What Will We Learn?
- What is image segmentation and why is it relevant?
- What is image thresholding and how is it implemented in MATLAB?
- What are the most commonly used image segmentation techniques and how do they work?
Segmentation is one of the most crucial tasks in image processing and computer vision. As you may recall from our discussion in Chapter 1 (Section 1.5), image segmentation is the operation that marks the transition between low-level image processing and image analysis: the input of a segmentation block in a machine vision system is a preprocessed image, whereas the output is a representation of the regions within that image. This representation can take the form of the boundaries among those regions (e.g., when edge-based segmentation techniques are used) or information about which pixel belongs to which region (e.g., in clustering-based segmentation). Once an image has been segmented, the resulting individual regions (or objects) can be described, represented, analyzed, and classified with techniques such as the ones presented in Chapters 18 and 19.
Segmentation is defined as the process of partitioning an image into a set of nonoverlapping regions whose union is the entire image. These regions should ideally correspond to objects and their meaningful parts, and background. Most image segmentation algorithms are based on one of two basic properties that can be extracted from pixel values—discontinuity and similarity—or ...