Abstract
Object detection is one of the most important problems in computer vision and it is the base for many others, such as navigation, stereo matching and augmented reality. One of the most popular and powerful choices for performing object detection is using keypoint correspondence approaches. Several keypoint detectors and descriptors has already been proposed but they often extract information from the neighborhood of each point individually, without considering the structure and relationship between them. Exploring structural pattern recognition techniques is a powerful way to fill this gap. In this chapter the concept of keygraphs is explored for extracting structural features from regular keypoints. Keygraphs provide more flexibility to the description process and are more robust than traditional keypoint descriptors, such as SIFT and SURF, because they rely on structural information. The results observed in different tests show that this simplicity significantly improves the time performance, while also keeping them highly discriminative. The effectivity of keygraphs is validated by using them to detect objects in real-time applications on a mobile phone.