Robert M. Lougheed

The cytocomputer: A practical pipelined image processor

This paper presents a new architecture for image processing. It consists of a pipeline of identical programmable serial processing stages, referred to as a cytocomputer. Comparisons are made between cytocomputer and parallel array systems. Cytocomputer systems are shown generally to possess the advantages of lower complexity, high bandwidth and greater architectural flexibility. A first generation system is described and examples of processing are illustrated. Finally, current development efforts are described.

A High Speed Recirculating Neighborhood Processing Architecture

The Environmental Research Institute of Michigan (ERIM) has developed the fourth generation of its cellular image processing systems, known as Cytocomputers®. These systems have been developed over the past nine years, primarily for image analysis and machine vision. Other applications have been demonstrated in image enhancement, computer aided design, and signal processing. The new system utilizes the well-proven and mathematically supported neighborhood processing stages with modules for image storage, multiple-image combinations and high-speed image transfer. These modules support the cellular processor stages in an open, extendable architecture which allows enhancement through the addition of modules optimized for particular transformations. This paper first discusses the special requirements of machine vision and image analysis systems and the types of operations required. Next, an overview of the alternative architectures for image processors is presented, along with a discussion of the tradeoffs and criteria which must be weighed in system design. Finally, the new system is described and examples of its operation are discussed.

3-D imaging systems and high-speed processing for robot control

Bin picking by a robot in real time requires the performance of a series of tasks that are beyond the capabilities of commercially available state-of-the-art robotic systems. In this paper, a laser-ranging sensor for real-time robot control is described. This sensor is incorporated into a robot system that has been applied to the bin-picking or random-parts problem. This system contains new technological components that have been developed recently at the Environmental Research Institute of Michigan (ERIM). These components (the 3-D imaging scanner and a recirculating cellular-array pipeline processor) make generalized real-time robot vision a practical and viable technology. This paper describes these components and their implementation in a typical real-time robot vision system application.

Advanced Image-Processing Architectures For Machine Vision

A large number of computer system designs for image analysis have been proposed, and many have been or are being constructed. Although many of them share similar features, the use of proprietary terminology and lack of detailed standardized processing examples in descriptive documentation makes understanding and comparison difficult. Consequently, the a priori estimation of algorithm performance on a given system becomes a combination of ad hoc guesses and somewhat idealized extrapolation. Also, in concentrating on only a portion of the overall data flow, many of these designs have optimized only a part of the system and have reduced the overall price/performance ratio. This paper identifies the fundamentally distinct primitive types of image transformations used in image processing and discusses the characteristics of typical applications and missions. Next, a taxonomy of architectures is presented which enables the various approaches to be unambiguously categorized and evaluated. Several existing designs are used as examples, and the relative merits of each are discussed.

Applying iconic processing in machine vision

Shape-based (iconic) approaches play a vital role in the early stages of a computer vision system. Many computer vision applications require only 2-D information about objects. These applications allow the use of techniques that emphasize pictorial or iconic features. In this chapter we present an iconic approach using morphological image processing as a tool for analyzing images to recover 2-D information. We also briefly discuss a special architecture that allows very fast implementation of morphological operators to recover useful information in diverse applications. We demonstrate the efficacy of this approach by presenting details of an application. We show that the iconic approach offers features that could simplify many tasks in machine vision systems.

An Analysis of Computer Architectural Factors Contributing to Image Processor Capacity

A quantitative analysis is provided of how specific architectural features of image processors enhance the over-all algorithmic capacity of the corresponding vision system. Such features are discussed at a more detailed level than common SISD and MISD architectural categorizations. Metrics are presented to allow comparisons to be made between fundamentally different architectures.

Ridge-flow determination in fingerprint images

The paper describes how, given a ridge-and-valley image of a fingerprint, a flow map can be extracted. The first step is a unique grayscale algorithm based on 3D mathematical morphology to extract the ridge lines under a wide range of conditions. The paper then compares the results of finding the ridge flow using micropatterns, extended templates (on binary and grayscale images), and several well-known, traditional techniques. The question of spatial resolution is also addressed.

Determination of bake time and temperature of painted plastic using mathematical morphology and neural networks

This paper discusses a new automated image analysis technique for inspecting and monitoring changes in plastic bumper surfaces during the paint-baking process. This new technique produces excellent performance, and is appropriate for on-line production monitoring as well as laboratory analysis. The objective of this work was to develop an accurate method for determining the paint bake time and temperature at which parts had been treated. This task was accomplished using mathematical morphology to extract differentiating features from samples collected at three magnifications and sending these feature-vectors to a back-propagation neural network for classification.

Use Of A Parallel Cellular Processing System For Fast Generation Of Perspective Plots

A perspective plot is an important tool used for the interpretation of raw or processed image data. Unfortunately, most developers must give up resolution of their plotted images in order to obtain results in a timely fashion. Conventional plotting methods are computationally intensive, and as a result often limit the size of plotted images to 128 pixels on a side, or less. The technique presented in this paper has been used with favorable results on images as large as 1024 pixels on a side, with processing times of under one second.