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Journal ArticleDOI

Parallel Image Processing Using Cellular Arrays

Rosenfeld
- 01 Jan 1983 - 
- Vol. 16, Iss: 1, pp 14-20
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TLDR
The basic techniques of image processing using two-dimensional arrays of processors, or cellular arrays, are reviewed and various extensions and generalizations of the cellular array concept are discussed and their possible implementations and applications are discussed.
Abstract
array computers are not new to image processing, but more refined techniques have led to broader implementations. We can now construct arrays with up to 128 x 128 processors. Nearly 25 years ago, Ungerl\"2 suggested a two-dimensional array of processing elements as a natural computer architecture for image processing and recognition. Ideally, in this approach, each processor is responsible for one pixel, or one element of the image, with neighboring processors responsible for neighboring pix-els. Thus, using hardwired communication between neighboring processors, local operations can be performed on the image, or local image features can be detected in parallel, with every processor simultaneously accessing its neighbors and computing the appropriate function for its neighborhood. Over the last two decades, several machines embodying this concept have been constructed. The Illiac 1113 used a 36 x 36 processor array (the Illiac IV used only an 8 x 8 array) to analyze \"events\" in nuclear bubble-chamber images by examining 36 x 36 \"windows\" of the images. In later machines, such as the CLIP,4 DAP,5 and Mpp,6 arrays of up to 128 x 128 processors were used and were applied blockwise to larger images. This article reviews the basic techniques of image processing using two-dimensional arrays of processors, or cellular arrays. It also discusses various extensions and generalizations of the cellular array concept and their possible implementations and applications. The term cellular array is used because these machines can be regarded as generalizations of bounded cellular automata, which have been studied extensively on a theoretical level. The relative merits of cellular arrays for image processing as compared to other architectures* are not discussed here; but they have been studied extensively for such purposes, on levels from theory to hardware. A cellular array (Figure 1) is a two-dimensional array of processors, or cells, usually rectangular, each of which can directly communicate with its neighbors in the array. Here, for simplicity, I assume that each cell is connected to its four horizontal and vertical neighbors. Each cell on the borders of the array then has only three neighbors, and each cell in the four corners of the array has only two. I also assume that a cell can distinguish its neighbors; i.e., it can send a different message to each neighbor, and when it receives messages from them, it knows which message came from which neighbor. To use a cellular array for image processing, we give …

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Journal ArticleDOI

Optimal image computations on reduced VLSI architectures

TL;DR: A communication-efficient parallel organization with a reduced number of processors is considered for problems in image processing and computer vision and it is shown that while such problems can be solved in O(n) time on a two-dimensional mesh-connected computer with n/sup 2/ processors, they can also be solved on the proposed organization in O-time using n processors only.
Proceedings Article

Varying Diameter and Problem Size in Mesh-Connected Computers.

TL;DR: In this paper, the authors analyzed the time as a function of the mesh diameter and problem size and showed that for many problems, smaller diameters can yield faster algorithms, and that there is a choice of diameter that is simultaneously best for several of these problems.
Journal ArticleDOI

Constraints on the application of 0.5-µm MOSFET's to ULSI systems

TL;DR: The characteristics of a MOSFET with an L eff = 0.5 µm, which is a building element of the next generation of ULSI's, are described from two viewpoints: 1) deviation from the scaling law and 2) system level scaling as discussed by the authors.
Journal ArticleDOI

Computing the Hough Transform on Reconfigurable Meshes

TL;DR: The purpose of this note is to show that this new paradigm can be used for computing the Hough transform—the most widely used technique for detecting lines and curves in binary or grey level images.
References
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Journal ArticleDOI

Design of a Massively Parallel Processor

TL;DR: The massively parallel processor (MPP) as discussed by the authors was designed to process satellite imagery at high rates, achieving 8-bit integer data, addition can occur at 6553 million operations per second (MOPS) and multiplication at 1861 MOPS.
Proceedings ArticleDOI

A computer oriented toward spatial problems

TL;DR: A stored program computer is described which can handle spatial problems by operating directly on information in planar form without scanning or using other techniques for transforming the problem into some other domain.
Journal ArticleDOI

The Illinois Pattern Recognition Computer-ILLIAC III

TL;DR: The Pattern Articulation Unit is the first modular parallel processor which is capable of more reliable visual identification than part analog/part digital preprocessors of much less generality and potential virtuosity and can serve as a prototype to a new generation of parallel computers that will capitalize upon thin film and integrated semiconductor circuitry of the immediate future.
Journal ArticleDOI

Pattern Detection and Recognition

TL;DR: Two types of pattern-processing problems are discussed in this paper and recognition and detection have been successfully carried out on an IBM 704 computer programmed to simulate a spatial computer (a stored-program machine comprised of a master control unit directing a network of logical modules).