TAILIEUCHUNG - Lecture Digital image processing - Lecture 28: Image segmentation

This chapter presents the following content: Similarity base image segmentation, image segmentation by thresholding, there are two main approaches to region-based segmentation, region growing, region splitting and merging, texture based segmentation, color based. | Digital Image Processing CSC331 Image Segmentation 1 Summery of previous lecture Similarity base Image Segmentation Image Segmentation by thresholding Global threshold Adaptive/Dynamic threshold Local threshold 2 Todays lecture There are two main approaches to region-based segmentation: Region growing Region splitting and merging Texture based segmentation Color based 3 Region-Based Segmentation Edges and thresholds sometimes do not give good results for segmentation. Region-based segmentation is based on the connectivity of similar pixels in a region. Each region must be uniform. Connectivity of the pixels within the region is very important. There are two main approaches to region-based segmentation: region growing and region splitting. Working of Region growing Start from a set of seed points and from these points grow the regions by appending to each seed those neighbouring pixels that have similar properties The selection of the seed points depends on the problem. When a priory information is not available, clustering techniques can be used: compute the above mentioned properties at every pixel and use the centroids of clusters The selection of similarity criteria depends on the problem under consideration and the type of image data that is available Descriptors must be used in conjunction with connectivity (adjacency) information Formulation of a “stopping rule”. Growing a region should stop when no more pixels satisfy the criteria for inclusion in that region. When a model of the expected results is partially available, the consideration of additional criteria like the size of the region, the likeliness between a candidate pixel and the pixels grown so far, and the shape of the region can improve the performance of the algorithm. 5 To conclude 6 7 8 9 10 11 Region-Based Segmentation Region Growing Region-Based Segmentation Region Growing Fig. shows the histogram of Fig. (a). It is difficult to segment the defects by thresholding methods. .

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• Digital image processing
• Lecture Digital image processing
• Digital images
• Digital computers
• Image processing
• Image segmentation
• Region growing
• Local processing
• Global processing
• Morphological image processing
• Autonomous machine applications
• Image digitization
• Theory of sampling
• Image transformation
• Image enhancement
• Image restoration
• Image transform
• Image compression
• Dark image