AIRCC's International Journal of Computer Science and Information Technology

D. Muhammad Noorul Mubarak ¹, M. Mohamed Sathik ², S. Zulaikha Beevi ³, K. Revathy ¹

Affiliations
1 Department of Computer Science, University of Kerala, Trivandrum, IN
2 Sathakathullah Appa College, Tirunelveli, Tamilnadu, IN
3 Department of IT, National College of Engineering, Tirunelveli, Tamilnadu, IN

Abstract

In this paper, we have made improvements in region growing image segmentation. The First one is seeds select method, we use Harris corner detect theory to auto find growing seeds. Through this method, we can improve the segmentation speed. In this method, we use the Improved Harris corner detect theory for maintaining the distance vector between the seed pixel and maintain minimum distance between the seed pixels. The homogeneity criterion usually depends on image formation properties that are not known to the user. We induced a new uncertainty theory called Cloud Model Computing (CMC) to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. Next to region growing operation, we use canny edge detector to enhance the border of the regions. The method was tested for segmentation on X-rays, CT scan and MR images. We found the method works reliable on homogeneity and region characteristics. Furthermore, the method is simple but robust and it can extract objects and boundary smoothly.

Keywords

Region Growing, Segmentation, Seeds Selection, Homogeneity Criterion, Cloud Model.

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K. Revathy ¹, M. Jayamohan ¹

Affiliations
1 Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, IN

Abstract

Fractal image compression is attractive except for its high encoding time requirements. The image is encoded as a set of contractive affine transformations. The image is partitioned into non-overlapping range blocks, and a best matching domain block larger than the range block is identified. There are many attempts on improving the encoding time by reducing the size of search pool for range-domain matching. But these methods are attempting to prepare a static domain pool that remains unchanged throughout the encoding process. This paper proposes dynamic preparation of separate domain pool for each range block. This will result in significant reduction in the encoding time. The domain pool for a particular range block can be decided based upon a parametric value. Here we use classification based on local fractal dimension.

Keywords

Fractal Image Compression, Dynamic Domain Pool, RMS, Fractal Dimension.

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