Cutting image (i) overview

Cutting image (i) overview

[Email protected]

Http://blog.csdn.net/zouxy09

The so-called image cutting refers to the gray, color, texture and shape characteristics of the image divided into a number of non-overlapping areas. And make these characteristics in the same region to show similarity, and in different regions show obvious differences. We first outline the basic image cutting method now, and then do some specific understanding and learning of the individual methods.

1. Cutting method based on threshold value

The basic idea of threshold method is to calculate one or more gray threshold values based on the gray feature of image, and compare the gray value of each pixel in the image with the threshold value. Finally, the pixels are divided into appropriate categories based on the results. Therefore, one of the most critical steps in this class of methods is to solve the best gray threshold values according to a criterion function.

2, Edge-based cutting method

The so-called Edge refers to the set of successive pixels on the boundary line of two different regions in the image. Is the reflection of the discontinuity of local feature of image, which reflects the mutation of image characteristics such as grayscale, color and texture.

In general, edge-based cutting method refers to the edge detection based on gray value, it is based on the edge of Gray value will show a step or roof type changes on the basis of the method.

There are obvious differences between the gray values of the pixel points on the edge of the step type, while the roof shape edge is at the turning point where the gray value rises or falls. Based on this feature, it is possible to use differential operators for edge detection. Even with the extremum of the first derivative and the 0 points of the second derivative to determine the edge, the detailed implementation can use the image and template to complete the convolution.

3, Region-based cutting method

These methods are divided into different regions according to the similarity criterion, including seed region growth method, region splitting merging method and watershed method.

The seed region growth method starts from a group of seed pixels representing different growth regions, then merges the eligible pixels in the seed pixel neighborhood into the growth region represented by the seed pixels, and continues the merging process with the newly added pixels as new seed pixels. Until a new pixel matching the criteria is found.

The key of this method is to select the appropriate initial seed pixel and reasonable growth criterion.

The basic idea of the regional division merging method (Gonzalez,2002) is to divide the image randomly into several disjoint regions. These areas are then split or merged in accordance with the relevant guidelines to complete the cutting task. This method is suitable for both grayscale image cutting and texture image cutting.

Watershed method (Meyer,1990) is a kind of cutting method based on topological theory, and its basic idea is to think of images as topological landforms on the Earth, and the gray value of each pixel in the image indicates the altitude of the point. Each local minimum and its affected area is called a catchment basin, while the boundary of the basin forms a watershed. The implementation of the algorithm can simulate the process of flooding, the lowest point of the image is first drowned. Then the water gradually flooded the whole valley. When the water level reaches a certain height, it overflows, and the dam is built where the water overflows. Repeat this process until all the dots on the entire image are submerged. The establishment of a series of dams becomes a watershed for separating each basin. The watershed algorithm has a good response to the weak edge, but the noise in the image causes the watershed algorithm to cut.

4. Cutting method based on graph theory

Such a method associates the problem of image cutting with the minimum cut (min cut) Problem of the graph.

First, the image is mapped to a weighted, g=<vgraph. e>, each node N∈V in the graph corresponds to each pixel in the image. Each edge ∈E is connected to a pair of neighboring pixels, and the weights of the edges represent the non-negative similarity between neighboring pixels in terms of grayscale, color, or texture.

And a cut of the image is a cut of the graph. Each area being cut C∈S corresponds to a sub-diagram in the diagram. The optimal principle of cutting is to make the sub-graph of the partition maintain the most similarity inside. The similarity between the sub-graphs remains minimal. The essence of the method of cutting based on graph theory is to remove specific edges. Divide the graph into several sub-graphs to achieve cutting.

There are graphcut,grabcut and Random Walk , which are based on graph theory.

5. Cutting method based on energy functional

This kind of method mainly refers to the active contour model (active Contourmodel) and the algorithm developed on it, the basic idea is to use continuous curve to express the target edge, and define an energy function to make its independent variable contain the edge curve. Thus the cutting process is transformed into the process of solving the minimum value of the energy functional, which can be solved by solving the function of Euler (Euler). Lagrange) Equation to achieve, the curve position where the energy reaches the hour is the contour of the target. The active contour model can be divided into two categories according to the different curves in the model: the reference active contour model (parametric active Contourmodel) and the geometric Active contour model (geometric active Contour Model).

The reference activity Contour model is based on the Lagrange frame, which expresses the curve directly in the form of the curve. The most representative is the Snake model presented by Kasset A1 (1987) . This kind of model has been successfully applied in the field of early bio-image cutting, but it has many disadvantages such as the influence of the initial contour setting and the difficulty to deal with the change of the topological structure of the curve, and its energy function depends only on the selection of curve parameters, which is independent of the geometrical shape of the object, which limits its further application.

The curve motion process of the geometric active contour model is based on the geometric metric parameter of the curve and not the expression of the curve, so it can deal with the change of topological structure well. and can solve the problem that the reference activity contour model is difficult to solve. The level set method(Osher,1988) is introduced, which greatly promotes the development of the geometric active contour model, so the geometric active contour model is often referred to as the level setting method.

Cutting image (i) overview