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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： M.S. Allili D. Ziou Département d'informatique Faculté des Sciences Université de Sherbrook Sherbrooke QUE Canada

In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region features distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences.

关键词： Robustness Active contours Video sequences level set computer vision Object detection Tracking Calculus Application software Video surveillance

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Channel Segmentation using Confidence and Curvature-Guided level sets on Noisy Seismic Images

Channel Segmentation using Confidence and Curvature-Guided L...

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ieee workshop on Applications of computer vision (WACV)

作者： Benjamin J. Kadlec Department of Computer Science University of Colorado Boulder CO USA

This paper presents a new method for segmenting channel features from commonly noisy 3D seismic images. Anisotropic diffusion using Gaussian-smoothed first order structure tensors is conducted along the strata of seismic images in a way that filters across discontinuous regions from noise or faulting, while preserving channel edges. The eigenstructure of the second order structure tensor is used to generate an estimation of orientation and channel curvature. Gaussian smoothing of second order tensor orientations accounts for noisy vectors from imprecise finite difference calculations and generates a stable tensor across the image. Analysis of the confidence and direction of second order eigenvectors is used to enhance depositional curvature in channel features by generating a confidence and curvature attribute. The tensor-derived attribute forms the terms of a PDE, which is iteratively updated as an implicit surface using the level set process. This technique is tested on two 3D seismic images with results that demonstrate the effectiveness of the approach.

关键词： Image segmentation level set Noise level Tensile stress Gaussian noise Anisotropic magnetoresistance Gaussian channels Filters Smoothing methods Noise generators

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Active unsupervised texture segmentation on a diffusion based feature space

Active unsupervised texture segmentation on a diffusion base...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M. Rousson T. Brox R. Deriche I.N.R.I.A. Sophia Antipolis France Math. Image Analysis Group University of Saarland Germany

We propose a novel and efficient approach for active unsupervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that incorporates these features in a level set based unsupervised segmentation process that adaptively takes into account their estimated statistical information inside and outside the region to segment. The approach has been tested on various textured images, and its performance is favorably compared to recent studies.

关键词： Image segmentation Tensile stress Layout Feature extraction Data mining Gabor filters level set Statistics Smoothing methods Image texture analysis

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Sphere2: Jerry's rig, an OpenGL application for non-linear panorama viewing and interaction

Sphere2: Jerry's rig, an OpenGL application for non-linear p...

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2012 Western New York Image Processing workshop, WNYIPW 2012

作者： Keane, Tommy P. Cahill, Nathan D. Rhody, Harvey Hu, Bo Tarduno, John Jacobs, Robert Pelz, Jeff Chester F. Carlson Center for Imaging Science Rochester Institute of Technology Rochester NY 14623 United States School of Mathematical Sciences Rochester Institute of Technology Rochester NY 14623 United States Department of Earth and Environmental Sciences University of Rochester Rochester NY 14611 United States Department of Brain and Cognitive Sciences University of Rochester Rochester NY 14611 United States

ISBN: (纸本)9781467355988

Given a set of images, or time-lapsed imagery, that is captured in an unconstrained domain, there are numerous methods to map that data into a domain that is readily displayable on basic rectilinear digital displays. However, while these mappings can be mathematically sound, they are methods that modify the spatio-temporal 'scale' of the scene, and thus can become quite semantically confusing when viewed in this manner. In working with data to be mathematically and scientifically analyzed, we have found that we also require a level of semantic consistency, such as in providing accessible imagery for expert annotation or in generating 'virtual field trips'. This is why we have applied the same mathematical rigor to instead develop an interactive, OpenGL-based viewing application for spatio-temporally non-linear imagery. This application is extensible and entirely custom, such that we can not only interact with single sets of related imagery, but also view the relationships across sets of imagery in a semantically viable, and mathematically sound, domain. © 2012 ieee.

关键词： computer vision

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A Probabilistic Approach to Linear Subspace Fitting for computer vision Problems

A Probabilistic Approach to Linear Subspace Fitting for Comp...

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ieee computer Society Conference on computer vision and Pattern Recognition workshops (CVPRW)

作者： T. Okatani Graduate School of Information Sciences University of Tohoku Sendai Japan

Several computer vision problems, such as some of photometric problems and the problem of affine structure from motion, are formulated as fitting linear subspace(s) to point data in a multi-dimensional space. In ideal cases the linear subspaces can easily be computed by PCA/SVD algorithms. Unfortunately this will not apply to real cases, since there are outliers and missing components in real data. Furthermore it is sometimes necessary to fit multiple different subspaces to a set of point data in a situation where each point belongs to one of the subspaces but it is unknown which subspace each point belongs to. One straightforward solution to these advanced cases is to adopt the expectation maximization framework based on Bayesian inference. However, this solution does not seem to have been well considered in computer vision community, as far as the above problems of linear subspace fitting are concerned. This paper presents expectation maximization algorithms and its extension, variational Bayes-based algorithm, for several cases of linear subspace fitting and applies them to computer vision problems.

