We propose a motion segmentation algorithm that aims to break a scene into its most prominent moving groups. A weighted graph is constructed on the image sequence by connecting pixels that are in the spatiotemporal neighborhood of each other. At each pixel, we define motion profile vectors which capture the probability distribution of the image velocity. The distance between motion profiles is used to assign a weight on the graph edges. Using normalised cuts we find the most salient partitions of the spatiotemporal graph formed by the image sequence. For segmenting long image sequences, we have developed a recursive update procedure that incorporates knowledge of segmentation in previous frames for efficiently finding the group correspondence in the new frame.

/pdf/motion-segmentation-and-tracking-using-normalized-cuts-2bw5kic0sy.pdf

Motion segmentation and tracking using normalized cuts

Representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. One such representation, called layered representation, has recently been proposed. Although a number of algorithms have been developed for computing these representations, there has not been a consolidated effort into developing a precise mathematical formulation of the problem. This paper presents one such formulation based on maximum likelihood estimation (MLE) of mixture models and the minimum description length (MDL) encoding principle. The three major issues in layered motion representation are: (i) how many motion models adequately describe image motion, (ii) what are the motion model parameters, and (iii) what is the spatial support layer for each motion model. >

/pdf/layered-representation-of-motion-video-using-robust-maximum-158ka7hojk.pdf

Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding

Many natural images contain reflections and transparency, i.e., they contain mixtures of reflected and transmitted light. When viewed from a moving camera, these appear as the superposition of component layer images moving relative to each other. The problem of multiple motion recovery has been previously studied by a number of researchers. However no one has yet demonstrated how to accurately recover the component images themselves. In this paper we develop an optimal approach to recovering layer images and their associated motions from an arbitrary number of composite images. We develop two different techniques for estimating the component layer images given known motion estimates. The first approach uses constrained least squares to recover the layer images. The second approach iteratively refines lower and upper bounds on the layer images using two novel compositing operations, namely minimum- and maximum-composites of aligned images. We combine these layer extraction techniques with a dominant motion estimator and a subsequent motion refinement stage. This results in a completely automated system that recovers transparent images and motions from a collection of input images.

Layer extraction from multiple images containing reflections and transparency

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm

A MAP Approach for Joint Motion Estimation, Segmentation, and Super Resolution

Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized.

Segmentation of moving objects in image sequence: A review

This paper presents a framework combining two prevailing approaches to motion analysis: optical flows which describes motion at each point, and methods that define global motions for larger regions. Image motion is represented by layers-image regions whose coherent motion can be approximated by some parametric motion model. The motion at every point is obtained by the parametric motion estimate of the entire layer, corrected by a residual flow which captures the difference between the real image motion and the layer's motion model. The new approach is able to construct accurate flow fields in the presence of multiple motions, motion boundaries, and transparent motions.

/pdf/accurate-computation-of-optical-flow-by-using-layered-motion-4safw5nu1w.pdf

Accurate computation of optical flow by using layered motion representations

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Accurate computation of optical flow by using layered motion representations