Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
An active lattice model in a Bayesian framework
Computer Vision and Image Understanding
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking of Tagged MR Images by Bayesian Analysis of a Network of Quads
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Recognition of perspectively distorted planar grids
Pattern Recognition Letters
Microarray image gridding with stochastic search based approaches
Image and Vision Computing
Analysis of Building Textures for Reconstructing Partially Occluded Facades
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A deformable grid approach for Bayesian image registration
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Constructing the histogram representation for automatic gridding of cDNA microarray images
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
A new method for gridding DNA microarrays
Computers in Biology and Medicine
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A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov Random Field (MRF) model which represents the spatial coordinates of the grid nodes. Knowledge of how grid nodes are depicted in the observed image is described through the observation model. The prior consists of a node prior and an arc (edge) prior, both modeled as Gaussian MRFs. The node prior models variations in the positions of grid nodes and the arc prior models variations in row and column spacing across the grid. Grid matching is done by placing an initial rough grid over the image and applying an ensemble annealing scheme to maximize the posterior distribution of the grid. The method can be applied to noisy images with missing grid nodes and grid-node artifacts and the method accommodates a wide range of grid distortions including: large-scale warping, varying row/column spacing, as well as nonrigid random fluctuations of the grid nodes. The methodology is demonstrated in two case studies concerning 1) localization of DNA signals in hybridization filters and 2) localization of knit units in textile samples.