Robust recognition of buildings in compressed large aerial scenes

Author

Azencott, Robert ; Durbin, François ; Paumard, José

Author_Institution

DIAM-CMLA, Ecole Normale Superieure, Cachan, France

Volume

1

fYear

1996

fDate

16-19 Sep 1996

Firstpage

617

Abstract

This paper shows how it is possible to recognize and localize objects in compressed images. The compression method we choose is based on the extraction of the quincunx multiscale edges. The edges of the object and the scene are both computed, and then matched using the censored Hausdorff distance. This distance is computed by double truncation of the classical Hausdorff distance. The localization is based on a coarse-to-fine method. Robustness to noise and possible occlusions of the objects is shown. This algorithm is fast on a workstation and we have implemented it on a massively parallel computer, demonstrating real-time feasibility

Keywords

building; data compression; edge detection; feature extraction; image coding; image matching; object recognition; algorithm; buildings; censored Hausdorff distance; classical Hausdorff distance; coarse-to-fine method; compressed images; compressed large aerial scenes; compression method; double truncation; image matching; massively parallel computer; noise robustness; object localization; object occlusions; object recognition; quincunx multiscale edge extraction; real-time feasibility; robust recognition; workstation; Concurrent computing; Image coding; Image edge detection; Image recognition; Image resolution; Layout; Noise robustness; Pixel; Smoothing methods; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1996. Proceedings., International Conference on

Conference_Location

Lausanne

Print_ISBN

0-7803-3259-8

Type

conf

DOI

10.1109/ICIP.1996.560947

Filename

560947