Learning OpenCV

Chapter 9. Image Parts and Segmentation

Parts and Segments

This chapter focuses on how to isolate objects or parts of objects from the rest of the image. The reasons for doing this should be obvious. In video security, for example, the camera mostly looks out on the same boring background, which really isn't of interest. What is of interest is when people or vehicles enter the scene, or when something is left in the scene that wasn't there before. We want to isolate those events and to be able to ignore the endless hours when nothing is changing.

Beyond separating foreground objects from the rest of the image, there are many situations where we want to separate out parts of objects, such as isolating just the face or the hands of a person. We might also want to preprocess an image into meaningful super pixels, which are segments of an image that contain things like limbs, hair, face, torso, tree leaves, lake, path, lawn and so on. Using super pixels saves on computation; for example, when running an object classifier over the image, we only need search a box around each super pixel. We might only track the motion of these larger patches and not every point inside.

We saw several image segmentation algorithms when we discussed image processing in Chapter 5. The routines covered in that chapter included image morphology, flood fill, threshold, and pyramid segmentation. This chapter examines other algorithms that deal with finding, filling and isolating objects and object parts in ...