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Shedding light on the weather

Shedding light on the weather

引用

conference on computer vision and pattern recognition (cvpr)

作者： S.G. Narasimhan S.K. Nayar Computer Science Department Columbia University NY USA

Virtually all methods in image processing and computer vision, for removing weather effects from images, assume single scattering of light by particles in the atmosphere. In reality, multiple scattering effects are significant. A common manifestation of multiple scattering is the appearance of glows around light sources in bad weather. Modeling multiple scattering is critical to understanding the complex effects of weather on images, and hence essential for improving the performance of outdoor vision systems. We develop a new physics-based model for the multiple scattering of light rays as they travel from a source to an observer. This model is valid for various weather conditions including fog, haze, mist and rain. Our model enables us to recover from a single image the shapes and depths of sources in the scene. In addition, the weather condition and the visibility of the atmosphere can be estimated. These quantities can, in turn, be used to remove the glows of sources to obtain a clear picture of the scene. Based on these results, we demonstrate that a camera observing a distant source can serve as a "visual weather meter". The model and techniques described in this paper can also be used to analyze scattering in other media, such as fluids and tissues. Therefore, in addition to vision in bad weather, our work has implications for medical and underwater imaging.

关键词： Light scattering Particle scattering Atmospheric modeling Atmosphere Layout Image processing computer vision Light sources Machine vision Rain

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Simultaneous pose and correspondence determination using line features

Simultaneous pose and correspondence determination using lin...

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. David D. DeMenthon R. Duraiswami H. Samet Army Research Laboratory Adelphi MD USA Department of Computer Science University of Maryland College Park MD USA Institute for Advanced Computer Studies University of Maryland College Park MD USA

We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.

关键词： Cameras Educational institutions Working environment noise computer vision computer science Laboratories Powders Milling machines Military computing Robustness

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Flux invariants for shape

Flux invariants for shape

引用

conference on computer vision and pattern recognition (cvpr)

作者： P. Dimitrov J.N. Damon K. Siddiqi School of Computer Science McGill University Montreal Canada Department of Mathematics Chapel Hill North Carolina State University Chapel Hill NC USA

We consider the average outward flux through a Jordan curve of the gradient vector field of the Euclidean distance function to the boundary of a 2D shape. Using an alternate form of the divergence theorem, we show that in the limit as the area of the region enclosed by such a curve shrinks to zero, this measure has very different behaviors at medial points than at non-medial ones, providing a theoretical justification for its use in the Hamilton-Jacobi skeletonization algorithm of Siddiqi et al. (2002). We then specialize to the case of shrinking circular neighborhoods and show that the average outward flux measure also reveals the object angle at skeletal points. Hence, formulae for obtaining the boundary curves, their curvatures, and other geometric quantities of interest, can be written in terms of the average outward flux limit values at skeletal points. Thus this measure can be viewed as a Euclidean invariant for shape description: it can be used to both detect the skeleton from the Euclidean distance function, as well as to explicitly reconstruct the boundary from it. We illustrate our results with several numerical simulations.

关键词： Euclidean distance Skeleton Shape measurement Numerical simulation computer vision computer science Mathematics Area measurement Image reconstruction Biomedical imaging

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Hue fields and color curvatures: a perceptual organization approach to color image denoising

Hue fields and color curvatures: a perceptual organization a...

引用

conference on computer vision and pattern recognition (cvpr)

作者： O. Ben-Shahar S.W. Zucker Department of Computer Science Yale University New Heaven CT USA Department of Computer Science and Electrical Engineering Yale University New Heaven CT USA

The denoising of color images is an increasingly studied problem whose state-of-the-art solutions employ a variety of diffusion schemes. Specifying the correct diffusion is difficult, however, in part because of the subtleties of color interactions. We address this difficulty by proposing a perceptual organization approach to color denoising based on the principle of good continuation. We exploit the periodic chromatic (hue) component of the color in its representation as a frame field. We derive two hue curvatures and use them to construct a local model for the behavior of the color, which in turn specifies consistency constraints between nearby color measurements. These constraints are then used to replace noisy pixels by examining their spatial context. Such a contextual analysis (combined with standard methods to handle the scalar channels, saturation and lightness), results in a robust noise removal process that preserves discontinuities, singularities, and fine chromatic structures, including those that diffusion processes are prone to distort. We demonstrate our approach on a variety of synthetic and natural images.

关键词： Color Noise reduction computer science Computed tomography Colored noise Psychology Noise robustness Diffusion processes computer vision Image processing

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An efficient approach to learning inhomogeneous Gibbs model

An efficient approach to learning inhomogeneous Gibbs model

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conference on computer vision and pattern recognition (cvpr)

作者： Ziqiang Liu Hong Chen Heung-Yeung Shum Visiting from Vision Lab Department of Inf. Science and Electronic Engineering University of Zhejiang China Microsoft Research Asia China

ISBN: (纸本)0769519008

The inhomogeneous Gibbs model (IGM) (Liu et al., 2001) is an effective maximum entropy model in characterizing complex high-dimensional distributions. However, its training process is so slow that the applicability of IGM has been greatly restricted. In this paper, we propose an approach for fast parameter learning of IGM. In IGM learning, features are incrementally constructed to constrain the learnt distribution. When a new feature is added, Markov-chain Monte Carlo (MCMC) sampling is repeated to draw samples for parameter learning. In contrast, our approach constructs a closed-form reference distribution using approximate information gain criteria. Because our reference distribution is very close to the optimal one, importance sampling can be used to accelerate the parameter optimization process. For problems with high-dimensional distributions, our approach typically achieves a speedup of two orders of magnitude compared to the original IGM. We further demonstrate the efficiency of our approach by learning a high-dimensional joint distribution of face images and their corresponding caricatures.

关键词： Entropy Monte Carlo methods Sampling methods Random variables Statistical distributions Asia Acceleration Shape Information analysis computer society

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Spacetime stereo: shape recovery for dynamic scenes

Spacetime stereo: shape recovery for dynamic scenes

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conference on computer vision and pattern recognition (cvpr)

作者： Li Zhang B. Curless S.M. Seitz Department of Computer Science and Engineering University of Washington Seattle WA Department of Computer Science and Engineering University of Washington Seattle WA USA

This paper extends the traditional binocular stereo problem into the spacetime domain, in which a pair of video streams is matched simultaneously instead of matching pairs of images frame by frame. Almost any existing stereo algorithm may be extended in this manner simply by replacing the image matching term with a spacetime term. By utilizing both spatial and temporal appearance variation, this modification reduces ambiguity and increases accuracy. Three major applications for spacetime stereo are proposed in this paper. First, spacetime stereo serves as a general framework for structured light scanning and generates high quality depth maps for static scenes. Second, spacetime stereo is effective for a class of natural scenes, such as waving trees and flowing water, which have repetitive textures and chaotic behaviors and are challenging for existing stereo algorithms. Third, the approach is one of very few existing methods that can robustly reconstruct objects that are moving and deforming over time, achieved by use of oriented spacetime windows in the matching procedure. Promising experimental results in the above three scenarios are demonstrated.

关键词： Shape Layout Stereo vision Pixel Robustness Image reconstruction computer science Streaming media Image matching Chaos

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The optimal distance measure for object detection

The optimal distance measure for object detection

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conference on computer vision and pattern recognition (cvpr)

作者： S. Mahamud M. Hebert Department of Computer Science Carnegie Mellon University Pittsburgh PA USA

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the misclassification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchical distance model that is both fast and accurate. Finally, the nearest neighbor search over object part classes was integrated into a whole object detection system and evaluated against an indoor detection task yielding good results.

关键词： Object detection Nearest neighbor searches Neural networks Shape measurement computer vision Testing Solid modeling Runtime computer science Power system modeling

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Learning object intrinsic structure for robust visual tracking

Learning object intrinsic structure for robust visual tracki...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Qiang Wang Guangyou Xu Haizhou Ai Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China

ISBN: (纸本)0769519008

In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle-filter style tracker. We will show that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.

关键词： Robustness Particle tracking computer vision Shape Particle filters computer science Laboratories Intelligent systems Intelligent structures Training data

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Dynamic depth recovery from unsynchronized video streams

Dynamic depth recovery from unsynchronized video streams

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conference on computer vision and pattern recognition (cvpr)

作者： Chunxiao Zhou Hai Tao Department of Computer Engineering University of California Santa Cruz CA USA

ISBN: (纸本)0769519008

We propose an algorithm for estimating dense depth information of dynamic scenes from multiple video streams captured using unsynchronized stationary cameras. We solve this problem by first imposing two assumptions about the scene motion and the temporal offset between cameras. The motion of a scene is described using a local constant velocity model and the camera temporal offset is assumed to be constant within a short of period of time. Based on these models, geometric relations between the images of moving scene points, the scene depth, the scene motions, and the camera temporal offset are investigated and an estimation method is developed to compute the camera temporal offset. The three main steps of the proposed algorithm are: 1) the estimation of the temporal offset between cameras, 2) the synthesis of synchronized image pairs based on the estimated camera temporal offset and optical flow fields computed in each view, and 3) the stereo computation based on the synthesized synchronous image pairs. The proposed algorithm has been tested on both synthetic data and real image sequences. Promising quantitative and qualitative experimental results are demonstrated in the paper.

关键词： Streaming media Layout Cameras Stereo vision Optical computing Motion estimation Video sequences computer vision Solid modeling Optical sensors

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Subset selection for efficient SVM tracking

Subset selection for efficient SVM tracking

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conference on computer vision and pattern recognition (cvpr)

作者： S. Avidan MobileEye Vision Technologies Limited Jerusalem Israel

We update the SVM (support vector machine) score of an object through a video sequence with a small and variable subset of support vectors. In the first frame we use all the support vectors to compute the SVM score of the object but in subsequent frames we use only a small and variable subset of support vectors to update the SVM score. In each frame we calculate the dot-products of the support vectors in the subset with the pattern of the object being tracked. The difference in the dot-products, between past and current frames, is used to update the SVM score. This is done at a fraction of the computational cost required to re-evaluate the SVM score from scratch in every frame. The two methods we develop are "cyclic subset selection", in which we break the set of all support vectors into subsets of equal size and use them cyclically, and "maximum variance subset selection", in which we choose the support vectors whose dot-product with the test pattern varied the most in previous frames. We combine these techniques together for the problem of maintaining the SVM score of objects through a video sequence. Results on real video sequences are shown.

关键词： Support vector machines Object detection Video sequences Testing Face detection Support vector machine classification Layout Acceleration Vehicle detection Computational efficiency

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