Image Chunking: Defining Spatial Building Blocks for Scene Analysis

Item

Title
en_US Image Chunking: Defining Spatial Building Blocks for Scene Analysis
Creator
en_US Mahoney, James V.
Date
2004-10-20T20:02:38Z
Date Available
2004-10-20T20:02:38Z
Date Issued
en_US 1987-08-01
Identifier
en_US AITR-980
uri
http://hdl.handle.net/1721.1/6854
Abstract
en_US Rapid judgments about the properties and spatial relations of objects are the crux of visually guided interaction with the world. Vision begins, however, with essentially pointwise representations of the scene, such as arrays of pixels or small edge fragments. For adequate time-performance in recognition, manipulation, navigation, and reasoning, the processes that extract meaningful entities from the pointwise representations must exploit parallelism. This report develops a framework for the fast extraction of scene entities, based on a simple, local model of parallel computation.sAn image chunk is a subset of an image that can act as a unit in the course of spatial analysis. A parallel preprocessing stage constructs a variety of simple chunks uniformly over the visual array. On the basis of these chunks, subsequent serial processes locate relevant scene components and assemble detailed descriptions of them rapidly. This thesis defines image chunks that facilitate the most potentially time-consuming operations of spatial analysis---boundary tracing, area coloring, and the selection of locations at which to apply detailed analysis. Fast parallel processes for computing these chunks from images, and chunk-based formulations of indexing, tracing, and coloring, are presented. These processes have been simulated and evaluated on the lisp machine and the connection machine.
Extent
en_US 188 p.
11497118 bytes
8961816 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-980
Subject
en_US machine vision
en_US chunking
en_US segmentation
en_US tracing
en_US blobsdetection
en_US image understanding
en_US visual routines
en_US region growing
Item sets
AI Technical Reports (1964 - 2004)