Abstract:

Object detection has become an interesting topic in the filed of computer vision. Object information is very important and can be used in robotics, image retrieval and other technical areas. Object detection is a very challenging problem in computer vision, because the position and size of objects in an image must be known by computer itself. When the position and size of objects are not known, the detector should be able to differentiate between the query object and the negative objects. Although many methods and research papers have been presented about how to detect objects, we have chosen a native way to detect objects: contour-based object detection.

The purpose of this thesis is to present one kind of object detector using only contour information. The creation of such a detector could be separated into two parts. Firstly, those contours of the object must be formed and found. We use the result of some interesting point detectors as a starting point, and combine them with a well-known edge detector – “Canny edge detector”. Thus, we can identify the object contours in the image. Secondly, our object detector is formed by inputting one image or inputting a series of positive images. We must deal with them in different ways. If a pure picture is inputted with a specify object, we can only matching it without any training methods. But if several pieces of data are given, we try to use some learning methods such as SVM (support vector machine) to learn the model and classify the object in the image.

Finally, we use the method of “sliding windows” to detect objects in testing stage. We match the model with the query sub-image. By using “Chamfer matching” we can get a probability map of the object. The highest point is the object center detected by our detector. The benefit of the Chamfer matching is that it can efficiently match edges, which suits our purposes. We compare and show the results in different methods and objects.