tailieunhanh - Ebook Object detection and recognition in digital images: Part 2

(BQ) Part 1 book "Object detection and recognition in digital images" has contents: Object detection and tracking, object recognition, recognition based on tensor decompositions, recognition from deformable models, template based recognition, template based recognition. | 4 Object Detection and Tracking Introduction This section is devoted to selected problems in object detection and tracking. Objects in this context are characterized by their salient features, such as color, shape, texture, or other traits. Then the problem is telling whether an image contains a defined object and, if so, then indicating its position in an image. If instead of a single image a video sequence is processed, then the task can be to track, or follow, the position and size of an object seen in the previous frame and so on. This assumes high correlation between consecutive frames in the sequence, which usually is the case. Eventually, an object will disappear from the sequence and the detection task can be started again. Detection can be viewed as a classification problem in which the task is to tell the presence or absence of a specific object in an image. If it is present, then the position of the object should be provided. Classification within a group of already detected objects is usually stated separately, however. In this case the question is formulated about what particular object is observed. Although the two groups are similar, recognition methods are left to the next chapter. Thus, examples of object detection in images are, for instance, detection of human faces, hand gestures, cars, and road signs in traffic scenes, or just ellipses in images. On the other hand, if we were to spot a particular person or a road sign, etc. we would call this recognition. Since detection relies heavily on classification, as already mentioned, one of the methods discussed in the previous section can be used for this task. However, not least important is the proper selection of features that define an object. The main goal here is to choose features that are the most characteristic of a searched object or, in other words, that are highly discriminative, thus allowing an accurate response of a classifier. Finally, computational complexity of the methods is also .