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Fast Track Matching and Event Detection

Fast Track Matching and Event Detection

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

作者： Tao Ding Mario Sznaier Octavia I. Camps Department of Electrical Engineering Pennsylvania State University University Park PA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

this paper addresses the problems of track stitching and dynamic event detection in a sequence of frames. the input data consists of tracks, possibly fragmented due to occlusion, belonging to multiple targets. the goals are to (i) establish track identity across occlusion, and (ii) detect points where the motion of these targets undergo substantial changes. the main result of the paper is a simple, computationally inexpensive approach that achieves these goals in a unified way. Given a continuous track, the main idea is to detect changes in the dynamics by parsing it into segments according to the complexity of the model required to explain the observed data. Intuitively, changes in this complexity correspond to points where the dynamics change. Since the problem of estimating the complexity of the underlying model can be reduced to estimating the rank of a matrix constructed from the observed data, these changes can be found with a simple algorithm, computationally no more expensive that a sequence of SVDs. Proceeding along the same lines, fragmented tracks corresponding to multiple targets can be linked by searching for sets corresponding to minimal complexity joint models. As we show in the paper, this problem can be reduced to a semi-definite optimization and efficiently solved with commonly available software.

关键词： Event detection Target tracking Robustness Motion detection Particle tracking Computational complexity Application software Algorithm design and analysis Lighting Filtering

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Symmetric Multi-View Stereo Reconstruction From Planar Camera Arrays

Symmetric Multi-View Stereo Reconstruction From Planar Camer...

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

作者： Matthieu Maitre Yoshihisa Shinagawa Minh N. Do Microsoft Corporation Redmond WA USA Siemens Medical Solutions Inc. Malvern PA USA University of Illinois Urbana IL USA

We present a novel stereo algorithm which performs surface reconstruction from planar camera arrays. It incorporates the merits of both generic camera arrays and rectified binocular setups, recovering large surfaces like the former and performing efficient computations like the latter. First, we introduce a rectification algorithm which gives freedom in the design of camera arrays and simplifies photometric and geometric computations. We then define a novel set of data-fusion functions over 4-neighborhoods of cameras, which treat all cameras symmetrically and enable standard binocular stereo algorithms to handle arrays with arbitrary number of cameras. In particular, we introduce a photometric fusion function which handles partial visibility and extracts depth information along both horizontal and vertical baselines. Finally, we show that layered depth images and sprites with depth can be efficiently extracted from the rectified 3D space. Experimental results on real images confirm the effectiveness of the proposed method, which reconstructs dense surfaces larger by 20% on Tsukuba.

关键词： Cameras Image reconstruction Surface reconstruction Data mining Stereo image processing Surface treatment Sprites (computer) Photometry Planar arrays Reconstruction algorithms

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Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval

Semi-Supervised Distance Metric Learning for Collaborative I...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Steven C. H. Hoi Wei Liu Shih-Fu Chang School of Computer Engineering Nanyang Technological University Singapore Department of Electrical Engineering Columbia University USA

Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called "Collaborative Image Retrieval" (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called "Laplacian Regularized Metric Learning" (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both log data and unlabeled data information through an effective graph regularization framework. We show that reliable metrics can be learned from real log data even they may be noisy and limited at the beginning stage of a CIR system. We conducted extensive evaluation to compare the proposed method with a large number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information.

关键词： Collaboration Image retrieval Feedback Euclidean distance Information retrieval Content based retrieval Laplace equations Machine learning Robustness Extraterrestrial measurements

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Manifold-Manifold Distance with Application to Face recognition based on Image Set

Manifold-Manifold Distance with Application to Face Recognit...

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

作者： Ruiping Wang Shiguang Shan Xilin Chen Wen Gao Graduate School of Chinese Academy of Sciences Beijing China Digital Media Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China School of EE&CS Peking University Beijing China

In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. the proposed MMD method is evaluated on the task of Face recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.

