Advantages Of Image Segmentation

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IMAGE SEGMENTATION USING REGULARIZED TREE PARTITIONING APPROACHES
Jayagar D
Jeyakumari D
PG Scholar
Department of Electronics and Communication
Associate professor
Department of electronics and communication
RVS college of Engineering and Technology
RVS college of Engineering and Technology
Coimbatore, Tamil Nadu
Coimbatore, Tamil Nadu jayagardreams@gmail.com dgjeyakumari@gmail.com@gmail.com
Abstract - In computer vision and image processing the leading challenge is image segmentation. The image segmentation with partitioning techniques are still under processed, but the results are not as satisfactory as user desire. The arising problems are evacuation, dynamic flow. The Regularized tree partitioning approaches are used to reduce these problems
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It is useful everywhere whenever it is wanted to analyse what is inside an image. As it is already known that the image segmentation identifies the diseases in medical imaging and also in many applications like face and iris detection, fingerprint recognition and in brake light detection technique also this image segmentation technique is used. In image segmentation each technique has its own advantages and also disadvantages, so it is hard to tell which one is better in all the techniques. There are several previous works about the image segmentation, great survey resources can be found from these surveys, separate the image segmentation techniques into three different…show more content…
Digital image processing, improvement for human observers or performing autonomous analysis, offer advantages in cost, speed, flexibility, and with the rapidly falling values and rising performance of personal computers.
II. PRELIMINARIES
Graph-based grouping methods first represent a set of data points in an arbitrary feature space as a weighted undirected graph G = (V, E, W), where a node v ∈ V in the graph Corresponds to a point in the feature space, an edge e ∈ E is formed between a pair of nodes u and v, and the weight w ∈ W on the edge e is a function of the similarity between nodes u and v.
In this paper, the interesting part is segmenting images using a tree structure to represent an image and study the techniques of partitioning the trees under new criteria. In implementation, a maximum weight spanning tree is used to approximate an image graph. A maximum spanning tree is a spanning tree of a weighted graph having the maximum weight. It computed by applying the Kruskal’s

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