9,238 research outputs found
A multi-view approach to cDNA micro-array analysis
The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research
Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences
under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China
under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany
On Geometric Alignment in Low Doubling Dimension
In real-world, many problems can be formulated as the alignment between two
geometric patterns. Previously, a great amount of research focus on the
alignment of 2D or 3D patterns, especially in the field of computer vision.
Recently, the alignment of geometric patterns in high dimension finds several
novel applications, and has attracted more and more attentions. However, the
research is still rather limited in terms of algorithms. To the best of our
knowledge, most existing approaches for high dimensional alignment are just
simple extensions of their counterparts for 2D and 3D cases, and often suffer
from the issues such as high complexities. In this paper, we propose an
effective framework to compress the high dimensional geometric patterns and
approximately preserve the alignment quality. As a consequence, existing
alignment approach can be applied to the compressed geometric patterns and thus
the time complexity is significantly reduced. Our idea is inspired by the
observation that high dimensional data often has a low intrinsic dimension. We
adopt the widely used notion "doubling dimension" to measure the extents of our
compression and the resulting approximation. Finally, we test our method on
both random and real datasets, the experimental results reveal that running the
alignment algorithm on compressed patterns can achieve similar qualities,
comparing with the results on the original patterns, but the running times
(including the times cost for compression) are substantially lower
Roadmap on optical security
Postprint (author's final draft
Kannada Character Recognition System A Review
Intensive research has been done on optical character recognition ocr and a
large number of articles have been published on this topic during the last few
decades. Many commercial OCR systems are now available in the market, but most
of these systems work for Roman, Chinese, Japanese and Arabic characters. There
are no sufficient number of works on Indian language character recognition
especially Kannada script among 12 major scripts in India. This paper presents
a review of existing work on printed Kannada script and their results. The
characteristics of Kannada script and Kannada Character Recognition System kcr
are discussed in detail. Finally fusion at the classifier level is proposed to
increase the recognition accuracy.Comment: 12 pages, 8 figure
Robust density modelling using the student's t-distribution for human action recognition
The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE
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