关键词： Face recognition Probes Image converters Content addressable storage Testing Cameras Image storage Video sequences Image recognition Object recognition

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3D Ultrasound Tracking of the Left Ventricle Using One-Step Forward Prediction and Data Fusion of Collaborative Trackers

3D Ultrasound Tracking of The Left Ventricle Using One-Step ...

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

作者： Lin Yang Bogdan Georgescu Yefeng Zheng Peter Meer Dorin Comaniciu Integrated Data Systems Siemens AG Corporate Research and Development Princeton NJ USA Electrical and Computer Engineering Rutgers University Piscataway NJ USA

Tracking the left ventricle (LV) in 3D ultrasound data is a challenging task because of the poor image quality and speed requirements. Many previous algorithms applied standard 2D tracking methods to tackle the 3D problem. However, the performance is limited due to increased data size, landmarks ambiguity, signal drop-out or non-rigid deformation. In this paper we present a robust, fast and accurate 3D LV tracking algorithm. We propose a novel one-step forward prediction to generate the motion prior using motion manifold learning, and introduce two collaborative trackers to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, our algorithm provides the best results and sub-voxel accuracy. the new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains 4,925,440 voxels.

关键词： Ultrasonic imaging Collaboration Robustness Tracking Data systems Image quality Shape Heart Cardiovascular diseases Image segmentation

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Image Segmentation with a Parametric Deformable Model using Shape and Appearance Priors

Image Segmentation with a Parametric Deformable Model using ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Ayman El-Baz Georgy Gimel'farb Dept. of Bioengineering University of Louisville Louisville KY USA Dept. of Computer Science The University of Auckland Auckland New Zealand

We propose a novel parametric deformable model controlled by shape and visual appearance priors learned from a training subset of co-aligned images of goal objects. the shape prior is derived from a linear combination of vectors of distances between the training boundaries and their common centroid. the appearance prior considers gray levels within each training boundary as a sample of a Markov-Gibbs random field with pairwise interaction. Spatially homogeneous interaction geometry and Gibbs potentials are analytically estimated from the training data. To accurately separate a goal object from an arbitrary background, empirical marginal gray level distributions inside and outside of the boundary are modeled with adaptive linear combinations of discrete Gaussians (LCDG). the evolution of the parametric deformable model is based on solving an Eikonal partial differential equation with a new speed function which combines the prior shape, prior appearance, and current appearance models. Due to the analytical shape and appearance priors and a simple Expectation-Maximization procedure for getting the object and background LCDG, our segmentation is considerably faster than most of the known geometric and parametric models. Experiments with various goal images confirm the robustness, accuracy, and speed of our approach.

关键词： Image segmentation Deformable models Shape control Vectors Geometry Training data Gaussian distribution Partial differential equations Parametric statistics Robustness

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Viewpoint-Independent Object Class Detection using 3D Feature Maps

Viewpoint-Independent Object Class Detection using 3D Featur...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Joerg Liebelt Cordelia Schmid Klaus Schertler IW-SI EADS Innovation Works Munich Germany LEAR INRIA Grenoble Montbonnot France

this paper presents a 3D approach to multi-view object class detection. Most existing approaches recognize object classes for a particular viewpoint or combine classifiers for a few discrete views. We propose instead to build 3D representations of object classes which allow to handle viewpoint changes and intra-class variability. Our approach extracts a set of pose and class discriminant features from synthetic 3D object models using a filtering procedure, evaluates their suitability for matching to real image data and represents them by their appearance and 3D position. We term these representations 3D Feature Maps. For recognizing an object class in an image we match the synthetic descriptors to the real ones in a 3D voting scheme. Geometric coherence is reinforced by means of a robust pose estimation which yields a 3D bounding box in addition to the 2D localization. the precision of the 3D pose estimation is evaluated on a set of images of a calibrated scene. the 2D localization is evaluated on the PASCAL 2006 dataset for motorbikes and cars, showing that its performance can compete with state-of-the-art 2D object detectors.

