IEEE Computer Graphics and Applications
Shadow Removal from a Single Image
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Detection of moving cast shadows for object segmentation
IEEE Transactions on Multimedia
Tracking humans using novel optical flow algorithm for surveillance videos
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications and a robust segmentation of motion objects from the static background is generally required. Segmented foreground objects generally include their self shadows as foreground objects since the shadow intensity differs and gradually changes from the background in a video sequence. Moreover, self shadows are vague in nature and have no clear boundaries. To eliminate such shadows from motion segmented video sequences, we propose an algorithm based on inferential statistical one way ANalysis Of VAriance (ANOVA) F test. This statistical model can deal scenes with complex and time varying illuminations without restrictions on the number of light sources and surface orientations. Results obtained with different indoor and outdoor sequences show that algorithm can effectively and robustly detect associated self shadows from segmented frames.