Concept-oriented video skimming and adaptation via semantic classification
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
FPGA-Based Vocabulary Recognition Module for Humanoid Robot
Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics
Spatiotemporal region enhancement and merging for unsupervized object segmentation
Journal on Image and Video Processing
VLSI implementation of image segmentation processor for brain MRI
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Video-object segmentation and 3D-trajectory estimation for monocular video sequences
Image and Vision Computing
A novel trajectory clustering approach for motion segmentation
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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Segmenting and tracking of objects in video is of great importance for video-based encoding, surveillance, and retrieval. However, the inherent difficulty of object segmentation and tracking is to distinguish changes in the displacement of objects from disturbing effects such as noise and illumination changes. Therefore, in this paper, we formulate a color-based deformable model which is robust against noisy data and changing illumination. Computational methods are presented to measure color constant gradients. Further, a model is given to estimate the amount of sensor noise through these color constant gradients. The obtained uncertainty is subsequently used as a weighting term in the deformation process. Experiments are conducted on image sequences recorded from three-dimensional scenes. From the experimental results, it is shown that the proposed color constant deformable method successfully finds object contours robust against illumination, and noisy, but homogeneous regions.