关键词： Object detection computer vision Data mining Matched filters Filtering Image recognition Voting Robustness Yield estimation Layout

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Face Tracking and recognition with Visual Constraints in Real-World Videos

Face Tracking and Recognition with Visual Constraints in Rea...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Minyoung Kim Sanjiv Kumar Vladimir Pavlovic Henry Rowley Department of Computer Science Rutgers University Piscataway NJ USA Google Research New York NY USA Google Research Mountain View CA USA

We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework. the generative term conforms the particles to the space of generic face poses while the discriminative one ensures rejection of poorly aligned targets. this leads to a tracker that significantly improves robustness against abrupt appearance changes and occlusions, critical for the subsequent recognition phase. Identity of the tracked subject is established by fusing pose-discriminant and person-discriminant features over the duration of a video sequence. this leads to a robust video-based face recognizer with state-of-the-art recognition performance. We test the quality of tracking and face recognition on real-world noisy videos from YouTube as well as the standard Honda/UCSD database. Our approach produces successful face tracking results on over 80% of all videos without video or person-specific parameter tuning. the good tracking performance induces similarly high recognition rates: 100% on Honda/UCSD and over 70% on the YouTube set containing 35 celebrities in 1500 sequences.

关键词： Face recognition Videos Target tracking Lighting Face detection Robustness Particle tracking Filtering YouTube computer science

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Locally Assembled Binary (LAB) Feature with Feature-centric Cascade for Fast and Accurate Face Detection

Locally Assembled Binary (LAB) Feature with Feature-centric ...

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

作者： Shengye Yan Shiguang Shan Xilin Chen Wen Gao Key Lab of Intelligent Information Processing Chinese Academy and Sciences Beijing China Key Lab of Intelligent Information Processing Chinese Academy of Sciences (CAS) Beijing China School of EE& Peking University Beijing China Digital Media Research Center Institute of Computing Technology Chinese Academy and Sciences Beijing China

In this paper, we describe a novel type of feature for fast and accurate face detection. the feature is called Locally Assembled Binary (LAB) Haar feature. LAB feature is basically inspired by the success of Haar feature and Local Binary pattern (LBP) for face detection, but it is far beyond a simple combination. In our method, Haar features are modified to keep only the ordinal relationship (named by binary Haar feature) rather than the difference between the accumulated intensities. Several neighboring binary Haar features are then assembled to capture their co-occurrence with similar idea to LBP. We show that the feature is more efficient than Haar feature and LBP both in discriminating power and computational cost. Furthermore, a novel efficient detection method called feature-centric cascade is proposed to build an efficient detector, which is developed from the feature-centric method. Experimental results on the CMU+MIT frontal face test set and CMU profile test set show that the proposed method can achieve very good results and amazing detection speed.

关键词： Assembly Face detection computer vision Detectors Content addressable storage Computational efficiency Testing Intelligent robots Humans Skin

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Auto-context and Its Application to High-level vision Tasks

Auto-context and Its Application to High-level Vision Tasks

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

作者： Zhuowen Tu Lab of Neuro Imaging University of California Los Angeles USA

the notion of using context information for solving high-level vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. the current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. In this paper, we propose an auto-context algorithm. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. the discriminative probability (or classification confidence) maps by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. the algorithm then iterates to approach the ground truth. Auto-context learns an integrated low-level and context model, and is very general and easy to implement. Under nearly the identical parameter setting in the training, we apply the algorithm on three challenging vision applications: object segmentation, human body configuration, and scene region labeling. It typically takes about 30 ～ 70 seconds to run the algorithm in testing. Moreover, the scope of the proposed algorithm goes beyond high-level vision. It has the potential to be used for a wide variety of problems of multi-variate labeling.

关键词： Shape Layout Context modeling Markov random fields Hidden Markov models Biological system modeling Labeling Statistics Neuroimaging Algorithm design and analysis

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