Geoffrey Alec William West

Using symmetry, ellipses, and perceptual groups for detecting generic surfaces of revolution in 2D images

A number of objects in the real world can be described as surfaces of revolution. These are a particular type of generalized cylinder with a straight axis whose 3-D shape is formed by rotating a 2-D plane about the axis. Examples of such objects are vases, many chess pieces, light bulbs, table lamps, etc. This paper describes a number of techniques that can be used to recognize this class of object in a typical cluttered scene under perspective projection. Use is made of the symmetry of the occluding boundary, perceptual grouping of ellipses, 3-D models and the hypothesis that an ellipse is a circle in the real world.

Human saccadic eye movements and tracking by active foveation in log polar space

One of the possible models of the human visual system (HVS) in the computer vision literature has a high resolution fovea and exponentially decreasing resolution periphery. The high resolution fovea is used to extract necessary information in order to solve a vision task and the periphery may be used to detect motion. To obtain the desired information, the fovea is guided by the contents of the scene and other knowledge to position the fovea over areas of interest. These eye movements are called saccades and corrective saccades. A two stage process has been implemented as a mechanism for changing foveation in log polar space. Initially, the open loop stage roughly foveates on the best interest feature and then the closed loop stage is invoked to accurately iteratively converge onto the foveation point. The open loop stage developed for the foveation algorithm is applied to saccadic eye movements and a tracking system. Log polar space is preferred over Cartesian space as: (1) it simultaneously provides high resolution and a wide viewing angle; and (2) feature invariance occurs in the fovea which simplifies the foveation process.

Validation of machine learning techniques: decision trees and finite training set

There has been some recent interest in using machine learning techniques as part of pattern recognition systems. However, little attention is typically given to the validity of the features and types of rules generated by these systems and how well they perform across a variety of features and patterns. We focus on such issues of validity and comparative performance using two different types of decision tree techniques. In addition, we introduce the notion of including legal perturbations of objects in the training set and show that the performance of the resulting classifiers was better than that those trained without such legal constructs in the data selection.

Adaptive depletion for improvement of MPEG video compression

Traditional data compression algorithms for 2D images work using the information theoretic paradigm, attempting to reduce redundant information by as much as possible. However, through the use of a depletion algorithm that takes advantage of characteristics of the human visual system, images can be displayed using only half or a quarter of the original information with no appreciable loss of quality.