Robust regression and outlier detection
Robust regression and outlier detection
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical model-based change detection in moving video
Signal Processing
Image difference threshold strategies and shadow detection
BMVC '95 Proceedings of the 1995 British conference on Machine vision (Vol. 1)
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion-based Object Segmentation using Sprites and Anisotropic Diffusion
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
Detection and segmentation of moving objects in complex scenes
Computer Vision and Image Understanding
On candidates selection for hysteresis thresholds in edge detection
Pattern Recognition
Short-term motion-based object segmentation
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Robust Global Motion Estimation Oriented to Video Object Segmentation
IEEE Transactions on Image Processing
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
IEEE Transactions on Circuits and Systems for Video Technology
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
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We present an unsupervised motion-based object segmentation algorithm for video sequences with moving camera, employing bidirectional inter-frame change detection. For every frame, two error frames are generated using motion compensation. They are combined and a segmentation algorithm based on thresholding is applied. We employ a simple and effective error fusion scheme and consider spatial error localization in the thresholding step. We find the optimal weights for the weighted mean thresholding algorithm that enables unsupervised robust moving object segmentation. Further, a post processing step for improving the temporal consistency of the segmentation masks is incorporated and thus we achieve improved performance compared to the previously proposed methods. The experimental evaluation and comparison with other methods demonstrate the validity of the proposed method.