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Combining Greyvalue Invariants with Local Constraints for Object Recognition

Cordelia Schmid 1 Roger Mohr 1 
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : This paper addresses the problem of recognizing objects in large image databases. The method is based on local characteristics which are invariant to similarity transformations in the image. These characteristics are computed at automatically detected keypoints using the greyvalue signal. The method therefore works on images such as paintings for which geometry based recognition fails. Due to the locality of the method, images can be recognized being given part of an image and in the presence of occlusions. Applying a voting algorithm and semi-local constraints makes the method robust to noise, scene clutter and small perspective deformations. Experiments show an efficient recognition for different types of images. The approach has been validated on an image database containing 1020 images, some of them being very similar by structure, texture or shape.
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Cordelia Schmid, Roger Mohr. Combining Greyvalue Invariants with Local Constraints for Object Recognition. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR '96), Jun 1996, San Francisco, United States. pp.872--877, ⟨10.1109/CVPR.1996.517174⟩. ⟨inria-00548368⟩



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