@proceedings {3303,
	title = {Comparative analysis of spatial patterns of gene expression in Drosophila Melanogaster imaginal discs.},
	year = {2007},
	month = {2007 Apr 21},
	abstract = {<p>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.</p>
},
	author = {Harmon, C and Ahammad, Parvez and Hammonds, Ann S and Weiszmann, Richard and Celniker, Susan E and Sastry, S and Rubin, Gerald M}
}
@proceedings {3304,
	title = {Compressed domain real-time action recognition.},
	year = {2006},
	month = {2006 Oct 3},
	abstract = {<p>We present a compressed domain scheme that is able to recognize and localize actions in real-time. The recognition problem is posed as performing a video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost.</p>
},
	author = {Yeo, C and Ahammad, Parvez and Ramchandran, K and Sastry, S}
}
