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Saliency and Segregation Without Feature Gradient: New Insights for Segmentation from Orientation-Defined Textures

Saliency and Segregation Without Feature Gradient: New Insig...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： O. Ben-Shahar Ben-Gurion University of the Negev Beersheba Israel

The analysis of texture patterns, and texture segregation in particular, are at the heart of perceptual organization. In this paper we question the widely accepted view that the detection (both perceptual and computational) of salient perceptual singularities between perceptually coherent texture regions is tightly dependent upon feature gradients. Specifically, we study smooth orientation-defined textures (ODTs) and show that they exhibit striking perceptual singularities even without any outstanding gradients in their defining feature, namely orientation. We further show how these generic singularities are not only unpredictable from the orientation gradient, but that they also defy popular segmentation algorithms and neural models. We then examine smooth ODTs from a (differential) geometric point of view and develop a theory that fully predicts their perceptual singularities from two ODT curvatures. The computational results exhibit striking correspondence to segregation performed by human subjects and provide a conclusive evidence for the role of curvature in texture segregation. Extensions and implications of our results are developed for various aspects of visual processing.

关键词： Humans pattern analysis Heart Shape Surface texture Geometry Image segmentation Predictive models computer vision pattern recognition

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Vertical Parallax from Moving Shadows

Vertical Parallax from Moving Shadows

引用

conference on computer vision and pattern recognition (CVPR)

作者： Y. Caspi M. Werman Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

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.

关键词： Cameras Layout Light sources Image reconstruction computer science Strips Mathematics Casting Laser beams Pixel

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Learning a manifold-constrained map between image sets: applications to matching and pose estimation

Learning a manifold-constrained map between image sets: appl...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jihun Ham Ikkjin Ahn D. Lee GRASP Laboratory University of Pennsylvania USA

This paper proposes a method for matching two sets of images given a small number of training examples by exploiting the underlying structure of the image manifolds. A nonlinear map from one manifold to another is constructed by combining linear maps locally defined on the tangent spaces of the manifolds. This construction imposes strong constraints on the choice of the maps, and makes possible good generalization of correspondences between all of the image sets. This map is flexible enough to approximate an arbitrary diffeomorphism between manifolds and can serve many purposes for applications. The underlying algorithm is a non-iterative efficient procedure whose complexity mainly depends on the number of matched training examples and the dimensionality of the manifold, and not on the number of samples nor on the dimensionality of the images. Several experiments were performed to demonstrate the potential of our method in image analysis and pose estimation. The first example demonstrates how images from a rotating camera can be mapped to the underlying pose manifold. Second, computer generated images from articulating toy figures are matched using the underlying 4 dimensional manifold to generate image-driven animations. Finally, two sets of actual lip images during speech are matched by their appearance manifold. In all these cases, our algorithm is able to obtain reasonable matches between thousands of large-dimensional images, with a minimum of computation.

关键词： Cameras Image analysis Image generation computer vision Robot vision systems Laboratories Animation Speech Lighting Application software

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Principled Hybrids of Generative and Discriminative Models

Principled Hybrids of Generative and Discriminative Models

引用

conference on computer vision and pattern recognition (CVPR)

作者： J.A. Lasserre C.M. Bishop T.P. Minka University of Cambridge Cambridge UK Microsoft Research Limited Cambridge UK

When labelled training data is plentiful, discriminative techniques are widely used since they give excellent generalization performance. However, for large-scale applications such as object recognition, hand labelling of data is expensive, and there is much interest in semi-supervised techniques based on generative models in which the majority of the training data is unlabelled. Although the generalization performance of generative models can often be improved by ‘training them discriminatively’, they can then no longer make use of unlabelled data. In an attempt to gain the benefit of both generative and discriminative approaches, heuristic procedure have been proposed [2, 3] which interpolate between these two extremes by taking a convex combination of the generative and discriminative objective functions. In this paper we adopt a new perspective which says that there is only one correct way to train a given model, and that a ‘discriminatively trained’ generative model is fundamentally a new model [7]. From this viewpoint, generative and discriminative models correspond to specific choices for the prior over parameters. As well as giving a principled interpretation of ‘discriminative training’, this approach opens door to very general ways of interpolating between generative and discriminative extremes through alternative choices of prior. We illustrate this framework using both synthetic data and a practical example in the domain of multi-class object recognition. Our results show that, when the supply of labelled training data is limited, the optimum performance corresponds to a balance between the purely generative and the purely discriminative.

关键词： Hybrid power systems Training data Object recognition computer vision Large-scale systems Labeling Machine learning Application software Parametric statistics Predictive models

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Robust Boundary DetectionWith Adaptive Grouping

Robust Boundary DetectionWith Adaptive Grouping

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： F.J. Estrada A.D. Jepson Centre for Vision Research York University Toronto ONT Canada Department of Comp. Science University of Toronto Toronto ONT Canada

This paper presents a perceptual grouping algorithm that performs boundary extraction on natural images. Our grouping method maintains and updates a model of the appearance of the image regions on either side of a growing contour. This model is used to change grouping behaviour at run-time, so that, in addition to following the traditional Gestalt grouping principles of proximity and good continuation, the grouping procedure favours the path that best separates two visually distinct parts of the image. The resulting algorithm is computationally efficient and robust to clutter and texture. We present experimental results on natural images from the Berkeley Segmentation Database and compare our results to those obtained with three alternate grouping methods.

