Efficient Dense Matching for Textured Scenes Using Region Growing

: We describe a simple and e#cient dense matching method based on region growing techniques, which can be applied to a wide range of globally textured images. Our method can deal with non-rigid scenes and large camera motions. First a few highly distinctive features like points or areas are extracted and matched. These initial matches are then used in a correlation-based region growing step which propagates the matches in textured and more ambiguous regions of the images. The implementation of the algorithm is also given and is demonstrated on both synthetic and real image pairs. Key-words: Dense Matching, Region Growing, Correlation #R#esum#e:tsvp# Unite de recherche INRIA Rhone-Alpes 655, avenue de l'Europe, 38330 MONTBONNOT ST MARTIN (France) Telephone : 04 76 61 52 00 - International: +33 4 76 61 52 00 Telecopie : 04 76 61 52 52 - International: +33 4 76 61 52 52 Appariement dense d'images textur#ees par croissance de r#egions R#esum#e : Un algorithme simple et rapide d'app..

Finding the Collineation Between two Projective Reconstructions

The problem of finding the collineation between two 3-D projective reconstructions has been proved to be useful for a variety of tasks such as calibration of a stereo rig and 3-D affine and/or Euclidean reconstruction. Moreover such a collineation may well be viewed as a point transfer method between two image pairs with applications to visually guided robot control. In spite of this potential, methods for properly estimating such a projective transformation have received little attention in the past. In this paper we describe linear, non-linear and robust methods for estimating this transformation. We test the numerical stability of these methods with respect to image noise and to the number of matched points. Finally we briefly describe three applications: stereo image transfer, Euclidean reconstruction, and self calibration of a stereoscopic camera pair