New Types of Keypoints for Detecting Known Objects in Visual Search Tasks

Abstract

Visual exploration of unknown environments is considered a typical and highly important task in intelligent robotics. Although robots with visual capabilities comparable to human skills (e.g. mushroom-picking robots or bird-viewing robots) are apparently unachievable in the near future, but the concept of robots able to search for known objects in unknown surroundings is one of the ultimate goals for machine vision applications. In the scenarios that are currently envisaged, the expectations should be realistically limited. Nevertheless, one can expect that a robot, after a visual presentation of an object of interest, should be able to “learn” it and, subsequently, to detect the same object in complex scenes which may be degraded by typical effects, i.e. partial visibility of the objects (due to occlusions and/or poor illumination) and their unpredictable locations. The purpose of this chapter is to propose a novel mechanism that is potentially useful (it has been confirmed by promising preliminary results) in such applications. Several theories exist explaining the human perception of objects (e.g. Edelman, 1997). Some researchers promote the importance of multiple model views (e.g. Tarr et al., 1997) others (e.g. Biederman, 1987) postulate viewpoint invariants in the form of shape primitives (geons). From all the theories, however, the practical conclusion is that vision systems detecting objects in a human-like manner should use locally-perceived features as the fundamental tool for matching the scene content to the models of known objects. The idea of using local features (keypoints, local visual saliencies, interest points, characteristic points, corner points – several almost equivalent names exist) in machine vision can be traced back to the 80’s (e.g. Moravec, 1983; Harris & Stephens, 1988). Although stereovision and motion tracking were initially the most typical applications, it was later found that the same approach can be used in more challenging tasks (e.g. matching images in order to detect partially hidden objects). A well-known Harris-Plessey operator (Harris & Stephens, 1988) was combined with local descriptors of detected points to solve object recognition problems in which local features from analysed images are matched against a database of images depicting known objects (e.g. Schmid & Mohr, 1995). The intention was to retrieve images containing arbitrarily rotated and partially occluded objects. Subsequently developed keypoint detectors address the issues of scale changes (this was the weakest point of the original detectors) and perspective distortions. Generally, to achieve