Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters

Abstract. In this study, a crater detection system for a large-scale image database is proposed. The original images are grouped according to spatial frequency patterns and both optimized parameter sets and noise reduction techniques used to identify candidate craters. False candidates are excluded using a self-organizing map (SOM) approach. The results show that despite the fact that a accurate classification is achievable using the proposed technique, future improvements in detection process of the system are needed.

Attention mechanisms in the CHREST cognitive architecture

In this paper, we describe the attention mechanisms in CHREST, a computational architecture of human visual expertise. CHREST organises information acquired by direct experience from the world in the form of chunks. These chunks are searched for, and verified, by a unique set of heuristics, comprising the attention mechanism. We explain how the attention mechanism combines bottom-up and top-down heuristics from internal and external sources of information. We describe some experimental evidence demonstrating the correspondence of CHREST’s perceptual mechanisms with those of human subjects. Finally, we discuss how visual attention can play an important role in actions carried out by human experts in domains such as chess

Recognition of Visual Object Classes

Object recognition is both about recognizing specific objects, e.g., "That is my dog Spot." and about recognizing classes of objects, e.g., "That is a dog." Our focus is on the latter problem, even though we do not offer a precise definition for what constitutes a class. In some cases, for example with human faces, the objects in a class are visually similar and form a visual object class. In other cases, say chairs, objects in the class may not look at all alike---the only similarities are in function. Recognition of functional object classes requires higher-level cognitive reasoning, we restrict here our attention to visual object classes. The main difficulty in object recognition is the problem of invariance. The pixel representation provided by the camera is dependent upon the lighting conditions, object pose, camera position, etc. Further, there is inherent variability between different instances from the same object class. Our approach to this problem is to model an object class ..

Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection

Users of digital image libraries are often not interested in image data per se but in derived products such as catalogs of objects of interest. Converting an image database into a usable catalog is typically carried out manually at present. For many larger image databases the purely manual approach is completely impractical. In this paper we describe the development of a trainable cataloging system: the user indicates the location of the objects of interest for a number of training images and the system learns to detect and catalog these objects in the rest of the database. In particular we describe the application of this system to the cataloging of small volcanoes in radar images of Venus. The volcano problem is of interest because of the scale (30,000 images, order of 1 million detectable volcanoes), technical difficulty (the variability of the volcanoes in appearance) and the scientific importance of the problem. The problem of uncertain or subjective ground truth is of fundamental..