3 research outputs found
Optimal multi-scale matching
Abstract The coarse-to-fine search strategy is extensively used in current reported research
Image Matching with Scale Adjustment
In this paper we address the problem of matching two images with two
different resolutions: a high-resolution image and a low-resolution one. The
difference in resolution between the two images is not known and without loss
of generality one of the images is assumed to be the high-resolution one. On
the premise that changes in resolution act as a smoothing equivalent to changes
in scale, a scale-space representation of the high-resolution image is
produced. Hence the one-to-one classical image matching paradigm becomes
one-to-many because the low-resolution image is compared with all the
scale-space representations of the high-resolution one. Key to the success of
such a process is the proper representation of the features to be matched in
scale-space. We show how to represent and extract interest points at variable
scales and we devise a method allowing the comparison of two images at two
different resolutions. The method comprises the use of photometric- and
rotation-invariant descriptors, a geometric model mapping the high-resolution
image onto a low-resolution image region, and an image matching strategy based
on local constraints and on the robust estimation of this geometric model.
Extensive experiments show that our matching method can be used for scale
changes up to a factor of 6
Theorems and algorithms for multiple view geometry with applications to electron tomography
The thesis considers both theory and algorithms for geometric computer vision. The framework of the work is built around the application of autonomous transmission electron microscope image registration.
The theoretical part of the thesis first develops a consistent robust estimator that is evaluated in estimating two view geometry with both affine and projective camera models. The uncertainty of the fundamental matrix is similarly estimated robustly, and the previous observation whether the covariance matrix of the fundamental matrix contains disparity information of the scene is explained and its utilization in matching is discussed. For point tracking purposes, a reliable wavelet-based matching technique and two EM algorithms for the maximum likelihood affine reconstruction under missing data are proposed. The thesis additionally discusses identification of degeneracy as well as affine bundle adjustment.
The application part of the thesis considers transmission electron microscope image registration, first with fiducial gold markers and thereafter without markers. Both methods utilize the techniques proposed in the theoretical part of the thesis and, in addition, a graph matching method is proposed for matching gold markers. Conversely, alignment without markers is disposed by tracking interest points of the intensity surface of the images. At the present level of development, the former method is more accurate but the latter is appropriate for situations where fiducial markers cannot be used.
Perhaps the most significant result of the thesis is the proposed robust estimator because of consistence proof and its many application areas, which are not limited to the computer vision field. The other algorithms could be found useful in multiple view applications in computer vision that have to deal with uncertainty, matching, tracking, and reconstruction. From the viewpoint of image registration, the thesis further achieved its aims since two accurate image alignment methods are suggested for obtaining the most exact reconstructions in electron tomography.reviewe