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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

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Exact Inference in Multi-label CRFs with Higher Order Cliques

Exact Inference in Multi-label CRFs with Higher Order Clique...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Srikumar Ramalingam Pushmeet Kohli Karteek Alahari Philip H. S. Torr Oxford Brookes University UK Microsoft Research Limited Cambridge UK

this paper addresses the problem of exactly inferring the maximum a posteriori solutions of discrete multi-label MRFs or CRFs with higher order cliques. We present a framework to transform special classes of multi-label higher order functions to submodular second order boolean functions (referred to as (F{sub}s){sup}2), which can be minimized exactly using graph cuts and we characterize those classes. the basic idea is to use two or more boolean variables to encode the states of a single multi-label variable. there are many ways in which this can be done and much interesting research lies in finding ways which are optimal or minimal in some sense. We study the space of possible encodings and find the ones that can transform the most general class of functions to (F{sub}s){sup}2. Our main contributions are two-fold. First, we extend the subclass of submodular energy functions that can be minimized exactly using graph cuts. Second, we show how higher order potentials can be used to improve single view 3D reconstruction results. We believe that our work on exact minimization of higher order energy functions will lead to similar improvements in solutions of other labelling problems.

关键词： Labeling Boolean functions computer vision Polynomials Cost function Encoding Minimization methods Discrete transforms Image reconstruction Image segmentation

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Finding Trails

Finding Trails

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Scott Morris Kobus Barnard Computer Science Department University of Arizona Tucson USA

We present a statistical learning approach for finding recreational trails in aerial images. While the problem of recognizing relatively straight and well defined roadways in digital images has been well studied in the literature, the more difficult problem of extracting trails has received no attention. However, trails and rough roads are less likely to be adequately mapped, and change more rapidly over time. Automated tools for finding trails will be useful to cartographers, recreational users and governments. In addition, the methods developed here are applicable to the more general problem of finding linear structure. Our approach combines local estimates for image pixel trail probabilities with the global constraint that such pixels must link together to form a path. For the local part, we present results using three classification techniques. To construct a global solution (a trail) from these probabilities, we propose a global cost function that includes both global probability and path length. We show that the addition of a length term significantly improves trail finding ability. However, computing the optimal trail becomes intractable as known dynamic programming methods do not apply. thus we describe a new splitting heuristic based on Dijkstra's algorithm. We then further improve upon the results with a trail sampling scheme. We test our approach on 500 challenging images along the 2500 mile continental divide mountain bike trail, where assumptions prevalent in the road literature are violated.

关键词： Roads Pixel Statistical learning Image recognition Digital images Government Cost function Dynamic programming Image sampling Testing

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3D Face Tracking and Expression Inference from a 2D Sequence Using Manifold Learning

3D Face Tracking and Expression Inference from a 2D Sequence...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Wei-Kai Liao Gerard Medioni Computer Science Department Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

We propose a person-dependent, manifold-based approach for modeling and tracking rigid and nonrigid 3D facial deformations from a monocular video sequence. the rigid and nonrigid motions are analyzed simultaneously in 3D, by automatically fitting and tracking a set of landmarks. We do not represent all nonrigid facial deformations as a simple complex manifold, but instead decompose them on a basis of eight 1D manifolds. Each 1D manifold is learned offline from sequences of labeled expressions, such as smile, surprise, etc. Any expression is then a linear combination of values along these 8 axes, with coefficient representing the level of activation. We experimentally verify that expressions can indeed be represented this way, and that individual manifolds are indeed 1D. the manifold dimensionality estimation, manifold learning, and manifold traversal operation are all implemented in the N-D Tensor Voting framework. Using simple local operations, this framework gives an estimate of the tangent and normal spaces at every sample, and provides excellent robustness to noise and outliers. the output of our system, besides the tracked landmarks in 3D, is a labeled annotation of the expression. We demonstrate results on a number of challenging sequences.

