Wednesday, May 18, 2005
Reviews on multimodel image matching (thesis)
Titled: Variational methods for multimodal image matching
Thesis by Gerardo Hermosillo Valadez (2002)
Retrieved at: http://rangiroa.essi.fr/rapports/2002/02-these-hermosillo.pdf
This thesis deals with a specific problem in the field of image analysis namely image matching. Image matching is establishing correspondences between points in two different images. Solving this problem is a fundamental prerequisite in understanding and exploiting the contents of images. The term multimodal image matching here means having to establish correspondence information from several sources (such as camera calibration, 3D reconstruction from two or more views, using several modalities e.g. MRI etc).
As matching under varying illumination conditions are difficult, they are other possible approach to the solution of this problem that is by defining meaningful structures in the images, invariant under transformations of grey-level intensities (e.g. edges, corners) and then design low-level methods to extract the structure from the image. Hence, this thesis propose a variational framework for dense multimodal matching, which relies on the computation of global and local statistical dissimilarity measures (calculus variations and partial differential equations) between intensities of corresponding regions.
Research questions:
(a) What should a good matching satisfy?
a. Optical flow constraint
b. Local image differences
c. General block matching
d. Cross-correlation
e. Mutual information
f. Correlation-ratio
(b) Model of transformation or some other constraint allowing to limit the search for possible matches.
a. Search for low-D transformations (e.g. affine, quadratic, spline-interpolation)
b. Statistical similarity
c. Non-rigid (complex) transformation using statistical similarity
d. Block-matching strategies
e. Parametric intensity correlations
The main contributions of his work are listed below:
(a) He proposed a unifying framework for a family of variational problems for multimodal image matching. This framework subsumes block matching algorithmic approaches as well as techniques for non-rigid matching based on the global estimation of the intensity relations.
(b) He formally computed the gradient of local and global statistical dissimilarity measures, which is an essential step in defining and studying the well posedness of their minimization. Contrary to more standard matching terms like intensity differences or the optical flow constraint, these matching terms are non-local, which makes the standard method of the calculus of variations inapplicable.
(c) He showed that the operators defined by the gradients of these criteria satisfy some Lipschitz-continuity conditions which are required for the well posedness of the associating matching flow.
Thesis by Gerardo Hermosillo Valadez (2002)
Retrieved at: http://rangiroa.essi.fr/rapports/2002/02-these-hermosillo.pdf
This thesis deals with a specific problem in the field of image analysis namely image matching. Image matching is establishing correspondences between points in two different images. Solving this problem is a fundamental prerequisite in understanding and exploiting the contents of images. The term multimodal image matching here means having to establish correspondence information from several sources (such as camera calibration, 3D reconstruction from two or more views, using several modalities e.g. MRI etc).
As matching under varying illumination conditions are difficult, they are other possible approach to the solution of this problem that is by defining meaningful structures in the images, invariant under transformations of grey-level intensities (e.g. edges, corners) and then design low-level methods to extract the structure from the image. Hence, this thesis propose a variational framework for dense multimodal matching, which relies on the computation of global and local statistical dissimilarity measures (calculus variations and partial differential equations) between intensities of corresponding regions.
Research questions:
(a) What should a good matching satisfy?
a. Optical flow constraint
b. Local image differences
c. General block matching
d. Cross-correlation
e. Mutual information
f. Correlation-ratio
(b) Model of transformation or some other constraint allowing to limit the search for possible matches.
a. Search for low-D transformations (e.g. affine, quadratic, spline-interpolation)
b. Statistical similarity
c. Non-rigid (complex) transformation using statistical similarity
d. Block-matching strategies
e. Parametric intensity correlations
The main contributions of his work are listed below:
(a) He proposed a unifying framework for a family of variational problems for multimodal image matching. This framework subsumes block matching algorithmic approaches as well as techniques for non-rigid matching based on the global estimation of the intensity relations.
(b) He formally computed the gradient of local and global statistical dissimilarity measures, which is an essential step in defining and studying the well posedness of their minimization. Contrary to more standard matching terms like intensity differences or the optical flow constraint, these matching terms are non-local, which makes the standard method of the calculus of variations inapplicable.
(c) He showed that the operators defined by the gradients of these criteria satisfy some Lipschitz-continuity conditions which are required for the well posedness of the associating matching flow.
# posted by Jacey Lynn Minoi : 12:35 pm
