Gray-scale and colour image segmentation via region growing and region merging

This paper introduces image segmentation as an important first task of any image-analysis process. A seeded region-growing and -merging algorithm is created to segment gray-scale and videophone-type colour images. The approach starts with a set of seed pixels, and from these grows regions by appending to each seed pixel those neighbouring pixels that satisfy a certain predicate. Small regions of far-away values are merged into neighbouring regions. Regions of similar value are also merged. Homogeneity functions are introduced for both gray-scale and colour images. The colour-image segmentation approach utilizes the HSI (hue, saturation, intensity) colour space because of its close relation to the way in which people describe the perception of colour.

[1]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  John Knapman,et al.  Hierarchical probabilistic image segmentation , 1994, Image Vis. Comput..

[4]  Jean-Michel Morel,et al.  Variational methods in image segmentation , 1995 .

[5]  Sebastiano B. Serpico,et al.  Region Growing and Merging Techniques for Accurate Image Segmentation , 1989 .

[6]  Linda G. Shapiro,et al.  Image Segmentation Techniques , 1984, Other Conferences.

[7]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[8]  P J Elliott,et al.  Interactive region and volume growing for segmenting volumes in MR and CT images. , 1994, Medical informatics = Medecine et informatique.

[9]  Daniel Crevier Hue-based segmentation of color images , 1993, Proceedings of Canadian Conference on Electrical and Computer Engineering.