Results 1  10
of
17
A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape
 International Journal of Computer Vision
, 2007
"... Abstract. Since their introduction as a means of front propagation and their first application to edgebased 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 86 (4 self)
 Add to MetaCart
Abstract. Since their introduction as a means of front propagation and their first application to edgebased 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 regionbased level set segmentation methods and clarify how they can all be derived from a common statistical framework. Regionbased 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 edgebased schemes such as the classical Snakes, regionbased 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 wellsuited 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.
Motion competition: a variational approach to piecewise parametric motion segmentation
 Int. J. Comput. Vision
, 2005
"... Abstract. We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatiotemporal image gradient, given a particular veloci ..."
Abstract

Cited by 54 (8 self)
 Add to MetaCart
Abstract. We present a novel variational approach for segmenting the image plane into a set of regions of parametric motion on the basis of two consecutive frames from an image sequence. Our model is based on a conditional probability for the spatiotemporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. Exploiting the Bayesian framework, we derive a cost functional which depends on parametric motion models for each of a set of regions and on the boundary separating these regions. The resulting functional can be interpreted as an extension of the MumfordShah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimizing this functional with respect to its dynamic variables results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion discontinuity set. We propose two different representations of this motion boundary: an explicit splinebased implementation which can be applied to the motionbased tracking of a single moving object, and an implicit multiphase level set implementation which allows for the segmentation of an arbitrary number of multiply connected moving objects. Numerical results both for simulated ground truth experiments and for realworld sequences demonstrate the capacity of our approach to segment objects based exclusively on their relative motion.
A Variational Technique for Time Consistent Tracking of Curves and Motion
 J MATH IMAGING VIS
"... In this paper, a new framework for the tracking of closed curves and their associated motion fields is described. The proposed method enables a continuous tracking along an image sequence of both a deformable curve and its velocity field. Such an approach is formalized through the minimization of a ..."
Abstract

Cited by 13 (6 self)
 Add to MetaCart
In this paper, a new framework for the tracking of closed curves and their associated motion fields is described. The proposed method enables a continuous tracking along an image sequence of both a deformable curve and its velocity field. Such an approach is formalized through the minimization of a global spatiotemporal continuous cost functional, w.r.t a set of variables representing the curve and its related motion field. The resulting minimization process relies on optimal control approach and consists in a forward integration of an evolution law followed by a backward integration of an adjoint evolution model. This latter pde includes a term related to the discrepancy between the current estimation of the state variable and discrete noisy measurements of the system. The closed curves are represented through implicit surface modeling, whereas the motion is described either by a vector field or through vorticity and divergence maps depending on the kind of targeted applications. The efficiency of the approach is demonstrated on two types of image sequences showing deformable objects and fluid motions.
High Resolution Motion Layer Decomposition Using DualSpace Graph Cuts
 Proc. IEEE Conf. Computer Vision and Pattern Recognition
, 2008
"... We introduce a novel energy minimization method to decompose a video into a set of superresolved moving layers. The proposed energy corresponds to the cost of coding the sequence. It consists of a data term and two terms imposing regularity of the geometry and the intensity of each layer. In contra ..."
Abstract

Cited by 13 (2 self)
 Add to MetaCart
We introduce a novel energy minimization method to decompose a video into a set of superresolved moving layers. The proposed energy corresponds to the cost of coding the sequence. It consists of a data term and two terms imposing regularity of the geometry and the intensity of each layer. In contrast to existing motion layer methods, we perform graph cut optimization in the (dual) layer space to determine which layer is visible at which video position. In particular, we show how arising higherorder terms can be accounted for by a generalization of alpha expansions. Moreover, our model accurately captures longterm temporal consistency. To the best of our knowledge, this is the first work which aims at modeling details of the image formation process (such as camera blur and downsampling) in the context of motion layer decomposition. The experimental results demonstrate that energy minimization leads to a reconstruction of a video in terms of a superposition of multiple highresolution motion layers. 1.
Early spatiotemporal grouping with a distributed oriented energy representation
 In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
, 2009
"... Spatiotemporal data is associated with vast amounts of raw samples. Given the limited computational resources typically available, an initial organization of this data supporting semantically meaningful lines of inquiry would facilitate efficient processing. In this paper, a new representation for g ..."
Abstract

