This study is to investigate a new representation of a partition of an image domain into a number of regions using a level set method derived from a statistical framework. The proposed model is composed of evolving simple closed planar curves by a region-based force determined by maximizing the posterior image densities over all possible partitions of the image plane containing three terms: a Bayesian term based on the prior probability, a regularity term adopted to avoid the generation of excessively irregular and small segmented regions, and a term based on a region merging prior related to region area, which is applied to allow the number of regions to vary automatically during curve evolution and therefore can optimize the objective functional implicitly with respect to the number of regions. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.
Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.
This study is to investigate a new representation of a partition of an image domain into a fixed but arbitrary number of regions via active contours and level sets. The proposed algorithm is composed of simple closed evolving planar curves by an explicit correspondence to minimize the energy functional containing three terms: multiregion fitting energy, regularization related to the length of the curve, and the distance regularizing term to penalize the deviation of the level set function from a signed distance function. This formulation leads to a system of coupled curve evolution equations, which is easily amenable to a level set implementation, and an unambiguous segmentation because the evolving regions form a partition of the image domain at all times during curve evolution. In order to increase the robustness of the method to noise and to reduce the computational cost, a multiresolution level set schema is proposed, which can perform the evolution curves of the partitioned image at a different resolution. Given these advantages, the proposed method can get good performance and experiments show promising segmentation results on both synthetic and real images.
The purpose of this study is to propose a novel method of a partition of an image domain into an adaptive number of regions using a multilayer foreground-filled method. First, two coupled curves based on a three-region Chan-Vese model, which is built based on the techniques of evolving simple closed planar curves by an explicit correspondence to minimize energy functional containing a fitting term and a regularization term, evolve simultaneously to segment images containing two objects and one background region in each image layer. Second, a foreground-filled technique is used to generate a new image and the three-region Chan-Vese model is repeated to segment the new image for the next image layer. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented on the basis on the length change of an evolving curve. This iterative process is repeated until the background image layer is detected. Numerical experiments on some synthetic and real images have demonstrated the efficiency and robustness of our method.
Representing an object with multiple image fragments or patches for target tracking in a video has proved to be able to maintain the spatial information. The major challenges in visual tracking are effectiveness and robustness. We propose a robust fragments-based tracking algorithm with adaptive feature selection. The best discriminate feature is used for tracking, which can improve tracking effectiveness. A set of likelihood images corresponding to the most discriminative features are fused to divide the object into some fragments, which can maintain the spatial information. By weighting the fragment and background colors, more robust target and candidate models are built. Given these advantages, the novel tracking algorithm can provide more accurate performance and can be directly extended to a multiple object-tracking system.