Analyzing DNA Sequences Using Clustering Algorithm

Data mining gives a bright prospective in DNA sequences analysis through its concepts and techniques. This study carries out exploratory data analysis method to cluster DNA sequences.Feature vectors have been developed to map the DNA sequences to a twelve-dimensional vector in the space. Lysozyme, Myoglobin and Rhodopsin protein families have been tested in this space. The results of DNA sequences comparison among homologous sequences give close distances between their characterization vectors which are easily distinguishable from non-homologous in experiment it with a fixed DNA sequence size that does not exceed the maximum length of the shortest DNA sequence. Global comparison for multiple DNA sequences simultaneously presented in the genomic space is the main advantage of this work by applying direct comparison of the corresponding characteristic vectors distances. The novelty of this work is that for the new DNA sequence, there is no need to compare the new DNA sequence with the whole DNA sequences length, just the comparison focused on a fixed number of all the sequences in a way that does not exceed the maximum length of the new DNA sequence. In other words, parts of the DNA sequence can identify the functionality of the DNA sequence, and make it clustered with its family members

Accurate Corner Detection Methods using Two Step Approach

Many image features are proved to be good candidates for recognition. Among them are edges, lines, corners, junctions or interest points in general. Importance of corner detection in digital images is increasing with increasing work in computer vision in imagery. One of the most promising techniques is the one based on Harris corner detection method. This work describes different approaches to detect corner in efficient way. Based on the works carried out by Harris method, the authors have worked upon increasing efficiency using edge detection methods on image, along with applying the Harris on this pre-processed image. Most of the time, such a step is performed as one of the first steps upon which more complicated algorithm rely. Hence, good outcome of such an operation influences the whole vision channel. This paper contains a quantitative comparison of three such modified techniques using Sobel2013;Harris, Canny-Harris and Laplace-Harris with Harris operator on the basis of distances computed by these methods from user detected corners

Corner detection of contour images using spectral clustering

Corner detection plays an important role in object recognition and motion analysis. In this paper, we propose a hierarchical corner detection framework based on spectral clustering (SC). The framework consists of three stages: contour smoothing, corner cell extraction and corner localization. In the contour smoothing stage, wavelet decomposition is imposed on the raw contour to reduce noise. In the corner cell extraction stage, several atomic corner cells are obtained by SC. In the corner localization stage, the corner points of each corner cell are located by the corner locator based on the kernel-weighted cosine curvature measure. Experimental results demonstrate the superiority of our framework.Xi Li, Weiming Hu, Zhongfei Zhan