Learning OpenCV

Chapter 7. Histograms and Matching

In the course of analyzing images, objects, and video information, we frequently want to represent what we are looking at as a histogram. Histograms can be used to represent such diverse things as the color distribution of an object, an edge gradient template of an object [Freeman95], and the distribution of probabilities representing our current hypothesis about an object's location. Figure 7-1 shows the use of histograms for rapid gesture recognition. Edge gradients were collected from "up", "right", "left", "stop" and "OK" hand gestures. A webcam was then set up to watch a person who used these gestures to control web videos. In each frame, color interest regions were detected from the incoming video; then edge gradient directions were computed around these interest regions, and these directions were collected into orientation bins within a histogram. The histograms were then matched against the gesture models to recognize the gesture. The vertical bars in Figure 7-1 show the match levels of the different gestures. The gray horizontal line represents the threshold for acceptance of the "winning" vertical bar corresponding to a gesture model.

Histograms find uses in many computer vision applications. Histograms are used to detect scene transitions in videos by marking when the edge and color statistics markedly change from frame to frame. They are used to identify interest points in images by assigning each interest point a "tag" consisting of ...