Abstract
There have been many successful researches on image segmentations that employ Markov Random Field model. However, most of them were interested in two-dimensional MRF, or spatial MRF, and very few researches are interested in three-dimensional MRF model. Generally, ’three-dimensional’ have two meaning, that are spatially three-dimensional and spatio-temporal. In this paper, we especially are interested in segmentations of spatio-temporal images which appears to be equivalent to tracking problem of moving objects such as vehicles etc. For that purpose, by extending usual two-dimensional MRF, we defined a dedicated three-dimensional MRF which we defined as Spatio-Temporal MRF model(S-T MRF). This S-T MRF models a tracking problem by determining labels of groups of pixels by referring to their texture and labeling correlations along the temporal axis as well as the x-y image axes.Although vehicles severely occlude each other in general traffic images,segmentation boundaries of vehicle regions will be determined precisely by this S-T MRF optimizing such boundaries through spatio-temporal images. Consequently, it was proved that the algorithm has performed 95% success of tracking in middle-angle image at an intersection and 91% success in low-angle and front-view images at a highway junction.
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Kamijo, S., Ikeuchi, K., Sakauchi, M. (2001). Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_20
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DOI: https://doi.org/10.1007/3-540-44745-8_20
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