关键词： computer vision Principal component analysis Inference algorithms Lighting Photometry Fitting Bayesian methods Cameras Pixel Object recognition

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Bridged variational Autoencoders for Joint Modeling of Images and Attributes

Bridged Variational Autoencoders for Joint Modeling of Image...

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ieee workshop on Applications of computer vision (WACV)

作者： Ravindra Yadav Ashish Sardana Vinay P Namboodiri Rajesh M Hegde IIT Kanpur NVIDIA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Generative models have recently shown the ability to realistically generate data and model the distribution accurately. However, joint modeling of an image with the attribute that it is labeled with requires learning a cross modal correspondence between image and attribute data. Though the information present in a set of images and its attributes possesses completely different statistical properties altogether, there exists an inherent correspondence that is challenging to capture. Various models have aimed at capturing this correspondence either through joint modeling of a variational autoencoder or through separate encoder networks that are then concatenated. We present an alternative by proposing a bridged variational autoencoder that allows for learning cross-modal correspondence by incorporating cross-modal hallucination losses in the latent space. In comparison to the existing methods, we have found that by using a bridge connection in latent space we not only obtain better generation results, but also obtain highly parameter-efficient model which provide 40% reduction in training parameters for bimodal dataset and nearly 70% reduction for trimodal dataset. We validate the proposed method through comparison with state of the art methods and benchmarking on standard datasets.

关键词： Training Decoding Computational modeling Data models Bridges Task analysis computer architecture

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Distance Regularized level set Evolution and Its Application to Image Segmentation

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ieee TRANSACTIONS ON IMAGE PROCESSING 2010年第12期19卷 3243-3254页

作者： Li, Chunming Xu, Chenyang Gui, Changfeng Fox, Martin D. Vanderbilt Univ Inst Imaging Sci Nashville TN 37232 USA Siemens Corp Res Technol Business Ctr Berkeley CA 94704 USA Univ Connecticut Dept Math Storrs CT 06269 USA Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient

关键词： Forward and backward diffusion image segmentation level set method narrowband reinitialization

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Shape Prior Segmentation of Multiple Objects with Graph Cuts

Shape Prior Segmentation of Multiple Objects with Graph Cuts

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26th ieee Conference on computer vision and Pattern Recognition (CVPR 2008), vol.3

作者： Nhat Vu B. S. Manjunath Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape.

关键词： Image segmentation level set Shape measurement Multi-stage noise shaping Pixel Runtime Kernel Labeling Probability Background noise

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Simultaneous semantic segmentation of a set of partially labeled images

Simultaneous semantic segmentation of a set of partially lab...

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ieee workshop on Applications of computer vision (WACV)

作者： Qiongjie Tian Baoxin Li Computer Science and Engineering Arizona State University

ISBN: (纸本)9781509006427

Semantic segmentation, by which an image is decomposed into regions with their respective semantic labels, is often the first step towards image understanding. Existing research on this regard is mainly performed under two conditions: the fully-supervised setting that relies on a set of images with pixel-level labels and the weakly-supervised one that uses only image-level labels. In both cases, the labeling task is time-consuming and laborious, and thus training data are always limited. In practice, there are voluminous on-line images, which unfortunately often have only incomplete image-level labels (tags) but would otherwise be potentially useful for a learning-based algorithm. Only limited efforts have been attempted on using such coarsely and incompletely labelled data for semantic segmentation. This paper proposes a new approach to semantic segmentation of a set of partially-labelled images, using a formulation considering information from multiple visual similar images. Experiments on several popular datasets, with comparison with existing methods, demonstrate evident performance improvement of the proposed approach.

关键词： Semantics Image segmentation Labeling Context Training data Graphical models computer science

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Tracking Distributions with an Overlap Prior

Tracking Distributions with an Overlap Prior

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26th ieee Conference on computer vision and Pattern Recognition (CVPR 2008), vol.3

作者： Ismail Ben Ayed Shuo Li Ian Ross GE Healthcare Systems London ONT Canada London Health Sciences Centre London ONT Canada

Recent studies have shown that embedding similarity/dissimilarity measures between distributions in the variational level set framework can lead to effective object segmentation/tracking algorithms. In this connection, existing methods assume implicitly that the overlap between the distributions of image data within the object and its background has to be minimal. Unfortunately, such assumption may not be valid in many important applications. This study investigates an overlap prior, which embeds knowledge about the overlap between the distributions of the object and the background in level set tracking. It consists of evolving a curve to delineate the target object in the current frame. The level set curve evolution equation is sought following the maximization of a functional containing three terms: (1) an original overlap prior which measures the conformity of overlap between the nonparametric (kernel-based) distributions within the object and the background to a learned description, (2) a term which measures the similarity between a model distribution of the object and the sample distribution inside the curve, and (3) a regularization term for smooth segmentation boundaries. The Bhattacharyya coefficient is used as an overlap measure. Apart from leading to a method which is more versatile than current ones, the overlap prior speeds up significantly the curve evolution. Comparisons and results demonstrate the advantages of the proposed prior over related methods, and its usefulness in important applications such as the left ventricle tracking in Magnetic Resonance (MR) images.

关键词： level set Image segmentation Photometry Target tracking Object segmentation Equations Biomedical imaging Medical services Magnetic resonance Image sequences

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