Image segmentation in computer vision refers to the process of dividing an image into multi-pixel, often irregular, contiguous regions. The resulting groups of pixels are then referred to as 'superpixels'  and can be used to give a different, more salient representation of an image.

Author: Alastair P. Moore
Author: Simon J. D. Prince
Author: Jonathan Warrell

Publication: IEEE Conference on Computer Vision and Pattern Recognition. 2010 | full text (PDF)

Year: 2010

Image parsing remains difficult due to the need to combine local and contextual information when labeling a scene. We approach this problem by using the epitome as a prior over label configurations. Several properties make it suited to this task. First, it allows a condensed patch-based representation. Second, efficient E-M based learning and inference algorithms can be used. Third, non-stationarity is easily incorporated. We consider three existing priors, and show how each can be extended using the epitome.

Author: Jonathan Warrell
Author: Simon J.D. Prince
Author: Alastair P. Moore

Publication: CVPR 2009 Proceedings | full text (PDF)

Year: 2009

Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. In this paper we investigate incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of the object boundaries in the image.

Author: Alistair P. Moore
Author: Simon J.D. Prince
Author: Jonathan Warrell
Author: Umar Mohammed
Author: Graham Jones

Publication: Computer Vision, IEEE 12th International Conference 2009 | full text (PDF)

Year: 2009