关键词： Robustness Change detection algorithms Runtime Image segmentation Image databases Visual databases Humans Data mining Parallel processing Educational institutions

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Reinforcement Matching Using Region Context

Reinforcement Matching Using Region Context

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Hongli Deng E.N. Mortensen L. Shapiro T.G. Dietterich Electrical Engineering and Computer Science Oregon State University USA Computer Science and Engineering University of Washington USA

Local feature-based matching is robust to both clutter and occlusion. However, a primary shortcoming of local features is a deficiency of global information that can cause ambiguities in matching. Local features combined with global relationships convey much more information, but global spatial information is often not robust to occlusion and/or non-rigid transformations. This paper proposes a new framework for including global context information into local feature matching, while still maintaining robustness to occlusion, clutter, and nonrigid transformations. To generate global context information, we extend previous fixed-scale, circular-bin methods by using affine-invariant log-polar elliptical bins. Further, we employ a reinforcement matching scheme that provides greater robustness to occlusion and clutter than previous methods that non-discriminately compare accumulated bins values over the entire context. We also present a more robust method of calculating a feature’s dominant orientation. We compare reinforcement matching to nearest neighbor matching without region context and to robust matching methods (RANSAC and PROSAC).

关键词： computer vision computer science Solid modeling Euclidean distance Shape Histograms pattern recognition conferences Robustness

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Scrambling for Video Surveillance with Privacy

Scrambling for Video Surveillance with Privacy

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： F. Dufaux T. Ebrahimi Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

In this paper, we address the problem of scrambling regions of interest in a video sequence for the purpose of preserving privacy in video surveillance. We propose an efficient solution based on transform-domain scrambling. More specifically, the sign of selected transform coefficients is pseudo-randomly flipped during encoding. We address more specifically the two cases of MPEG-4 and Motion JPEG 2000. Simulation results show that the technique can be successfully applied to conceal information in regions of interest in the scene while providing with a good level of security. Furthermore, the scrambling is flexible and allows adjusting the amount of distortion introduced. Finally, this is achieved with a small impact on coding performance and negligible computational complexity increase.

关键词： Video surveillance Privacy Layout Video compression computer architecture Face detection Cryptography Access control Video coding Performance analysis

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Nonparametric Priors on the Space of Joint Intensity Distributions for Non-Rigid Multi-Modal Image Registration

Nonparametric Priors on the Space of Joint Intensity Distrib...

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conference on computer vision and pattern recognition (CVPR)

作者： D. Cremers C. Guetter Chenyang Xu Department of Computer Science University of Bonn Germany Department of Imaging and Visualization Siemens AG Corporate Research and Development Princeton NJ USA

The introduction of prior knowledge has greatly enhanced numerous purely low-level driven image processing algorithms. In this work, we focus on the problem of nonrigid image registration. A number of powerful registration criteria have been developed in the last decade, most prominently the criterion of maximum mutual information. Although this criterion provides for good registration results in many applications, it remains a purely low-level criterion. As a consequence, registration results will deteriorate once this low-level information is corrupted, due to noise, partial occlusions or missing image structure. In this paper, we will develop a Bayesian framework that allows to impose statistically learned prior knowledge about the joint intensity distribution into image registration methods. The prior is given by a kernel density estimate on the space of joint intensity distributions computed from a representative set of pre-registered image pairs. This nonparametric prior accurately models previously learned intensity relations between various image modalities and slice locations. Experimental results demonstrate that the resulting registration process is more robust to missing low-level information as it favors intensity correspondences statistically consistent with the learned intensity distributions.

关键词： Image registration Biomedical imaging Mutual information Motion estimation computer vision Image motion analysis computer science Visualization Image processing Bayesian methods

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Detecting Bilateral Symmetry in Perspective

Detecting Bilateral Symmetry in Perspective

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： H. Cornelius G. Loy Computational Vision & Active Perception Laboratory Royal Institute of Technology Stockholm Sweden

A method is presented for efficiently detecting bilateral symmetry on planar surfaces under perspective projection. The method is able to detect local or global symmetries, locate symmetric surfaces in complex backgrounds, and detect multiple incidences of symmetry. Symmetry is simultaneously evaluated across all locations, scales, orientations and under perspective skew. Feature descriptors robust to local affine distortion are used to match pairs of symmetric features. Feature quadruplets are then formed from these symmetric feature pairs. Each quadruplet hypothesises a locally planar 3D symmetry that can be extracted under perspective distortion. The method is posed independently of a specific feature detector or descriptor. Results are presented demonstrating the efficacy of the method for detecting bilateral symmetry under perspective distortion. Our unoptimised Matlab implementation, running on a standard PC, requires of the order of 20 seconds to process images with 1,000 feature points.

关键词： computer vision Object detection Image analysis Detectors Psychology Image reconstruction Vehicle detection Laboratories Robustness Mirrors

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Robust Estimation in the Presence of Spatially Coherent Outliers

Robust Estimation in the Presence of Spatially Coherent Outl...

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： R. Fransens C. Strecha L. Van Gool ESAT-PSI Katholieke Universiteit Leuven Leuven Belgium

We present a generative model based approach to deal with spatially coherent outliers. The model assumes that image pixels are generated by either one of two distinct processes: an inlier process which is responsible for the generation of the majority of the data, and an outlier process which generates pixels not adhering to the inlier model. The partitioning into inlier and outlier regions is made explicit by the introduction of a hidden binary map. To account for the coherent nature of outliers this map is modelled as a Markov Random Field, and inference is made tractable by a mean field EM-algorithm. We make a connection with classical robust estimation theory, and derive the analytic expressions of the equivalent M-estimator for two limiting cases of our model. The effectiveness of the proposed method is demonstrated with two examples. First, in a synthetic linear regression problem, we compare our approach with different M-estimators. Next, in a 2D-face recognition experiment, we try to identify people from partially occluded facial images.

关键词： Robustness Spatial coherence Additive noise Pixel Face recognition computer vision Parameter estimation Image generation Image recognition Layout

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