Daryl T. Lawton

Processing differential image motion

The inference of three-dimensional camera motion parameters and the layout of a scene from image flows becomes particularly simple from a computational point of view if the scene contains depth variations. Under this condition, the differential image motion yields a simple estimate of the translation field lines at image locations corresponding to depth discontinuities in the scene. This in turn facilitates closed-form solutions of camera motion parameters and environmental depth. Our results may have relevance to human motion perception, which also seems to rely on depth variation in processing image motion.

Processing translational motion sequences

Abstract A procedure for processing real world image sequences produced by relative translational motion between a sensor and environmental objects is presented. In this procedure, the determination of the direction of sensor translation is effectively combined with the determination of the displacement of image features and environmental depth. It requires no restrictions on the direction of motion, nor the location and shape of environmental objects. It has been applied successfully to real-world image sequences from several different task domains. The processing consists of two basic steps: feature extraction and search. The feature extraction process picks out small image areas which may correspond to distinguishing parts of environmental objects. The direction of translational motion is then found by a search which determines the image displacement paths along which a measure of feature mismatch is minimized for a set of features. The correct direction of translation will minimize this error measure and also determine the corresponding image displacement paths for which the extracted features match well.

The image understanding environment program

The history of the image understanding environment (IUE) project, a five-year program to develop a common software environment for the development of algorithms and application systems, is reviewed. An overview of some of the data structures that are currently evolving as a specification for the IUE is provided. The ultimate goal of the project is to provide the basic data structures and algorithms that are required to carry state-of-the-art research in image understanding.<<ETX>>

Iconic To Symbolic Processing Using A Content Addressable Array Parallel Processor

We discuss the design of a large scale Content Addressable Array Parallel Processor (CAAPP) for low, medium and high level vision processing. This new architecture combines associative processing with global broadcast and response to and from an array of cells, and array processing via local cellular square neighborhood computation. The capabilities of the CAAPP allow us to close the feedback loop between high level processing and low level processing by supporting communication between different representations of an image. The CAAPP would provide a means of mapping the signal level (iconic) pixel-based representation of an image into a symbolic intermediate level representation suitable for high level vision processing.

Incorporating Content Addressable Array Processors Into Computer Vision Systems

A design is presented for a Content Addressable Array Parallel Processor (CAAPP) which is both practical and feasible. Its practicality stems from an extensive program of research into real applications of content addressability and parallelism. The feasibility of the design stems from development under a set of conservative engineering constraints tied to limitations of VLSI technology. We then describe various procedures for image processing on the CAAPP. The first performs image convolutions very quickly. It is shown that this algorithm can be generalized to perform convolutions with increased mask size with only a moderate reduction in speed. The second uses the CAAPP to quickly and robustly decompose an optic flow field into its rotational and translational components to recover sensor motion parameters. We also briefly describe techniques for associating symbolic descriptions with extracted image structures in the CAAPP.

A Simple Tunable Interest Operator and Some Applications

The extraction and classification of significant points along a contour is fundamental to many image processing tasks. In this paper, we present a simple process for extracting such points with several appealing properties: the operation is developed in terms of contours which are represented discretely; it is completely local and hence suitable for real time operation in vector or parallel processors; and it is tunable to extract significant points at different resolutions of orientation change along a contour. We also describe its use in linear feature extraction and processing restricted cases of environmental motion where the interest operator associates parameterized attributes with extracted image points. Matching features using these attributes allows for significant computational reductions over schemes based upon correlation matching without any loss of robustness, especially for such cases of restricted motion.

Applications Of Translational Motion Processing To Robotics

This report presents a procedure for processing real world image sequences produced by relative translational motion between a sensor and environmental objects. In this procedure, the determination of the direction of sensor translation is effectively combined with the determination of the displacements of image features and environmental depth. It requires no restrictions on the direction of motion, nor the location and shape of environmental objects. Extensions to other cases of motion analysis are considered.