Markov random field modeling in image analysis
Markov random field modeling in image analysis
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance measures for video object segmentation and tracking
IEEE Transactions on Image Processing
Segmentation Framework Based on Label Field Fusion
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
In this article, we put forward a novel region matching based motion estimation scheme to detect objects with accurate boundaries from video sequences. We have proposed a fuzzy edge incorporated Markov Random Field (MRF) model based spatial segmentation scheme that is able even to identify the blurred boundaries of objects in a scene. Expectation Maximization (EM) algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough knowledge of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. Moving objects in the scene are detected by the proposed χ2-test based local histogram matching. It is noticed that the proposed scheme provides better results with less object background misclassification as compared to optical flow and label fusion based techniques.