max flow - image segmentation
DESCRIPTION
Description of Image SegmentationTRANSCRIPT
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MEMBERS :• Cuya Broncano, Carolina
Victoria• Caycho Francia, Deisy• Diaz chavez, Carmen
Teresa• Rojas Serrano, Jose• Zegarra Calderon, Oscar
Alexis
Max Flow for Image Segmentation
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IMAGE SEGMENTATION
The term “Image segmentation” refers to the partition of an image into a set of regions or categories, which correspond to different objects or parts of objects.
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Neighboring pixels which are in different categories have dissimilar values.
A good segmentation is typically one which:
Pixels in the same category have similar greyscale of multivariate values and form a connected region.
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Segmentation objectives
The first objective is to decompose the image into parts for further analysis.
For example, in the chapter on color, an algorithm was presented for segmenting a human face from a color video image. The segmentation is reliable, provided that the person's clothing or room background does not have the same color components as a human face.
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The second objective of segmentation is to perform a change of representation.
Segmentation objectives
The regions must have the following characteristics:
• Regions of an image segmentation should be uniform and homogeneous with respect to some characteristic, such as gray level, color, or texture
• Region interiors should be simple and without many small holes.
• Adjacent regions of segmentation should have significantly different values with respect to the characteristic on which they are uniform.
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This technology is used in
• Locate tumors and other
pathologies
• Diagnosis, study of anatomical
structure
• Virtual surgery simulation
• Object ,Pedestrian, Face detection
• Locate objects in satellite images
• Face , Fingerprint ,Iris recognition
• Video surveillance
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algorithms and techniques for image segmentation
• Thresholding
• Edge-Based segmentation
• Region-Based segmentation
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Max-Flow/Min-Cut Algorithms
An image segmentation problem can be interpreted as partitioning the image elements (pixels/voxels) into different categories. A Cut of a graph is a partition of the vertices in the graph into two disjoint subsets.
Constructing a graph with an image, we can solve the segmentation problem using techniques for graph cuts in graph theory.
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Undirected Graph
An undirected graph G={V,E} is defined as a set of nodes (vertices V) and a set of undirected edges E that connect the nodes. Assigning each edge e ∈E a weight We, the graph becomes an undirected weighted graph.
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Directed Graph
A directed graph is defined as a set of nodes (vertices V) and a set of ordered set of vertices or directed edges E that connect the nodes. For an edge e = (u,v), u is called the tail of e, v is called the head of e. This edge is different from the edge e’=(
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Conclusions
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