关键词： Deformable models Active appearance model Active shape model Face detection Intelligent robots Facial animation Humans Principal component analysis computer science Robotics and automation

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Image Selection For Improved Multi-View Stereo

Image Selection For Improved Multi-View Stereo

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Alexander Hornung Boyi Zeng Leif Kobbelt RWTH Aachen University Germany

the Middlebury Multi-View Stereo evaluation [18] clearly shows that the quality and speed of most multi-view stereo algorithms depends significantly on the number and selection of input images. In general, not all input images contribute equally to the quality of the output model, since several images may often contain similar and hence overly redundant visual information. this leads to unnecessarily increased processing times. On the other hand, a certain degree of redundancy can help to improve the reconstruction in more "difficult" regions of a model. In this paper we propose an image selection scheme for multi-view stereo which results in improved reconstruction quality compared to uniformly distributed views. Our method is tuned towards the typical requirements of current multi-view stereo algorithms, and is based on the idea of incrementally selecting images so that the overall coverage of a simultaneously generated proxy is guaranteed without adding too much redundant information. Critical regions such as cavities are detected by an estimate of the local photo-consistency and are improved by adding additional views. Our method is highly efficient, since most computations can be out-sourced to the GPU. We evaluate our method with four different methods participating in the Middlebury benchmark and show that in each case reconstructions based on our selected images yield an improved output quality while at the same time reducing the processing time considerably.

关键词： Image reconstruction Surface reconstruction Stereo image processing Pipelines Humans Image generation Robot vision systems Cameras Iterative algorithms Image segmentation

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Accurate Polyp Segmentation for 3D CT Colongraphy Using Multi-Staged Probabilistic Binary Learning and Compositional Model

Accurate Polyp Segmentation for 3D CT Colongraphy Using Mult...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Le Lu Adrian Barbu Matthias Wolf Jianming Liang Marcos Salganicoff Dorin Comaniciu Computer Aided Diagnosis Group Siemens Medical Solutions Inc. Malvern PA USA Department Siemens Corporate Research Integrated Data Systems Princeton NJ USA

Accurate and automatic colonic polyp segmentation and measurement in Computed Tomography (CT) has significant importance for 3D polyp detection, classification, and more generally computer aided diagnosis of colon cancers. In this paper, we propose a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from their surrounding tissues in CT. Our system integrates low-, and mid-level information for discriminative learning under local polar coordinates which align on the 3D colon surface around detected polyp. More importantly, our supervised learning system has flexible modeling capacity, which offers a principled means of encoding semantic, clinical expert annotations of colonic polyp tissue identification and segmentation. the learning generality to unseen data is bounded by boosting [12, 11] and stacked generality [14]. Extensive experimental results on polyp segmentation performance evaluation and robustness testing with disturbances (using both training data and unseen data) are provided to validate our presented approach. the reliability of polyp segmentation and measurement has been largely increased to 98.2% (ie. errors ≤ 3mm), compared with other state of art work [4, 15] of about 75% ～ 80%.

关键词： Colonic polyps Computed tomography Colonography Colon Cancer detection Supervised learning Encoding Boosting Robustness Testing

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Computing Minimal Deformations: Application to Construction of Statistical Shape Models

Computing Minimal Deformations: Application to Construction ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Darko Zikic Michael Sass Hansen Ben Glocker Ali Khamene Rasmus Larsen Nassir Navab Computer Aided Medical Procedures (CAMP) Technische Universität München Germany Informatics and Mathematical Modeling (IMM) Technical University of Denmark Denmark Imaging and Visualization Department Siemens AG Corporate Research and Development Princeton USA

Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear registration method. For the further processing of the results, it is mostly assumed that this preregistration step completely removes the respective linear transformation. However, we show that in deformable settings, this is not the case. As a consequence, a significant linear component is still existent in the deformation computed by the nonlinear registration algorithm. For construction of statistical shape models (SSM) from deformations, this is an unwanted property: SSMs should not contain similarity transformations, since these do not capture information about shape. We propose a method which performs an a posteriori extraction of a similarity transformation from a given nonlinear deformation field, and we use the processed fields as input for SSM construction. For computation of minimal displacements, a closed-form solution minimizing the squared Euclidean norm of the displacement field subject to similarity parameters is used. Experiments on real inter-subject data and on a synthetic example show that the theoretically justified removal of the similarity component by the proposed method has a large influence on the shape model and significantly improves the results.

关键词： computer applications Shape Deformable models Mathematical model Visualization Data mining Application software Biomedical imaging Biomedical informatics thyristors

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Matching Images Under Unstable Segmentations

Matching Images Under Unstable Segmentations

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Varsha Hedau Himanshu Arora Narendra Ahuja ECE Department University of Illinois Urbana IL USA ObjectVideo Inc. Reston VA USA

Region based features are getting popular due to their higher descriptive power relative to other features. However, real world images exhibit changes in image segments capturing the same scene part taken at different time, under different lighting conditions, from different viewpoints, etc. Segmentation algorithms reflect these changes, and thus segmentations exhibit poor repeatability. In this paper we address the problem of matching regions of similar objects under unstable segmentations. Merging and splitting of regions makes it difficult to find such correspondences using one-to-one matching algorithms. We present partial region matching as a solution to this problem. We assume that the high contrast, dominant contours of an object are fairly repeatable, and use them to compute partial matching cost (PMC) between regions. Region correspondences are obtained under region adjacency constraints encoded by Region Adjacency Graph (RAG). We integrate PMC in a many-to-one label assignment framework for matching RAGs, and solve it using belief propagation. We show that our algorithm can match images of similar objects across unstable image segmentations. We also compare the performance of our algorithm with that of the standard one-to-one matching algorithm on three motion sequences. We conclude that our partial region matching approach is robust under segmentation irrepeatabilities.

关键词： Image segmentation Costs Corporate acquisitions Layout Shape Encoding Merging Belief propagation Robustness Object recognition

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Object Tracking and Detection after Occlusion via Numerical Hybrid Local and Global Mode-seeking

Object Tracking and Detection after Occlusion via Numerical ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.5

作者： Zhaozheng Yin Robert T. Collins Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets.

关键词： Object detection Target tracking Rotation measurement Extraterrestrial measurements Parameter estimation Optimization methods Simulated annealing Monte Carlo methods Videos Cameras

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Globally Optimal Surface Segmentation Using Regional Properties of Segmented Objects

Globally Optimal Surface Segmentation Using Regional Propert...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Xin Dou Andreas Wahle Xiaodong Wu Milan Sonka Department of Electrical and Computer Engineering University of Iowa Iowa IA USA

Efficient segmentation of globally optimal surfaces in volumetric images is a central problem in many medical image analysis applications. Intra-class variance has been successfully utilized, for instance, in the Chan-Vese model especially for images without prominent edges. In this paper, we study the optimization problem of detecting a region (volume) between two coupled smooth surfaces by minimizing the intra-class variance using an efficient polynomial-time algorithm. Our algorithm is based on the shape probing technique in computational geometry and computes a sequence of minimum-cost closed sets in a derived parametric graph. the method has been validated on computer-synthetic volumetric images and in X-ray CT-scanned datasets of plexiglas tubes of known sizes. Its applicability to clinical data sets was demonstrated in human CT image data. the achieved results were highly accurate with mean signed surface positioning errors of the inner and outer walls of the tubes of +0.013mm and 0.012mm, respectively, given a voxel size of 0.39 × 0.39 × 0.6mm{sup}3. Comparing with the original Chan-Vese method [8], our algorithm expressed higher robustness. With its polynomial-time efficiency, our algorithm is ready to be extended to higher-dimensional image segmentation. In addition, the developed technique is of its own interest. We expect that it can shed some light on solving other important optimization problems arising in computer vision. To the best of our knowledge, the shape probing technique is for the first time introduced into the field of computer vision.

关键词： Image segmentation Image edge detection Polynomials Shape computer vision Biomedical imaging Image sequence analysis Computational geometry X-ray imaging Humans

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Automatic registration of aerial imagery with untextured 3D LiDAR models

Automatic registration of aerial imagery with untextured 3D ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Min Ding Kristian Lyngbaek Avideh Zakhor Electrical Engineering and Computer Science Department University of California Berkeley USA

A fast 3D model reconstruction methodology is desirable in many applications such as urban planning, training, and simulations. In this paper, we develop an automated algorithm for texture mapping oblique aerial images onto a 3D model generated from airborne Light Detection and Ranging (LiDAR) data. Our proposed system consists of two steps. In the first step, we combine vanishing points and global positioning system aided inertial system readings to roughly estimate the extrinsic parameters of a calibrated camera. In the second step, we refine the coarse estimate of the first step by applying a series of processing steps. Specifically, We extract 2D corners corresponding to orthogonal 3D structural corners as features from both images and the untextured 3D LiDAR model. the correspondence between an image and the 3D model is then performed using Hough transform and generalized M-estimator sample consensus. the resulting 2D corner matches are used in Lowe's algorithm to refine camera parameters obtained earlier. Our system achieves 91% correct pose recovery rate for 90 images over the downtown Berkeley area, and overall 61% accuracy rate for 358 images over the residential, downtown and campus portions of the city of Berkeley.

关键词： Laser radar Cameras Clouds Image reconstruction Urban planning Geometry Solid modeling Image generation Application software Computational modeling

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