Intelligent multi-sensor integrations

Growth in the intelligence of space systems requires the use and integration of data from multiple sensors. Generic tools are being developed for extracting and integrating information obtained from multiple sources. The full spectrum is addressed for issues ranging from data acquisition, to characterization of sensor data, to adaptive systems for utilizing the data. In particular, there are three major aspects to the project, multisensor processing, an adaptive approach to object recognition, and distributed sensor system integration

Computational themes in applications of visual perception

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76646/1/AIAA-1987-1674-988.pd

Depth from relative normal flows

Most of the depth from image flow algorithms has to rely on either good initial guesses, or some assumptions about the object surfaces to achieve solutions that agree with the physical world. Waxman and Sinha point out that those restrictions can be relaxed if depth is computed from a relative image flow field. Since image flow determination is relatively much more difficult than normal flow determination, it is of interest to develop an algorithm to recover depth from normal flows. In this paper, we have shown that similar results can be obtained from relative normal flow fields as from relative image flow fields. We have implemented a normal flow estimation algorithm, and applied our algorithm to recover depth from intensity images.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28932/1/0000769.pd

A parallel algorithm for determining two-dimensional object positions using incomplete information about their boundaries

Extraction of two-dimensional object locations using current techniques is a computationally intensive process. In this paper a parallel algorithm is presented that can specify the location of objects from edge streaks produced by an edge operator. Best-first searches are carried out in a number of non-interacting and localized edge streak spaces. The outcome of each search is a hypothesis. Each edge streak votes for a single hypothesis; it may also take part in the formation of other hypotheses. A poll of the votes determined the stronger hypotheses. The algorithm can be used as a front end to a visual pattern recognition system where features are extracted from the hypothesized object boundary or from the area localized by the hypothesized boundary.Experimental results from a biomedical domain are presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28103/1/0000551.pd

LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types

Abstract Background The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established. Results We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance. Conclusions Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.http://deepblue.lib.umich.edu/bitstream/2027.42/112789/1/12864_2012_Article_4373.pd