Results 1 - 10 of 312

Object Tracking: A Survey

by Alper Yilmaz, Omar Javed, Mubarak Shah , 2006
"... The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns o ..."
Abstract - Cited by 701 (7 self) - Add to MetaCart
The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape

by Daniel Cremers, Mikael Rousson, Rachid Deriche - International Journal of Computer Vision , 2007
"... Abstract. Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of reg ..."
Abstract - Cited by 169 (4 self) - Add to MetaCart
Abstract. Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals and point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.
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Kernel Density Estimation and Intrinsic Alignment for Knowledge-driven Segmentation: Teaching Level Sets to Walk

by Daniel Cremers, Stanley J. Osher, Stefano Soatto - International Journal of Computer Vision , 2004
"... We address the problem of image segmentation with statistical shape priors in the context of the level set framework. Our paper makes two contributions: Firstly, we propose to generate invariance of the shape prior to certain transformations by intrinsic registration of the evolving level set fun ..."
Abstract - Cited by 116 (16 self) - Add to MetaCart
We address the problem of image segmentation with statistical shape priors in the context of the level set framework. Our paper makes two contributions: Firstly, we propose to generate invariance of the shape prior to certain transformations by intrinsic registration of the evolving level set function. In contrast to existing approaches to invariance in the level set framework, this closed-form solution removes the need to iteratively optimize explicit pose parameters. Moreover, we will argue that the resulting shape gradient is more accurate in that it takes into account the e#ect of boundary variation on the object's pose.
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Using prior shapes in geometric active contours in a variational framework

by Yunmei Chen, Hemant D. Tagare, Sheshadri Thiruvenkadam, Feng Huang, David Wilson, Kaundinya S. Gopinath, Richard, W. Briggs, Edward A. Geiser - IJCV , 2002
"... Abstract. In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the ..."
Abstract - Cited by 113 (3 self) - Add to MetaCart
Abstract. In this paper, we report an active contour algorithm that is capable of using prior shapes. The energy functional of the contour is modified so that the energy depends on the image gradient as well as the prior shape. The model provides the segmentation and the transformation that maps the segmented contour to the prior shape. The active contour is able to find boundaries that are similar in shape to the prior, even when the entire boundary is not visible in the image (i.e., when the boundary has gaps). A level set formulation of the active contour is presented. The existence of the solution to the energy minimization is also established. We also report experimental results of the use of this contour on 2d synthetic images, ultrasound images and fMRI images. Classical active contours cannot be used in many of these images.

Dynamical statistical shape priors for level set based tracking

by Daniel Cremers, Gareth Funka-lea - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2006
"... Abstract. In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has b ..."
Abstract - Cited by 102 (8 self) - Add to MetaCart
Abstract. In recent years, researchers have proposed to introduce statistical shape knowledge into the level set method in order to cope with insufficient low-level information. While these priors were shown to drastically improve the segmentation of images or image sequences, so far the focus has been on statistical shape priors that are time-invariant. Yet, in the context of tracking deformable objects, it is clear that certain silhouettes may become more or less likely over time. In this paper, we tackle the challenge of learning dynamical statistical models for implicitly represented shapes. We show how these can be integrated into a segmentation process in a Bayesian framework for image sequence segmentation. Experiments demonstrate that such shape priors with memory can drastically improve the segmentation of image sequences. 1 Level Set Based Image Segmentation In 1988, Osher and Sethian [16] introduced the level set method 1 as a means to implicitly propagate boundaries C(t) in the image plane Ω ⊂ R 2 by evolving an
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Geodesic Active Regions: A new framework to deal with frame partition problems in Computer Vision

