@inproceedings{Kuehne_2001a,
author={Kuhne, G. and Weickert, J. and Schuster, O. and Richter, S.},
booktitle={Image Processing, 2001. Proceedings. 2001 International Conference on},
title={A tensor-driven active contour model for moving object segmentation},
year={2001},
month={Oct},
volume={2},
pages={73-76 vol.2},
abstract={We propose an approach to the segmentation of video objects based on motion cues. Motion analysis is performed by estimating local orientations in the spatiotemporal domain using the three-dimensional structure tensor. These estimates are integrated as an external force into an active contour model, thus stopping the evolving curve when it reaches the moving object's boundary. To enable simultaneous detection of several objects, we reformulate the tensor-based active contour model using the level-set technique. In addition, a contour refinement technique has been developed to better approximate the real boundary of the moving object. We provide promising experimental results calculated on real-world video sequences widely used within the computer vision community},
keywords={computer vision;edge detection;image motion analysis;image segmentation;image sequences;object detection;tensors;video signal processing;3D structure tensor;computer vision;contour refinement;level-set technique;local orientation estimation;motion analysis;motion cues;moving object segmentation;object detection;real boundary approximation;real-world video sequences;spatiotemporal domain;stopping function;tensor-based motion detection;tensor-driven active contour model;three-dimensional structure tensor;video objects segmentation;Active contours;Motion analysis;Motion detection;Motion estimation;Object detection;Object recognition;Object segmentation;Tensile stress;Video compression;Video sequences},
doi={10.1109/ICIP.2001.958427},}