@article {12477,
	title = {An Efficient and Robust Algorithm for Shape Indexing and Retrieval},
	journal = {Multimedia, IEEE Transactions on},
	volume = {12},
	year = {2010},
	month = {2010/08//},
	pages = {372 - 385},
	abstract = {Many shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations, rigid transformations, etc. The features characterize pairwise geometric relationships between interest points on the shape. The fact that each shape is represented using a number of distributed features instead of a single global feature that captures the shape in its entirety provides robustness to the approach. Shapes in the database are ordered according to their similarity with the query shape and similar shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Depending on the application, the approach can be used directly for matching or as a first step for obtaining a short list of candidate shapes for more rigorous matching. We show that the features proposed to perform shape indexing can be used to perform the rigorous matching as well, to further improve the retrieval performance.},
	keywords = {activity, algorithm;shape, classification;human, databases;robust, estimation;large, indexing;shape, MATCHING, matching;image, methods;shape, pose, recognition;, retrieval;image, retrieval;indexing;shape},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2010.2050735},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@conference {12486,
	title = {Pose estimation in heavy clutter using a multi-flash camera},
	booktitle = {Robotics and Automation (ICRA), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/05//},
	pages = {2028 - 2035},
	abstract = {We propose a novel solution to object detection, localization and pose estimation with applications in robot vision. The proposed method is especially applicable when the objects of interest may not be richly textured and are immersed in heavy clutter. We show that a multi-flash camera (MFC) provides accurate separation of depth edges and texture edges in such scenes. Then, we reformulate the problem, as one of finding matches between the depth edges obtained in one or more MFC images to the rendered depth edges that are computed offline using 3D CAD model of the objects. In order to facilitate accurate matching of these binary depth edge maps, we introduce a novel cost function that respects both the position and the local orientation of each edge pixel. This cost function is significantly superior to traditional Chamfer cost and leads to accurate matching even in heavily cluttered scenes where traditional methods are unreliable. We present a sub-linear time algorithm to compute the cost function using techniques from 3D distance transforms and integral images. Finally, we also propose a multi-view based pose-refinement algorithm to improve the estimated pose. We implemented the algorithm on an industrial robot arm and obtained location and angular estimation accuracy of the order of 1 mm and 2 $\#$x00B0; respectively for a variety of parts with minimal texture.},
	keywords = {3D, algorithm;object, based, camera;multiview, depth, detection;object, detection;pose, distance, edge, edges;cameras;image, edges;integral, estimation;binary, estimation;multiflash, estimation;robot, function;depth, images;location, localization;pose, maps, matching;cost, matching;image, pose-refinement, texture;object, transforms;angular, vision;texture, vision;transforms;},
	doi = {10.1109/ROBOT.2010.5509897},
	author = {Ming-Yu Liu and Tuzel, O. and Veeraraghavan,A. and Chellapa, Rama and Agrawal,A. and Okuda, H.}
}
@article {12524,
	title = {Moving Object Verification in Airborne Video Sequences},
	journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
	volume = {19},
	year = {2009},
	month = {2009/01//},
	pages = {77 - 89},
	abstract = {This paper presents an end-to-end system for moving object verification in airborne video sequences. Using a sample selection module, the system first selects frames from a short sequence and stores them in an exemplar database. To handle appearance change due to potentially large aspect angle variations, a homography-based view synthesis method is then used to generate a novel view of each image in the exemplar database at the same pose as the testing object in each frame of a testing video segment. A rotationally invariant color matcher and a spatial-feature matcher based on distance transforms are combined using a weighted average rule to compare the novel view and the testing object. After looping over all testing frames, the set of match scores is passed to a temporal analysis module to examine the behavior of the testing object, and calculate a final likelihood. Very good verification performance is achieved over thousands of trials for both color and infrared video sequences using the proposed system.},
	keywords = {airborne, database;homography-based, databases;, matcher;color, matcher;image, matching;image, method;infrared, object, sequences;color, sequences;distance, sequences;moving, sequences;video, synthesis, system;exemplar, transforms;end-to-end, verification;spatial-feature, video, view},
	isbn = {1051-8215},
	doi = {10.1109/TCSVT.2008.2009243},
	author = {Yue,Zhanfeng and Guarino, D. and Chellapa, Rama}
}
@conference {13147,
	title = {Classifying Computer Generated Charts},
	booktitle = {Content-Based Multimedia Indexing, 2007. CBMI {\textquoteright}07. International Workshop on},
	year = {2007},
	month = {2007/06//},
	pages = {85 - 92},
	abstract = {We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.},
	keywords = {algorithm;scale, analysis;visual, classification;image, database;image, databases;, feature, Internet;bar-chart;curve-plot;image, invariant, match, matching;image, relationship;surface-plot;Internet;image, representation;image, segmentation;pie-chart;pyramid, segmentation;statistical, transform;scatter-plot;spatial},
	doi = {10.1109/CBMI.2007.385396},
	author = {Prasad,V. S.N and Siddiquie,B. and Golbeck,J. and Davis, Larry S.}
}
@conference {12588,
