@conference {13067,
	title = {Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance},
	booktitle = {Computer Vision (ICCV), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011/11//},
	pages = {161 - 168},
	abstract = {Subordinate-level categorization typically rests on establishing salient distinctions between part-level characteristics of objects, in contrast to basic-level categorization, where the presence or absence of parts is determinative. We develop an approach for subordinate categorization in vision, focusing on an avian domain due to the fine-grained structure of the category taxonomy for this domain. We explore a pose-normalized appearance model based on a volumetric poselet scheme. The variation in shape and appearance properties of these parts across a taxonomy provides the cues needed for subordinate categorization. Training pose detectors requires a relatively large amount of training data per category when done from scratch; using a subordinate-level approach, we exploit a pose classifier trained at the basic-level, and extract part appearance and shape information to build subordinate-level models. Our model associates the underlying image pattern parameters used for detection with corresponding volumetric part location, scale and orientation parameters. These parameters implicitly define a mapping from the image pixels into a pose-normalized appearance space, removing view and pose dependencies, facilitating fine-grained categorization from relatively few training examples.},
	keywords = {appearance, Birdlets;category, categorization;subordinate-level, detection;pose, detectors;pose-normalized, distinctions;shape, estimation;, extraction;pose, extraction;subordinate-level, information, model;salient, models;volumetric, pixels;part, poselet, primitives;computer, resolution;information, retrieval;object, scheme;volumetric, taxonomy;computer, vision;image},
	doi = {10.1109/ICCV.2011.6126238},
	author = {Farrell,R. and Oza,O. and Zhang,Ning and Morariu,V.I. and Darrell,T. and Davis, Larry S.}
}
@conference {13078,
	title = {Kernel PLS regression for robust monocular pose estimation},
	booktitle = {Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {24 - 30},
	abstract = {We evaluate the robustness of five regression techniques for monocular 3D pose estimation. While most of the discriminative pose estimation methods focus on overcoming the fundamental problem of insufficient training data, we are interested in characterizing performance improvement for increasingly large training sets. Commercially available rendering software allows us to efficiently generate large numbers of realistic images of poses from diverse actions. Inspired by recent work in human detection, we apply PLS and kPLS regression to pose estimation. We observe that kPLS regression incrementally approximates GP regression using the strongest nonlinear correlations between image features and pose. This provides robustness, and our experiments show kPLS regression is more robust than two GP-based state-of-the-art methods for pose estimation.},
	keywords = {(computer, 3D, analysis;rendering, correlations;projection, detection;monocular, detection;pose, estimation;Gaussian, estimation;nonlinear, estimation;regression, GP, graphics);, images;rendering, latent, monocular, PLS, pose, process;Kernel, processes;object, regression;Gaussian, regression;human, software;robust, structures;realistic, to},
	doi = {10.1109/CVPRW.2011.5981750},
	author = {Dondera,R. and Davis, Larry S.}
}
@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.}
}
@conference {13163,
	title = {Constraint Integration for Multiview Pose Estimation of Humans with Self-Occlusions},
	booktitle = {3D Data Processing, Visualization, and Transmission, Third International Symposium on},
	year = {2006},
	month = {2006/06//},
	pages = {900 - 907},
	abstract = {Detection of articulated objects such as humans is an important task in computer vision. We present a system that incorporates a variety of constraints in a unified multi- view framework to automatically detect humans in possibly crowded scenes. These constraints include the kinematic constraints, the occlusion of one part by another and the high correlation between the appearance of parts such as the two arms. The graphical structure (non-tree) obtained is optimized in a nonparametric belief propagation framework using prior based search.},
	keywords = {articulated, belief, constraint;multiview, detection;computer, detection;pose, estimation;, estimation;nonparametric, framework;self-occlusion;computer, graphics;computer, method;graphical, methods;object, object, pose, propagation, structure;kinematic, vision;correlation},
	doi = {10.1109/3DPVT.2006.45},
	author = {Gupta,A. and Mittal,A. and Davis, Larry S.}
}
