MEMBERS :• Cuya Broncano, Carolina

Victoria• Caycho Francia, Deisy• Diaz chavez, Carmen

Teresa• Rojas Serrano, Jose• Zegarra Calderon, Oscar

Alexis

Max Flow for Image Segmentation

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.

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.

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.

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.

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

algorithms and techniques for image segmentation

• Thresholding

• Edge-Based segmentation

• Region-Based segmentation

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.

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.

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’=(

Conclusions