by Nikos Paragios, Rachid Deriche , 2002
"... This paper presents a novel variational framework for dealing with frame partition problems in Computer Vision by the propagation of curves. This framework integrates boundary and region-based frame partition modules under a curve-based energy framework, which aims at finding a set of minimal le ..."
Abstract - Cited by 85 (10 self) - Add to MetaCart
This paper presents a novel variational framework for dealing with frame partition problems in Computer Vision by the propagation of curves. This framework integrates boundary and region-based frame partition modules under a curve-based energy framework, which aims at finding a set of minimal length curves that preserve three main properties: (i) they are regular and smooth, (ii) they are attracted by the boundary points (boundarybased information), (ii) and they create a partition that is optimal according to the expected region properties of the different hypotheses (regionbased information). The defined objective function is minimized using a gradient descent method. According to the obtained motion equations, the set of initial curves is propagated towards the best partition under the influence of boundary and region-based forces, and being constrained by a regularity force. The changes of topology are naturally handled thanks to the level set implementation. Furthermore, a coupled multi-phase propagation is proposed that imposes the idea of mutually exclusive propagating curves, and increases the robustness as well as the convergence rate. The proposed framework has been validated using three important applications in Computer Vision, the tasks of image and supervised texture segmentation in low-level vision and the task of motion estimation and tracking in motion analysis
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Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach

by Nikos Paragios, Rachid Deriche - In European Conference in Computer Vision , 1999
"... . This paper presents a novel variational method for image segmentation that unifies boundary and region-based information sources under the Geodesic Active Region framework. A statistical analysis based on the Minimum Description Length criterion and the Maximum Likelihood Principle for the obs ..."
Abstract - Cited by 84 (2 self) - Add to MetaCart
. This paper presents a novel variational method for image segmentation that unifies boundary and region-based information sources under the Geodesic Active Region framework. A statistical analysis based on the Minimum Description Length criterion and the Maximum Likelihood Principle for the observed density function (image histogram) using a mixture of Gaussian elements, indicates the number of the different regions and their intensity properties. Then, the boundary information is determined using a probabilistic edge detector, while the region information is estimated using the Gaussian components of the mixture model. The defined objective function is minimized using a gradientdescent method where a level set approach is used to implement the resulting PDE system. According to the motion equations, the set of initial curves is propagated toward the segmentation result under the influence of boundary and region-based segmentation forces, and being constrained by a regul...
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A nonparametric statistical method for image segmentation using information theory and curve evolution

by Junmo Kim, John W. Fisher, III, Anthony Yezzi, Müjdat Çetin, Alan S. Willsky - IEEE TRANSACTIONS ON IMAGE PROCESSING , 2005
"... In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We as ..."
Abstract - Cited by 82 (1 self) - Add to MetaCart
In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Furthermore, our method, which does not require any training, performs as good as methods based on training.

Minimization of Region-Scalable Fitting Energy for Image Segmentation

by Chunming Li, Chiu-yen Kao, John C. Gore, Zhaohua Ding - IEEE TRANS. ON IMAGE PROCESSING , 2008
"... Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions ..."
Abstract - Cited by 67 (3 self) - Add to MetaCart
Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmentation. In order to overcome the difficulties caused by intensity inhomogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, intensity information in local regions is extracted to guide the motion of the contour, which thereby enables our model to cope with intensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reinitialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.
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Sobolev active contours

by Andrea C. Mennucci, Ganesh Sundaramoorthi, Anthony Yezzi - INTERNATIONAL JOURNAL OF COMPUTER VISION , 2007
"... All previous geometric active contour models that have been formulated as gradient flows of various energies use the same L 2-type inner product to define the notion of gradient. Recent work has shown that this inner product induces a pathological Riemannian metric on the space of smooth curves. Ho ..."
Abstract - Cited by 66 (9 self) - Add to MetaCart
All previous geometric active contour models that have been formulated as gradient flows of various energies use the same L 2-type inner product to define the notion of gradient. Recent work has shown that this inner product induces a pathological Riemannian metric on the space of smooth curves. However, there are also undesirable features associated with the gradient flows that this inner product induces. In this paper, we reformulate the generic geometric active contour model by redefining the notion of gradient in accordance with Sobolev-type inner products. We call the resulting flows Sobolev active contours. Sobolev metrics induce favorable regularity properties in their gradient flows. In addition, Sobolev active contours favor global translations, but are not restricted to such motions; they are also less susceptible to certain types of local minima in contrast to traditional active contours. These properties are particularly useful in tracking applications. We demonstrate the general methodology by reformulating some standard edge-based and regionbased active contour models as Sobolev active contours and show the substantial improvements gained in segmentation.