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Robust AAM fitting by fusion of images and disparity data

Robust AAM fitting by fusion of images and disparity data

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Liebelt, Joerg Xiao, Jing Yang, Jie 76131 Karlsruhe Germany Epson Palo Alto Laboratory 3145 Porter Drive Palo Alto CA 94304 School of Computer Science Carnegie Mellon University Pittsburgh PA 15213

ISBN: (纸本)0769525970

Active Appearance Models (AAMs) have been popularly used to represent the appearance and shape variations of human faces. Fitting an AAM to images recovers the face pose as well as its deformable shape and varying appearance. Successful fitting requires that the AAM is sufficiently generic such that it covers all possible facial appearances and shapes in the images. Such a generic AAM is often difficult to be obtained in practice, especially when the image quality is low or when occlusion occurs. To achieve robust AAM fitting under such circumstances, this paper proposes to incorporate the disparity data obtained from a stereo camera with the image fitting process. We develop an iterative multi-level algorithm that combines efficient AAM fitting to 2D images and robust 3D shape alignment to disparity data. Experiments on tracking faces in low-resolution images captured from meeting scenarios show that the proposed method achieves better performance than the original 2D AAM fitting algorithm. We also demonstrate an application of the proposed method to a facial expression recognition task. © 2006 ieee.

关键词： Image reconstruction

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Meta-evaluation of image segmentation using machine learning

Meta-evaluation of image segmentation using machine learning

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Zhang, Hui Cholleti, Sharath Goldman, Sally A. Fritts, Jason E. Dept. of Computer Science and Engineering Washington University One Brookings Drive St. Louis MO 63130 Dept. of Mathematics and Computer Science St. Louis University 221 N. Grand Blvd St. Louis MO 63103

ISBN: (纸本)0769525970

Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation algorithm, or parameterization of a given algorithm, is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods have the advantage that they do not require a manually-segmented reference image for comparison, and can therefore be used for real-time evaluation. Current stand-alone evaluation methods often work well for some types of images, but poorly for others. We propose a meta-evaluation method in which any set of base evaluation methods are combined by a machine learning algorithm that coalesces their evaluations based on a learned weighting function, which depends upon the image to be segmented. The training data used by the machine learning algorithm can be labeled by a human, based on similarity to a human-generated reference segmentation, or based upon system-level performance. Experimental results demonstrate that our method performs better than the existing stand-alone segmentation evaluation methods. © 2006 ieee.

关键词： Image segmentation

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Diffusion distance for histogram comparison

Diffusion distance for histogram comparison

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Ling, Haibin Okada, Kazunori Center for Automation Research Computer Science Department University of Maryland College Park MD 20770 United States Imaging and Visualization Department Siemens Corporate Research Inc. 755 College Rd. E. Princeton NJ 08540 United States

ISBN: (纸本)0769525970

In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover's Distance with much greater efficiency. © 2006 ieee.

关键词： Image analysis

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Image matching using photometric information

Image matching using photometric information

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Kolomenkin, Michael Shimshoni, Ilan Electrical Engineering Department Technion Haifa 32000 Israel Department of Management Information Systems Haifa University Haifa 31905 Israel

ISBN: (纸本)0769525970

Image matching is an essential task in many computer vision applications. It is obvious that thorough utilization of all available information is critical for the success of matching algorithms. However most popular matching methods do not incorporate effectively photometric data. Some algorithms are based on geometric, color invariant features, thus completely neglecting available photometric information. Others assume that color does not differ significantly in the two images;that assumption may be wrong when the images are not taken at the same time, for example when a recently taken image is compared with a database. This paper introduces a method for using color information in image matching tasks. Initially the images are segmented using an off-the-shelf segmentation process (EDISON). No assumptions are made on the quality of the segmentation. Then the algorithm employs a model for natural illumination change to define the probability of two segments to originate from the same surface. When additional information is supplied (for example suspected corresponding point features in both images), the probabilities are updated. We show that the probabilities can easily be utilized in any existing image matching system. We propose a technique to make use of them in a SIFT-based algorithm. The technique's capabilities are demonstrated on real images, where it causes a significant improvement in comparison with the original SIFT results in the percentage of correct matches found. © 2006 ieee.

关键词： pattern matching

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Making a long video short: Dynamic video synopsis

Making a long video short: Dynamic video synopsis

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Rav-Acha, Alex Pritch, Yael Peleg, Shmuel School of Computer Science and Engineering Hebrew University of Jerusalem 91904 Jerusalem Israel

ISBN: (纸本)0769525970

The power of video over still images is the ability to represent dynamic activities. But video browsing and retrieval are inconvenient due to inherent spatio-temporal redundancies, where some time intervals may have no activity, or have activities that occur in a small image region. Video synopsis aims to provide a compact video representation, while preserving the essential activities of the original video. We present dynamic video synopsis, where most of the activity in the video is condensed by simultaneously showing several actions, even when they originally occurred at different times. For example, we can create a "stroboscopic movie", where multiple dynamic instances of a moving object are played simultaneously. This is an extension of the still stroboscopic picture. Previous approaches for video abstraction addressed mostly the temporal redundancy by selecting representative key-frames or time intervals. In dynamic video synopsis the activity is shifted into a significantly shorter period, in which the activity is much denser. Video examples can be found online in http://***/synopsis © 2006 ieee.

