Document Image Analysis for World War II Personal Records

Complete collections of invaluable documents of unique historical and political significance are decaying and at the same time they are virtually inaccessible, necessitating the invention of robust and efficient methods for their conversion into a searchable electronic form. This paper presents the issues encountered and problems addressed in the MEMORIAL project, whose goal is the establishment of a digital document workbench enabling the creation of distributed virtual archives based on documents existing in libraries, archives, museums, memorials, and public record offices. Successful approaches are described in the context of the chosen data class: a variety of typewritten documents containing personal information relating to the presence of individuals in World War II Nazi concentration camps

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

Unpulsed UBV Optical Emission from the Crab Pulsar

Based on observations of the Crab pulsar using the TRIFFID high speed imaging photometer in the UBV bands using the Special Astrophysical Observatory's 6m telescope in the Russian Caucasus, we report the detection of pronounced emission during the so-called `off' phase of emission. Following de-extinction, this unpulsed component of emission is shown to be consistent with a power law with an exponent of alpha = -0.60 +/- 0.37, the uncertainty being dominated by the error associated with the independent CCD photometry used to reference the TRIFFID data. This suggests a steeper power law form than that reported elsewhere in the literature for the total integrated spectrum, which is essentially flat with alpha ~ 0.1, although the difference in this case is only significant at the ~ 2 sigma level. Deeper reference integrated and TRIFFID phase-resolved photometry in these bands in conjunction with further observations in the UV and R region would constrain this fit further.Comment: 26 pages, 2 figures, uses aasms4.sty, accepted for publication in the Astrophysical Journa

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany