MachineVision_Chapter3

MachineVision_Chapter3 - Chapter 3 Regions A region in an...

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Chapter 3 Regions A region in an image is a group of connected pixels with similar properties. Regions are important for the interpretation of an image because they may correspond to objects in a scene. An image may contain several objects and, in turn, each object may contain several regions corresponding to different parts of the object. For an image to be interpreted accurately, it must be partitioned into regions that correspond to objects or parts of an object. However, due to segmentation errors, the correspondence between regions and objects will not be perfect, and object-specific knowledge must be used in later stages for image interpretation. 3.1 Regions and Edges Consider the simple image shown in Figure 3.1. This figure contains several objects. The first step in the analysis and understanding of this image is to partition the image so that regions representing different objects are explicitly marked. Such partitions may be obtained from the characteristics of the gray values of the pixels in the image. Recall that an image is a two-dimensional array and the values of the array elements are the gray values. Pixels, gray values at specified indices in the image array, are the observations, and all other attributes, such as region membership, must be derived from the gray values. There are two approaches to partitioning an image into regions: region-based segmentation and boundary estimation using edge detection. 73
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74 CHAPTER 3. REGIONS Figure 3.1: This figure shows an image with several regions. Note that regions and boundaries contain the same information because one representation can be derived from the other. In the region-based approach, all pixels that correspond to an object are grouped together and are marked to indicate that they belong to one region. This process is called segmentation. Pixels are assigned to regions using some criterion that distinguishes them from the rest of the image. Two very im- portant principles in segmentation are value similarity and spatial proximity. Two pixels may be assigned to the same region if they have similar intensity characteristics or if they are close to one another. For example, a specific measure of value similarity between two pixels is the difference between the gray values, and a specific measure of spatial proximity is Euclidean distance. The variance of gray values in a region and the compactness of a region can also be used as measures of value similarity and spatial proximity of pixels within a region, respectively. The principles of similarity and proximity come from the assumption that points on the same object will project to pixels in the image that are spatially close and have similar gray values. Clearly, this assumption is not satisfied in many situations. We can, however, group pixels in the image using these simple assumptions and then use domain-dependent knowledge to match regions to object models. In simple situations, segmentation can be done with thresholding and component labeling, as discussed in Chapter 2. Complex
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3.1. REGIONS AND EDGES
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This note was uploaded on 03/16/2011 for the course CSCI 8820 taught by Professor Suchi during the Spring '10 term at UGA.

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MachineVision_Chapter3 - Chapter 3 Regions A region in an...

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