Review algorithms for image segmentation methods

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Review algorithms for image segmentation methods

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Abstract

Image segmentation is one of the key stages in computer vision processes. Segmentation is the task of recognizing objects in an image. The rest of the image is background. Image segmentation algorithms are generally based on one of two basic properties of intensity values, discontinuity and similarity. Image segmentation was, is and will be a major research topic for many image-processing researchers. There is not a single image segmentation algorithm which can give the best result for every image. According to the type of the given image a proper approach is to be chosen to achieve accurate segmentation. This paper presents the various image segmentation algorithms.

Abstract
Introduction
Classification of image segmentation methods
Edge based methods
1. Convolution based edge detection
2. Second derivative based edge detection
Thresholding method
Region based methods
Graph based methods
1. Minimum cut
2. Grab cut
3. s-t min cut algorithm
4. Max-flow/min-cut algorithms
Clustering based methods
1. K-means algorithm (a partitional data clustering algorithm)
2. Fuzzy C-means algorithm, Mean shift algorithm, Expectation maximization (EM)
3. Gaussian mixture modeling (GMM), Hidden Markov Model (HMM)
Model based method
Conclusion
References

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[...] Fig Laplacian operator 4-neighbor 8neighbor The Marr-Hildreth method In this algorithm first the image is smoothed using a Gaussian for noise reduction. Then a twodimensional Laplacian is applied to the smoothed image. In the last step, loop through the result and look for sign changes. If there is a sign change and the slope across the sign change is greater than some threshold, mark it as an edge. Canny Edge Detector The Canny edge detector was released after MarrHildreth edge detector. [...]

[...] The main drawback of the watershed transformation is over segmentation GRAPH BASED METHODS Graphs can effectively be used for image segmentation in which partition the graph into a set of vertices (regions), such that the similarity within the region is high and similarity across the regions is low. Consider a graph G = where V is the set of all nodes or pixels, E is the set of edges connecting the nodes or pixels and defines the similarity among the neighborhood pixels. [...]

[...] IEEE Trans. Pattern Analysis and Machine Intelligence, vol no pp. 1,101-1,113, Nov J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 888–905, August Y. Weiss. Segmentation using Eigenvectors: A Unifying View. Proceedings of the International Conference on Computer Vision pages 975- Boykov Y., and Jolly, M.P Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In ICCV, J.K. Udupa and S. Samaresekera. Fuzzy connectedness and object [...]

[...] The model structure (e.g., the number of hidden states) can be determined by model selection techniques and parameters estimated using maximum likelihood algorithms, e.g., the EM algorithm MODEL BASED METHOD Model-based approaches to image segmentation have widely been applied in 2D and 3D medical image processing Deformable Model In 2D image segmentation, active contours, also known as are parametric curves which one tries to fit to an image, usually to the edges within an image Level Set Method Level sets implicitly define lower dimensional structures such as surfaces via a function for some constant usually fixed to zero. [...]

[...] Image segmentation methods are broadly classified into edge based, thresholding based, region based, graph based, clustering based and model based EDGE BASED METHODS Edge detection is a fundamental tool to detect outlines of an object and boundaries in the image. An edge-detection filter can also be used to improve the appearance of blurred or anti-aliased video streams The result of edge filtering must be post processed to yield useful object descriptions. One typical post processing is edge linking by using Hough Transform. [...]

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