Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Background Modeling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Motion-based background subtraction using adaptive kernel density estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3d segmentation of rodent brain structures using hierarchical shape priors and deformable models
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abnormal detection using interaction energy potentials
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Relay Level Set Method for Automatic Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
A 3D Laplacian-driven parametric deformable model
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Neurocomputing
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Motion saliency detection aims at finding the semantic regions in a video sequence. It is an important pre-processing step in many vision applications. In this paper, we propose a new algorithm, Temporal Spectral Residual, for fast motion saliency detection. Different from conventional motion saliency detection algorithms that use complex mathematical models, our goal is to find a good tradeoff between the computational efficiency and accuracy. The basic observation for salient motions is that on the cross section along the temporal axis of a video sequence, the regions of moving objects contain distinct signals while the background area contains redundant information. Thus our focus in this paper is to extract the salient information on the cross section, by utilizing the off-the-shelf method Spectral Residual, which is a 2D image saliency detection method. Majority voting strategy is also introduced to generate reliable results. Since the proposed method only involves Fourier spectrum analysis, it is computationally efficient. We validate our algorithm on two applications: background subtraction in outdoor video sequences under dynamic background and left ventricle endocardium segmentation in MR sequences. Compared with some state-of-art algorithms, our algorithm achieves both good accuracy and fast computation, which satisfies the need as a pre-processing method.