The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years.
This paper proposes a new indicator of text structure, called the lexical cohesion profile (LCP), which locates segment boundaries in a text. A text segment is a coherent scene; the words in a segment a~e linked together via lexical cohesion relations. LCP records mutual similarity of words in a sequence of text. The similarity of words, which represents their cohesiveness, is computed using a semantic network. Comparison with the text segments marked by a number of subjects shows that LCP closely correlates with the human judgments.
Computer vision is one of the most studied subjects of recent times with paramount
focus on stereo vision. Lot of activities in the context of stereo vision are getting
reported spanning over vast research spectrum including novel mathematical ideas,
new theoretical aspects, state of the art techniques and diverse range of applications.
4. SEGMENTATION AND EDGE DETECTION
4.1 Region Operations
Discovering regions can be a very simple exercise, as illustrated in 4.1.1. However, more often than not, regions are required that cover a substantial area of the scene rather than a small