Automatic generation of image-segmentation processes

J. Reinhardt, W. E. Higgins

Research output: Contribution to journalConference article

6 Citations (Scopus)

Abstract

Solutions to real-world image-segmentation problems typically require many image-processing steps. Unfortunately, the user must decide how to construct this sequence of steps for a given problem. So far, no system has been proposed to make the construction of these processes "easy" for the user. As a result, segmentation processes are often laboriously developed by an image-processing expert. We describe a method for automatically generating image-segmentation processes for arbitrary images. Our method uses cue-based image analysis. The user provides problem-specific information via easily defined cues. Two types of cues can be defined: (1) iconic cues, which are image-based and constructed by drawing directly onto the image data; and (2) symbolic cues, which are verbally specified facts. The cues are interpreted to help select image-processing functions. The user need not be an image-processing expert-he must only understand the significance of the specified cues for a particular problem.

Original languageEnglish (US)
Article number413780
Pages (from-to)791-795
Number of pages5
JournalProceedings - International Conference on Image Processing, ICIP
Volume3
DOIs
StatePublished - Jan 1 1994
EventProceedings of the 1994 1st IEEE International Conference on Image Processing. Part 3 (of 3) - Austin, TX, USA
Duration: Nov 13 1994Nov 16 1994

Fingerprint

Image segmentation
Image processing
Image analysis

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

@article{e176c35f0e284637b20a8b0d1e7f3d5b,
title = "Automatic generation of image-segmentation processes",
abstract = "Solutions to real-world image-segmentation problems typically require many image-processing steps. Unfortunately, the user must decide how to construct this sequence of steps for a given problem. So far, no system has been proposed to make the construction of these processes {"}easy{"} for the user. As a result, segmentation processes are often laboriously developed by an image-processing expert. We describe a method for automatically generating image-segmentation processes for arbitrary images. Our method uses cue-based image analysis. The user provides problem-specific information via easily defined cues. Two types of cues can be defined: (1) iconic cues, which are image-based and constructed by drawing directly onto the image data; and (2) symbolic cues, which are verbally specified facts. The cues are interpreted to help select image-processing functions. The user need not be an image-processing expert-he must only understand the significance of the specified cues for a particular problem.",
author = "J. Reinhardt and Higgins, {W. E.}",
year = "1994",
month = "1",
day = "1",
doi = "10.1109/ICIP.1994.413780",
language = "English (US)",
volume = "3",
pages = "791--795",
journal = "Proceedings - International Conference on Image Processing, ICIP",
issn = "1522-4880",

}

Automatic generation of image-segmentation processes. / Reinhardt, J.; Higgins, W. E.

In: Proceedings - International Conference on Image Processing, ICIP, Vol. 3, 413780, 01.01.1994, p. 791-795.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Automatic generation of image-segmentation processes

AU - Reinhardt, J.

AU - Higgins, W. E.

PY - 1994/1/1

Y1 - 1994/1/1

N2 - Solutions to real-world image-segmentation problems typically require many image-processing steps. Unfortunately, the user must decide how to construct this sequence of steps for a given problem. So far, no system has been proposed to make the construction of these processes "easy" for the user. As a result, segmentation processes are often laboriously developed by an image-processing expert. We describe a method for automatically generating image-segmentation processes for arbitrary images. Our method uses cue-based image analysis. The user provides problem-specific information via easily defined cues. Two types of cues can be defined: (1) iconic cues, which are image-based and constructed by drawing directly onto the image data; and (2) symbolic cues, which are verbally specified facts. The cues are interpreted to help select image-processing functions. The user need not be an image-processing expert-he must only understand the significance of the specified cues for a particular problem.

AB - Solutions to real-world image-segmentation problems typically require many image-processing steps. Unfortunately, the user must decide how to construct this sequence of steps for a given problem. So far, no system has been proposed to make the construction of these processes "easy" for the user. As a result, segmentation processes are often laboriously developed by an image-processing expert. We describe a method for automatically generating image-segmentation processes for arbitrary images. Our method uses cue-based image analysis. The user provides problem-specific information via easily defined cues. Two types of cues can be defined: (1) iconic cues, which are image-based and constructed by drawing directly onto the image data; and (2) symbolic cues, which are verbally specified facts. The cues are interpreted to help select image-processing functions. The user need not be an image-processing expert-he must only understand the significance of the specified cues for a particular problem.

UR - http://www.scopus.com/inward/record.url?scp=84999837352&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84999837352&partnerID=8YFLogxK

U2 - 10.1109/ICIP.1994.413780

DO - 10.1109/ICIP.1994.413780

M3 - Conference article

AN - SCOPUS:84999837352

VL - 3

SP - 791

EP - 795

JO - Proceedings - International Conference on Image Processing, ICIP

JF - Proceedings - International Conference on Image Processing, ICIP

SN - 1522-4880

M1 - 413780

ER -