Finding Geometric and Relational Structures in An Image

Abstract : We present a method for extracting geometric and relational structures from raw intensity data. On one hand, low-level image processing extracts isolated features. On the other hand, image interpretation uses sophisticated object descriptions in representation frameworks such as semantic networks. We suggest an intermediate-level description between low- and high-level vision. This description is produced by grouping image features into more and more abstract structures. First, we motivate our choice with respect to what should be represented and we stress the limitations inherent with the use of sensory data. Second, we describe our current implementation and illustrate it with various examples.
Document type :
Conference papers
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/inria-00589995
Contributor : Team Perception <>
Submitted on : Tuesday, May 3, 2011 - 8:28:52 AM
Last modification on : Monday, February 25, 2019 - 4:34:18 PM
Long-term archiving on : Friday, November 9, 2012 - 10:20:36 AM

File

HoraudSkordasVeillon90.pdf
Files produced by the author(s)

Identifiers

Collections

CEA | IMAG | INRIA | UGA | DRT | LETI | CEA-GRE

Citation

Radu Horaud, Thomas Skordas, Françoise Veillon. Finding Geometric and Relational Structures in An Image. 1st European Conference on Computer Vision (ECCV'90), Apr 1990, Antibes, France. pp.374--384, ⟨10.1007/BFb0014886⟩. ⟨inria-00589995⟩

Share

Metrics

Record views

555

Files downloads

356