Techniques and potential capabilities of multi-resolutional information (knowledge) processing

A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions

Design Considerations in the Development of an Automated Cartographic System

Cartography, the art of producing maps, is an extremely tedious job which is prone to human error and requires many hours for the completion of maps and their digital data bases. Cartography is a classic example of a job that needs to be automated. Through the new advances in image processing and pattern recognition, the automation of this task is made possible with the cartographer acting as a supervisor. This paper reviews current cartographic techniques, and examines design considerations for a fully automated cartographic system. The benefits of such a system would be improvements in speed, flexibility, and accuracy. The role of the cartographer, with such a system, would change to a process supervisor rather than that of a mass data entry

Signal-to-Noise Behavior for Matches to Gradient Direction Models of Corners in Images

Gradient direction models for corners of prescribed acuteness, leg length, and leg thickness are constructed by generating fields of unit vectors emanating from leg pixels that point normal to the edges. A novel FFT-based algorithm that quickly matches models of corners at all possible positions and orientations in the image to fields of gradient directions for image pixels is described. The signal strength of a corner is discussed in terms of the number of pixels along the edges of a corner in an image, while noise is characterized by the coherence of gradient directions along those edges. The detection-false alarm rate behavior of our corner detector is evaluated empirically by manually constructing maps of corner locations in typical overhead images, and then generating different ROC curves for matches to models of corners with different leg lengths and thicknesses. We then demonstrate how corners found with our detector can be used to quickly and automatically find families of polygons of arbitrary position, size and orientation in overhead images

A comparative study on contour-based corner detectors

Abstract Contour-based corner detectors directly or indirectly estimate a significance measure (e.g. curvature) on the points of a planar curve and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed promising detectors. This paper is an attempt to fill this gap. We present the general frame-work of the contourbased corner detection technique and discuss two major issues -curve smoothing and curvature estimation, which have major impacts on the corner detection performance. A number of promising detectors are compared using an automatic evaluation system on a common large dataset. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster

An adaptative method for the smothing of curves edge detection application

We present a new approach to smooth discrete curves . The smoothing is realized by associating portions of regular curves which are defined on each points interval . The originality of the method consists in finding the portions of curves by minimizing the squared error over a restricted neighbourhood around each point . Adding continuity constraints at the junction points, we obtain a direct formulation of the solution . A unique parameter allows to easily control the smoothing amplitude which can be selected between two extreme cases : interpolation or approximation . It seems like a drawer behaviour trying to join points by a curve . He can choose to join each point by a curve or only take into account the global form of the set of points . The method is particularly adapted to fit contours defined on an image and is used as a final step of image segmentation process . The parameter controlling the smoothing amplitude is computed from the value of local gradient magnitude on each pixel .La méthode de lissage de courbes discrètes présentée est fondée sur la minimisation d'un critère d'erreur quadratique appliqué sur des portions jointives de la courbe à traiter. En imposant des contraintes géométriques au niveau des points de jonctions entre intervalles, on aboutit à une formulation directe de la solution. Un paramètre unique permet de façon simple de contrôler la force du lissage qui évolue ainsi entre 2 cas extrêmes: l'interpolation et l'approximation. La méthode simule le comportement d'un scripteur cherchant à unir des points par une courbe, il peut privilégier le passage du tracé par chaque point ou au contraire respecter la forme globale définie par l'ensemble des points. Cette méthode adaptative de lissage est utilisée comme étape finale d'un processus de segmentation d'images, le paramètre contrôlant la force du lissage étant défini à partir du gradient mesuré localement en chaque point

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)