Craig C. Reinhart

Specifying heterogeneous suites for vision tasks

To date the vast majority of parallel processing research has been restricted to the use of a single, rapid, homogeneous architecture. The primary reasons for this restriction are the uniformity of computation within the selected application and the availability of machines on which to perform the research. But, restriction one is artificial in that a multitude of applications exist that require nonuniform processing. The computer vision community provides an abundance of nice examples. Furthermore, with the current onslaught of building block components and architectures, restriction two is slowly being pushed aside. The authors describe an approach to analyzing an application with the goal of specifying an appropriate parallel processor architecture that will facilitate efficient application development (hardware and software), execution, and maintenance. Various typical computer vision tasks to illustrate the techniques are also discussed.

Parallel processing for spatial grouping and matching

In this paper, we identify the computational requirements for structural pattern analysis, particularly for the operations of spatial grouping and matching. We describe two such algorithms that are in wide use here at USC and discuss approaches to reducing their execution times via parallel implementation. We provide brief descriptions and results of two research projects geared generally, toward the parallel implementation of computer vision systems and specifically, towards these algorithms.

Issues in parallel tree search for object recognition

The authors describe the parallel implementation of a 3D object recognition algorithm. The algorithm is representative of methods utilized by various computer vision researchers and presents some interesting problems that are generally overlooked by parallel processing researchers that have studied tree search problems. They describe their objectives in developing the parallel implementation and discuss its performance. They also (briefly) discuss the affects that the parallel implementation has on the runtime characteristics of the algorithm.<<ETX>>

Image demosaic via Nevatia-Babu edge detection

A template matching approach is used to demosaic (reconstruct) a full color image from the sparse pixel data captured by a CMOS imager. The proposed method is based on the Nevatia-Babu template-based linear feature extraction algorithm. This approach provides the color accuracy of gradient based algorithms yet reaps the benefit of regularity of processing of bilinear interpolation. In this paper we describe the algorithm (gradient estimation and color interpolation), compare results to other approaches, and present a hardware implementation. The consideration of hardware implementation is especially important as CMOS imagers find their way into low cost devices such as cell phones and other novelty camera applications.

Analog image processing for IVHS applications

Single vehicle roadway departures (SVRD) are serious vehicular accidents that may be avoided through the use of advanced technology. In this paper we briefly describe our Vehicle Lateral Position Measurement System designed to alert the driver of possible SVRD situations. We then describe our testing of the system under a contract awarded by the National Highway Traffic Safety Administration to Rockwell International.