.7426907.

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Bloch-Boulanger, Isabelle, Henri Maitre, and Francis J. M. Schmitt. "Mathematical morphology for 3-D object segmentation and partial matching." In Lausanne - DL tentative, edited by Murat Kunt. SPIE, 1990. http://dx.doi.org/10.1117/12.24232.

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Husain, Mustafa, Eli Saber, Vladimir Misic, and Stephen P. Joralemon. "Dynamic Object Tracking by Partial Shape Matching for Video Surveillance Applications." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.312947.

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Straub, Jeremy. "Detection of obscured and partially covered objects using partial network matching and an image feature network-based object recognition algorithm." In SPIE Defense + Security, edited by Steven S. Bishop and Jason C. Isaacs. SPIE, 2014. http://dx.doi.org/10.1117/12.2050171.

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Ma, Tianyang, and Longin Jan Latecki. "From partial shape matching through local deformation to robust global shape similarity for object detection." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995591.

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Toivanen, Miika, and Jouko Lampinen. "Incremental Object Matching with Bayesian Methods and Particle Filters." In 2009 Digital Image Computing: Techniques and Applications. IEEE, 2009. http://dx.doi.org/10.1109/dicta.2009.26.

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Wu, Tao, Xiaoqing Ding, Shengjin Wang, and Kongqiao Wang. "Video object tracking using improved chamfer matching and condensation particle filter." In Electronic Imaging 2008, edited by Kurt S. Niel and David Fofi. SPIE, 2008. http://dx.doi.org/10.1117/12.766388.

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Yu, Qiuze, Shunxin Min, Bo Pang, and Yan Zhang. "Simultaneously Multiple-object Pattern Matching based on Multi-swarms Particle Swarm Optimization." In 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2017. http://dx.doi.org/10.1109/icivc.2017.7984531.

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Xu, Yaowu, Eli S. Saber, and A. Murat Tekalp. "Shape matching of partially occluded objects for image retrieval using hierarchical content description." In Photonics West 2001 - Electronic Imaging, edited by Minerva M. Yeung, Chung-Sheng Li, and Rainer W. Lienhart. SPIE, 2001. http://dx.doi.org/10.1117/12.410977.

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