CLG for Automatic Image Segmentation

Automatic Multilevel Image Segmentation

An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR) is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized ...

Fuzzy Neighbor Voting for Automatic Image Annotation

With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper,...

K - Means Clustering for Automatic Image Segmentation

Learning Shapes for Automatic Image Segmentation

The detection and precise segmentation of specific objects is an important task in many computer vision and image analysis problems, particularly in medical domains. Existing methods such as template matching typically require excessive computation and user interaction, particularly if the desired objects have a variety of different shapes. This paper presents a new approach that uses unsupervi...

Improved techniques for automatic image segmentation

Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies. The segmentation process is divided into three basic steps, namely: simplif...