Object based segmentation of video using variational level sets
Machine Graphics & Vision International Journal
A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Unsupervised performance evaluation of image segmentation
EURASIP Journal on Applied Signal Processing
Video object segmentation and tracking using region-based statistics
Image Communication
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
EURASIP Journal on Advances in Signal Processing
Fast Object Tracking in Intelligent Surveillance System
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Trajectory tree as an object-oriented hierarchical representation for video
IEEE Transactions on Circuits and Systems for Video Technology
Spatiotemporal region enhancement and merging for unsupervized object segmentation
Journal on Image and Video Processing
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Filling the gap in quality assessment of video object tracking
Image and Vision Computing
Rough sets and neural networks based aerial images segmentation method
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Finite asymmetric generalized Gaussian mixture models learning for infrared object detection
Computer Vision and Image Understanding
A new evaluation measure for color image segmentation based on genetic programming approach
Image and Vision Computing
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We propose measures to evaluate quantitatively the performance of video object segmentation and tracking methods without ground-truth (GT) segmentation maps. The proposed measures are based on spatial differences of color and motion along the boundary of the estimated video object plane and temporal differences between the color histogram of the current object plane and its predecessors. They can be used to localize (spatially and/or temporally) regions where segmentation results are good or bad; and/or they can be combined to yield a single numerical measure to indicate the goodness of the boundary segmentation and tracking results over a sequence. The validity of the proposed performance measures without GT have been demonstrated by canonical correlation analysis with another set of measures with GT on a set of sequences (where GT information is available). Experimental results are presented to evaluate the segmentation maps obtained from various sequences using different segmentation approaches.