关键词： Video signal processing

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Robust multi-target tracking using spatio-temporal context

Robust multi-target tracking using spatio-temporal context

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Nguyen, Hieu T. Qiang, Ji Smeulders, Arnold W. M. Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute United States Intelligent Sensory Information Systems Faculty of Science University of Amsterdam Netherlands

ISBN: (纸本)0769525970

In multi-target tracking, the maintaining of the correct identity of targets is challenging. In the presented tracking method, accurate target identification is achieved by incorporating the appearance information of the spatial and temporal context of each target. The spatial context of a target involves local background and nearby targets. The first contribution of the paper is to provide a new discriminative model for multi-target tracking with the embedded classification of each target against its context. As a result, the tracker not only searches for the image region similar to the target but also avoids latching on nearby targets or on a background region. The temporal context of a target includes its appearances seen during tracking in the past. The past appearances are used to train a probabilistic PCA that is used as the measurement model of the target at the present. As the second contribution, we develop a new incremental scheme for probabilistic PCA. It can update accurately the full set of parameters including a noise parameterstill ignored in related literature. The experiments show robust tracking performance under the condition of severe clutter, occlusions and pose changes. © 2006 ieee.

关键词： Automatic target recognition

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Shape-based approach to robust image segmentation using Kernel PCA

Shape-based approach to robust image segmentation using Kern...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Dambreville, Samuel Rathi, Yogesh Tannenbaum, Allen Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta GA 30332 United States

ISBN: (纸本)0769525970

Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the work of Leventon et al., we revisit the use of principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. To this end, we utilize Kernel PCA and show that this method of learning shapes outperforms linear PCA, by allowing only shapes that are close enough to the training data. In the proposed segmentation algorithm, shape knowledge and image information are encoded into two energy functional entirely described in terms of shapes. This consistent description allows to fully take advantage of the Kernel PCA methodology and leads to promising segmentation results. In particular, our shape-driven segmentation technique allows for the simultaneous encoding of multiple types of shapes, and offers a convincing level of robustness with respect to noise, clutter, partial occlusions, or smearing. © 2006 ieee.

关键词： Image segmentation

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An integrated segmentation and classification approach applied to multiple sclerosis analysis

An integrated segmentation and classification approach appli...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Akselrod-Ballin, Ayelet Galun, Meirav Gomori, Moshe John Filippi, Massimo Valsasina, Paula Basri, Ronen Brandt, Achi Dept. of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel Dept. of Radiology Hadassah University Hospital Jerusalem Israel Neuroimaging Research Unit Hospital San Raffaele Milan Italy

ISBN: (纸本)0769525970

We present a novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detecting multiple sclerosis lesions in 3D MRI data. Our method uses segmentation to obtain a hierarchical, decomposition of a multi-channel, anisotropic MRI scan. It then produces a rich set of features describing the segments in terms of intensity, shape, location, and neighborhood relations. These features are then fed into a decision tree-based classifier, trained with data labeled by experts, enabling the detection of lesions in all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. We provide experiments showing successful detections of lesions in both simulated and real MR images. © 2006 ieee.

关键词： Brain

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Vertical parallax from moving shadows

Vertical parallax from moving shadows

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Caspi, Yaron Werman, Michael Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot 76100 Israel School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem 91904 Israel

ISBN: (纸本)0769525970

This paper presents a method for capturing and computing 3D parallax. 3D parallax, as used here, refers to vertical offset from the ground plane, height. The method is based on analyzing shadows of vertical poles (e.g., a tall building's contour) that sweep the object. Unlike existing beam-scanning approaches, such as shadow or structured light, that recover the distance of a point from the camera, our approach measures the height from the ground plane directly. Previous methods compute the distance from the camera using triangulation between rays outgoing from the light-source and the camera. Such a triangulation is difficult when the objects are far from the camera, and requires accurate knowledge of the light source position. In contrast, our approach intersects two (unknown) planes generated separately by two casting objects. This omits the need to precompute the location of the light source. Furthermore, it allows a moving light source to be used. The proposed setup is particularly useful when the camera cannot directly face the scene or when the object is far away from the camera. A good example is an urban scene captured by a single webcam. © 2006 ieee.

关键词： Three dimensional computer graphics

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SVM-KNN: Discriminative nearest neighbor classification for visual category recognition

SVM-KNN: Discriminative nearest neighbor classification for ...

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2006 ieee computer society conference on computer vision and pattern recognition, CVPR 2006

作者： Zhang, Hao Berg, Alexander C. Maire, Michael Malik, Jitendra Computer Science Division EECS Department Univ. of California Berkeley CA 94720 United States

ISBN: (纸本)0769525970

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation ofpairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images. © 2006 ieee.

关键词： Learning systems

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