Search results for " matching algorithm"
showing 3 items of 3 documents
Stereo Matching Tecniques for Cloud-top Height Retrieval
2006
This paper presents an ongoing study for the estimation of the cloud-top height by using only geometrical methods. It is based on the hypothesis that an infra-red camera is on board a satellite and pairs of images concern nearly the same scene. Stereo-vision techniques are therefore explored in order to test the methodology for height retrieval and in particular results of several techniques of stereo matching are evaluated. This study includes area-based matching algorithms by implementing the basic versions, without considering any further steps of optimisation to improve the results. Dense depth maps are the final outputs whose reliability is verified by computing error statistics with r…
Comparison of stereo vision techniques for cloud-top height retrieval
2007
This paper presents an ongoing study for the estimation of the cloud-top height by using only geometrical methods. In agreement with some recent studies showing that it is possible to achieve reliable height estimations not only with the classical methods based on radiative transfer, this article includes a comparison of performances of a selected set of vision algorithms devoted to extract dense disparity maps or motion fields from Infra Red stereo image pairs. This collection includes both area-based techniques and an optical flow-based method and the comparison is accomplished by using a set of cloudy scenes selected from the Along-Track Scanning Radiometer (ATSR2) database. The first gr…
Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition
2010
Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerpr…