arrow
Return

An improved seeded region growing algorithm

delete1997-10-01
delete261
PRE
AI
A
Andrew Mehnert *
P
Paul Jackway
DOI:10.1016/S0167-8655(97)00131-1delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Recently Adams and Bischof (1994) proposed a novel region growing algorithm for segmenting intensity images. The inputs to the algorithm are the intensity image and a set of seeds - individual points or connected components - that identify the individual regions to be segmented. The algorithm grows these seed regions until all of the image pixels have been assimilated. Unfortunately the algorithm is inherently dependent on the order of pixel processing. This means, for example, that raster order processing and anti-raster order processing do not, in general, lead to the same tessellation. In this paper we propose an improved seeded region growing algorithm that retains the advantages of the Adams and Bischof algorithm fast execution, robust segmentation, and no tuning parameters - but is pixel order independent. (C) 1997 Elsevier Science B.V.
Keywords:
priority queue
seeded region growing
segmentation
watershed segmentation
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Pattern Recognition Letters cover
Pattern Recognition Letters
IF:
3.3
Papers:
8.0K
Citations:
1.6W

Organization

No organization information available
Cited Papers

Cited Papers

Randomized controlled trial of supported employment in England: 2 year follow‐up of the Supported Work and Needs (SWAN) study
err2013-03-12
err0
errOAAI
errMARGARET HESLIN; LOUISE HOWARD; MORVEN LEESE; PAUL McCRONE; CHRISTOPHER RICE; MANUELA JARRETT; TERRY SPOKES; PETER HUXLEY; GRAHAM THORNICROFT
errShare
errSave
errShare
errSave
Mechanism of the reaction of glasses with orthophosphoric acid
err1963-11-01
err0
PREAI
errM. A. Matveev; �. E. Mazo; F. T. Kachur
errShare
errSave