MachineVision_Chapter3

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

Info icon This preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
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
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
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
Image of page 2
3.1. REGIONS AND EDGES
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern