IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner detection and curve representation using cubic B-spline
Computer Vision, Graphics, and Image Processing
Determination of Camera Location from 2-D to 3-D Line and Point Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding geometric and relational structures in an image
ECCV 90 Proceedings of the first european conference on Computer vision
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Data-driven registration for local deformations
Pattern Recognition Letters
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive-Scale Filtering and Feature Detection Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Point Correspondence Applied to Two-and Three-Dimensional Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The Trimmed Iterative Closest Point Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Matching of medical images by self-organizing neural networks
Pattern Recognition Letters
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Analysis of Articulated Objects from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structure from Motion with Wide Circular Field of View Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
A region-based multi-sensor image fusion scheme using pulse-coupled neural network
Pattern Recognition Letters
Multiscale contour corner detection based on local natural scale and wavelet transform
Image and Vision Computing
Fast algorithm for robust template matching with M-estimators
IEEE Transactions on Signal Processing
Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Mathematical and Computer Modelling: An International Journal
Establishing the correspondence between control points in pairs of mammographic images
IEEE Transactions on Image Processing
Fast block matching algorithm based on the winner-update strategy
IEEE Transactions on Image Processing
A comparative study of transformation functions for nonrigid image registration
IEEE Transactions on Image Processing
Self-organizing nets for optimization
IEEE Transactions on Neural Networks
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
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In this paper, a generalized application of Kohonen Network for automatic point correspondence of unimodal medical images is presented. Given a pair of two-dimensional medical images of the same anatomical region and a set of interest points in one of the images, the algorithm detects effectively the set of corresponding points in the second image, by exploiting the properties of the Kohonen self organizing maps (SOMs) and embedding them in a stochastic optimization framework. The correspondences are established by determining the parameters of local transformations that map the interest points of the first image to their corresponding points in the second image. The parameters of each transformation are computed in an iterative way, using a modification of the competitive learning, as implemented by SOMs. The proposed algorithm was tested on medical imaging data from three different modalities (CT, MR and red-free retinal images) subject to known and unknown transformations. The quantitative results in all cases exhibited sub-pixel accuracy. The algorithm also proved to work efficiently in the case of noise corrupted data. Finally, in comparison to a previously published algorithm that was also based on SOMs, as well as two widely used techniques for detection of point correspondences (template matching and iterative closest point), the proposed algorithm exhibits an improved performance in terms of accuracy and robustness.