Image segmentation based on object oriented mapping parameter estimation
Signal Processing
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Object Oriented Motion Estimation in Color Image Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Spatio-temporal video segmentation using a joint similarity measure
IEEE Transactions on Circuits and Systems for Video Technology
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The edge and motion are the main features that human visual system (HVS) perceives intensively. This paper proposes an algorithm for the segmentation of the moving object with accurate boundary using color and motion focusing on the HVS perception in the general image sequence. The proposed algorithm is composed of three parts: color segmentation, motion analysis, and region refinement and merging part. In the color segmentation phase, K-Means algorithm is used in consideration of the sensitivity of the human color perception to get the boundaries that HVS perceives. The global and local motion estimation are performed in parallel with color analysis. After that, Bayesian clustering using color and motion provides more accurate boundary. In the final stage, regions are merged taking into account their motion. The experimental results of the proposed algorithm show the accurate moving object boundary coinciding with the boundary that HVS perceives.