Performance evaluation in visual surveillance using the F-measure
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Video object relevance metrics for overall segmentation quality evaluation
EURASIP Journal on Applied Signal Processing
An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
Temporally coherent video matting
Graphical Models
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Towards temporally-coherent video matting
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
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In order to complement subjective evaluation of the quality of segmentation masks, this paper introduces a procedure for automatically assessing this quality. Algorithmically computed figures of merit are proposed. Assuming the existence of a perfect reference mask (ground truth), generated manually or with a reliable procedure over a test set, these figures of merit take into account visually desirable properties of a segmentation mask in order to provide the user with metrics that best quantify the spatial and temporal accuracy of the segmentation masks. For the sake of easy interpretation, results are presented on a peaked signal-to-noise ratio-like logarithmic scale.