A new Compression-Oriented Fast Image Segmentation Technique
Date Issued
2009
Date
2009
Author(s)
Kuo, Cheng-Jin
Abstract
In this thesis, we define the role of image segmentation as the front-stage processing of the image compression. Base on this, we hope there is an image segment algorithm with three advantages which are the fast speed, the good shape connectivity of its segmenting result, and the good shape matching.In chapter 2, we introduce some region-based image segmenting algorithm, including hierarchical data clustering, partitional data clustering, region growing algorithm, and splitting and merging algorithm.In chapter 3, we introduce the edge-based image segmenting algorithm and take the watershed algorithm as an example.In chapter 4, we introduce the improvement of the Fourier descriptor. It helps us to realize the importance of recording the boundary of an image shape.In the front of chapter 5, we compare the three algorithms below, Seeds Region Growing, K-means algorithm, and Watershed algorithm. We compare them and discuss the advantages and the drawbacks of them base on to be the compression-oriented image segmenting algorithm. The region growing algorithm is very reliable in shape matching with the good connectivity of its segmenting result, but it is too slow for us to use it. The K-means algorithm is very fast, but its segmenting result is fragmentary. It is very inconvenient for us to record the boundaries of the results after we sent the results into the compression stage. The watershed algorithm is widely use in image segmenting, but it is also famous, and maybe, notorious for the over-segmentation result. n the end of chapter 5, we propose and introduce our image segmenting algorithm in details. Our algorithm owns the advantages of the fast speed, the good shape connectivity with its segmenting results, and the not bad shape matching. n chapter 6, moreover, we improve our algorithm by using the adaptive threshold selecting with the local variance and the frequency. It help us to segment the image with suitable threshold value and improve the shape matching of our image segmenting method.here are conclusions and future work in chapter 7.ay this thesis be helpful for you.
Subjects
image processing
fast image segmentation
edge detection
fourier descriptor
Type
thesis
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