This research theme is concerned with the problem of low level image segmentation, or partitioning an image into regions, that represent low level image structure. A region is characterized as possessing a certain degree of interior homogeneity and a contrast with the surround which is large compared to the interior variation. This is a satisfactory characterization from both perceptual and quantitative viewpoints. Homogeneity and contrast may be defined differently: A region may be uniform, in which case its contrast with the surround must be large; alternatively, a region may be shaded, in which case the local contrast across a boundary point must be large compared to the interior variation on each side. The sizes, shapes, types of homogeneity, and contrast values of regions in an image are a priori unknown. The goal is the accurate detection of regions without using rigid, geometric, and photometric models and automatic estimation of all scales associated with an image.
- N. Ahuja, A Transform for Multiscale Image Segmentation by Integrated Edge and Region Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 18, No. 12, December 1996, 1211-1235.
- M. Tabb and N. Ahuja, Multiscale Image Segmentation by Integrated Edge and Region Detection, IEEE Transactions on Image Processing, Vol. 6, No. 5, May 1997, 642-655.
- Himanshu Arora, Narendra Ahuja, Analysis of ramp discontinuity model for multiscale image segmentation, ICPR(1), 2006.
- E. Akbas and N. Ahuja, From ramp discontinuities to segmentation tree, 9th Asian Conference on Computer Vision (ACCV), Xi’an, China, September 2009.