Level Set Based Moving Object Detection and Segmentation

Wavelet-based Moving Object Segmentation

Moving Object Segmentation Using Level Set Method with Shape Prior, Color and Texture

This paper presents a method for the combination of different feature cues in a level set based moving object segmentation framework. To distinguish object from background, motion detection is firstly adopted to locate object position and obtain coarse shape prior. Moreover, the color and texture feature descriptors that represent object contour are designed in this dissertation. Then the finer...

Simultaneous egomotion estimation, segmentation, and moving object detection

Robust egomotion estimation is a key prerequisite for making a robot truly autonomous. In previous work, a multimodel extension of random sample consensus (RANSAC) was introduced to deal with environments with rapid changes by incorporating moving object information. A multiscale matching algorithm was also proposed to resolve the issue of imperfect segmentation. In this paper, we present a nov...

A Model Set Based Object Segmentation Method Using Level Set Approach

A novel approach for model set based object segmentation is described. The proposed method enables the using of a model set to guide the object segmentation. The object shape model selection and segmentation based on the selected model is formulated as an integrated process of constrained contour energy minimization. The solution derived from this formulation produces an integrated searching pr...

Event-based Moving Object Detection and Tracking

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion extremely reliably in the most sophisticated scenarios but they come at a p...