Real-Time Iconic Image Processor

Published

March 1, 1985

Author(s)

Ronald Lumia, Michael O. Shneier, Ernest Kent

Abstract

The Sensory-Interactive Robotics Group at the National Bureau of Standards is producing PIPE, a pipelined image-processing engine, for research in low-level machine vision. PIPE processes sequences of images at field rates through a series of point and neighborhood operations. It is divided into a variable number of identical stages, each of which performs an independent set of operations on the image data stored in the stage. A stage control unit determines the sequence of operations performed within a stage on each image. This sequence is easily modified by a host computer during the inter-field interval when all of the stage control units can be totally reconfigured. Image flow through PIPE in several ways. In addition to the (standard pipeline) forward pathway, where an output image is sent to the next stage, an output image can also be sent to the same stage via a recursive pathway and to the previous stage via a retrograde pathway. As a result, PIPE can support relaxation operations, temporal neighborhood operations, and other local operations. Several processing modes are available in PIPE in addition to the usual SIMD mode of pipelined processors. In an MIMD mode, one of several operations is performed on a region of interest which can be defined by the host device or by previous image operations. PIPE also supports variable resolution pyramids where an image is compressed or expanded as it passes between stages.

Proceedings Title

IEEE International Conference on Robotics and Automation

Conference Dates

March 1, 1985

Conference Location

St. Louis, MO

Conference Title

Robotics and Automation

Pub Type

Conferences

Download Paper

Local Download

Keywords

Image Processing, Pipe

Citation

Lumia, R. , Shneier, M. and Kent, E. (1985), Real-Time Iconic Image Processor, IEEE International Conference on Robotics and Automation, St. Louis, MO, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=820228 (Accessed October 31, 2024)