Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Learning from one example in machine vision by sharing probability densities
Learning from one example in machine vision by sharing probability densities
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
DNA Array Image Analysis: Nuts & Bolts (Nuts & Bolts series)
DNA Array Image Analysis: Nuts & Bolts (Nuts & Bolts series)
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Automating Gene Expression Annotation for Mouse Embryo
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.