	title = {Efficient Indexing For Articulation Invariant Shape Matching And Retrieval},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Most shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations and rigid transformations. The features characterize pairwise geometric relationships between interest points on the shape, thereby providing robustness to the approach. Shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Even for a moderate size database of 1000 shapes, the retrieval process is several times faster than most techniques with similar performance. Extensive experimental results are presented to illustrate the advantages of our approach as compared to the best in the field.},
	keywords = {alignment;image, articulation, geometric, invariant, matching;image, matching;indexing;invariant, relationships;shape-wise, retrieval;indexing;, retrieval;pairwise, SHAPE},
	doi = {10.1109/CVPR.2007.383227},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@conference {13139,
	title = {Hierarchical Part-Template Matching for Human Detection and Segmentation},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Local part-based human detectors are capable of handling partial occlusions efficiently and modeling shape articulations flexibly, while global shape template-based human detectors are capable of detecting and segmenting human shapes simultaneously. We describe a Bayesian approach to human detection and segmentation combining local part-based and global template-based schemes. The approach relies on the key ideas of matching a part-template tree to images hierarchically to generate a reliable set of detection hypotheses and optimizing it under a Bayesian MAP framework through global likelihood re-evaluation and fine occlusion analysis. In addition to detection, our approach is able to obtain human shapes and poses simultaneously. We applied the approach to human detection and segmentation in crowded scenes with and without background subtraction. Experimental results show that our approach achieves good performance on images and video sequences with severe occlusion.},
	keywords = {analysis;global, approach;background, articulations;video, Bayesian, detection;human, detectors;hierarchical, detectors;partial, framework;Bayesian, human, likelihood, MAP, matching;human, matching;image, methods;image, occlusion, occlusions;shape, part-based, part-template, re-evaluation;global, segmentation;image, segmentation;local, sequences;, sequences;Bayes, SHAPE, subtraction;fine, template-based},
	doi = {10.1109/ICCV.2007.4408975},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {13137,
	title = {Robust Object Tracking with Regional Affine Invariant Features},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.},
	keywords = {affine, algorithm;feature, analysis;image, analysis;pixel, consistency;regional, detection;motion, detection;tracking;, extraction;image, feature, features;robust, invariant, matching;image, MOTION, object, resolution;object, tracking},
	doi = {10.1109/ICCV.2007.4408948},
	author = {Tran,Son and Davis, Larry S.}
}
@conference {12611,
	title = {Invariant Geometric Representation of 3D Point Clouds for Registration and Matching},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1209 - 1212},
	abstract = {Though implicit representations of surfaces have often been used for various computer graphics tasks like modeling and morphing of objects, it has rarely been used for registration and matching of 3D point clouds. Unlike in graphics, where the goal is precise reconstruction, we use isosurfaces to derive a smooth and approximate representation of the underlying point cloud which helps in generalization. Implicit surfaces are generated using a variational interpolation technique. Implicit function values on a set of concentric spheres around the 3D point cloud of object are used as features for matching. Geometric-invariance is achieved by decomposing implicit values based feature set into various spherical harmonics. The decomposition provides a compact representation of 3D point clouds while achieving rotation invariance},
	keywords = {3D, cloud;computer, function, geometric, graphics;geophysical, graphics;image, Interpolation, matching;image, point, processing;image, reconstruction;image, registration;image, registration;implicit, representation;interpolation;, representation;variational, signal, technique;clouds;computer, value;invariant},
	doi = {10.1109/ICIP.2006.312542},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@conference {14859,
	title = {Deformation invariant image matching},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {1466 -1473 Vol. 2 - 1466 -1473 Vol. 2},
	abstract = {We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D space, with intensity weighted relative to distance in x-y. We show that as this weight increases, geodesic distances on the embedded surface are less affected by image deformations. In the limit, distances are deformation invariant. We use geodesic sampling to get neighborhood samples for interest points, and then use a geodesic-intensity histogram (GIH) as a deformation invariant local descriptor. In addition to its invariance, the new descriptor automatically finds its support region. This means it can safely gather information from a large neighborhood to improve discriminability. Furthermore, we propose a matching method for this descriptor that is invariant to affine lighting changes. We have tested this new descriptor on interest point matching for two data sets, one with synthetic deformation and lighting change, and another with real non-affine deformations. Our method shows promising matching results compared to several other approaches},
	keywords = {deformation, deformations;nonaffine, deformations;point, descriptor;geodesic, distances;geodesic, geometry;differential, geometry;image, histogram;image, image, invariant, local, matching;computational, matching;deformation, matching;image, morphing;, sampling;geodesic-intensity},
	doi = {10.1109/ICCV.2005.67},
	author = {Ling,Haibin and Jacobs, David W.}
}
