Image Segmentation Basics:
There are two types of Image Segmentation:
a) Discontinuity Based Approach
b) Similarity Based approach
In the discontinuity based approach we try to segment the image with respect to isolated points, lines and edges. Edge detection algorithms such as sobel, prewitt , canny, lapalcian, LoG operators all fall under this category. For line detection hough transforms are used. For isolated points there are a variety of problem specific methods used.
In the similarity based approach there are several possible methods that can be used all of which give differing results.
1) Thresholding methods
2) Region Growing methods
3) Region Splitting and Merging methods
Thresholding is simply classifying a similar property(say color frequency or color intensity) and segmenting the images into parts or segements based on this property.(Degrees of freedom = 1 since you can only depend on 1 property for similarity check)
Region Growing methods involve taking seed points and then looking at adjacent pixels then classifying those pixels to be part of the segment or not by comparing some property. K-NN is an example of a region growing algorithm.(Degrees of freedom = 2 with the second degree dependent on the space orientation of the image.)
Region Splitting and Merging methods involve a tree based approach to the segmentation problem. First divide an image into k regions and then divide each of the k regions into l regions. Then finally merge regions with similar properties. (Degrees of freedom = 3 because you can split for the first time based on property a, second time based on property b and then merge according to property c)
There are two types of Image Segmentation:
a) Discontinuity Based Approach
b) Similarity Based approach
In the discontinuity based approach we try to segment the image with respect to isolated points, lines and edges. Edge detection algorithms such as sobel, prewitt , canny, lapalcian, LoG operators all fall under this category. For line detection hough transforms are used. For isolated points there are a variety of problem specific methods used.
In the similarity based approach there are several possible methods that can be used all of which give differing results.
1) Thresholding methods
2) Region Growing methods
3) Region Splitting and Merging methods
Thresholding is simply classifying a similar property(say color frequency or color intensity) and segmenting the images into parts or segements based on this property.(Degrees of freedom = 1 since you can only depend on 1 property for similarity check)
Region Growing methods involve taking seed points and then looking at adjacent pixels then classifying those pixels to be part of the segment or not by comparing some property. K-NN is an example of a region growing algorithm.(Degrees of freedom = 2 with the second degree dependent on the space orientation of the image.)
Region Splitting and Merging methods involve a tree based approach to the segmentation problem. First divide an image into k regions and then divide each of the k regions into l regions. Then finally merge regions with similar properties. (Degrees of freedom = 3 because you can split for the first time based on property a, second time based on property b and then merge according to property c)