Cited by 7 (6 self)
 Add to MetaCart
Spatiotemporal data is associated with vast amounts of raw samples. Given the limited computational resources typically available, an initial organization of this data supporting semantically meaningful lines of inquiry would facilitate efficient processing. In this paper, a new representation for grouping raw image data into a set of coherent spacetime regions is proposed. Unique in this proposal is that coherency is related to a richer description of local spacetime structure than generally considered. In particular, the representation describes the presence of particular oriented spacetime structures in a distributed manner. A key advantage of this representation is its ability to signal the presence of multiple oriented structures at a given spacetime location. More generally, the abstraction allows for the description and grouping of motion and nonmotionrelated patterns in a uniform manner. Empirical evaluation of the grouping method on synthetic and challenging natural imagery suggests its efficacy.
Extraction of layers of similar motion through combinatorial techniques
 In EMMCVPR
, 2005
"... Abstract. In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimat ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
Abstract. In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimation of these motion models as well as the estimation of their support in the image domain. Our technique is based on an iterative process that integrates robust motion estimation, MRFbased formulation, combinatorial optimization and the use of visual as well as motion features to recover the parameters of the motion models as well as their support layers. Special handling of occlusions as well as adaptive techniques to detect new objects in the scene are also considered. Promising results demonstrate the potentials of our approach. 1
Integrating Region and Boundary Information for Improved Spatial Coherence in Object Tracking
 In Workshop on Articulated and Nonrigid Motion (CVPR), Washington D.C
, 2004
"... This paper describes a novel method for performing spatially coherent motion estimation by integrating region and boundary information. The method begins with a layered, parametric flow model. Since the resulting flow estimates are typically sparse, we use the computed motion in a novel way to compa ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
This paper describes a novel method for performing spatially coherent motion estimation by integrating region and boundary information. The method begins with a layered, parametric flow model. Since the resulting flow estimates are typically sparse, we use the computed motion in a novel way to compare intensity values between images, thereby providing improved spatial coherence of a moving region. This dense set of intensity constraints is then used to initialize an active contour, which is influenced by both motion and intensity data to track the object's boundary.
On the Relationship between Image and Motion Segmentation
, 2004
"... In this paper we present a generative model for image sequences, which can be applied to motion segmentation and tracking, and to image sequence compression. The model consists of regions of relatively constant color that have a motion model explaining their motion in time. At each frame, the mo ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
In this paper we present a generative model for image sequences, which can be applied to motion segmentation and tracking, and to image sequence compression. The model consists of regions of relatively constant color that have a motion model explaining their motion in time. At each frame, the model can allow accretion and deletion of pixels. We also present an algorithm for maximizing the posterior probability of the image sequence model, based on the recently introduced SwendsenWang Cuts algorithm. We show how one can use multiple cues and model switching in a reversible manner to make better bottomup proposals. The algorithm works on the 3d spatiotemporal pixel volume to reassign entire trajectories of constant color in very few steps, while maintaining detailed balance.
Bayesian approaches to motionbased image and video segmentation
 1st International Workshop on Complex Motion, DAGM
, 2004
"... Abstract. We present a variational approach for segmenting the image plane into regions of piecewise parametric motion given two or more frames from an image sequence. Our model is based on a conditional probability for the spatiotemporal image gradient, given a particular velocity model, and on a ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract. We present a variational approach for segmenting the image plane into regions of piecewise parametric motion given two or more frames from an image sequence. Our model is based on a conditional probability for the spatiotemporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. We cast the problem of motion segmentation as one of Bayesian inference, we derive a cost functional which depends on parametric motion models for each of a set of domains and on the boundary separating them. The resulting functional can be interpreted as an extension of the MumfordShah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimization results in an eigenvalue problem for the motion parameters and in a gradient descent evolution for the motion boundary. The evolution of the motion boundaries is implemented by a multiphase level set formulation which allows for the segmentation of an arbitrary number of multiply connected moving objects. We further extend this approach to the segmentation of spacetime volumes of coherent motion from video sequences. To this end, motion boundaries are represented by a set of surfaces in spacetime. An implementation by a higherdimensional multiphase level set model allows the evolving surfaces to undergo topological changes. In contrast to an iterative segmentation of consecutive frame pairs, a constraint on the area of these surfaces leads to an additional temporal regularization of the computed motion boundaries. Numerical results demonstrate the capacity of our approach to segment objects based exclusively on their relative motion. 1
Motion Segmentation by EM Clustering of Good Features
, 2004
"... We present a new algorithm that does motion segmentation by tracking small textured patches and then clustering them using EM. A small patch has the advantage that its motion is well modeled by uniform flow and runs a lower risk of boundary inclusion. Inherently, a small patch has less data so it is ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We present a new algorithm that does motion segmentation by tracking small textured patches and then clustering them using EM. A small patch has the advantage that its motion is well modeled by uniform flow and runs a lower risk of boundary inclusion. Inherently, a small patch has less data so it is more susceptible to noise and it is not well suited to fit locally higher order flow models. To overcome these difficulties, we introduce a motion coherence detector to select only the best features and an efficient statistical technique to compute segmentwise affine flow from the EM clustering parameters. We incorporate a residual noise model without any statistical independence assumption and an efficient # test for the noise model to obtain dense segmentation. Computational efficiency is striven for within a rigorous mathematical framework. Experiments with real image sequences show good segments under a variety of conditions.