Digital Image Processing

Archana Verma ¹, Rajendra Kumar Bharti ¹, R. K. Singh ²

Affiliations
1 Uttarakhand Technical University, Dehradun, IN
2 ECE Department, B.C.T. Kumaon Engineering College, Dwarahat, Almora, Uttarakhand, IN

Abstract

Sequence assembly is a difficult problem whose importance is continuously growing even recently the cost of sequencing has dramatically decreased. The computer simulation for the evolution of sequence assembly of linear sequences have demonstrated the importance of recombination of blocks of sequences. Repeated cycles of point mutagenesis, recombination and selection should allow in "in vitro" molecular evolution of complex sequences, such as proteins. A method for the reassembly of genes from their random DNA fragments, resulting in "in vitro" recombination is reported by Stemmer and there are some laboratory techniques also available that results in reliable sequencing of approximately 500 nucleotides (500-mers). If we have lots and lots of 500-mers than the original genome (from which these 500-mers may be obtained) sequence will be assembled. All such methods are based on image fragmentation reassembly. In this paper we are proposing a technique that is based on tabular form. In the proposed technique we develop a table, which has four rows and two columns and contained information about the base character and position of the base character in the DNA sequence.

Keywords

Fragment, Genomes, In Vitro, Mutagenesis, Nucleotide, Reassembly, Sequence.

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Neetesh Gupta ¹, R. K. Singh ², P. K. Dey ³

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 ITM University, Gurgaon, Haryana, IN
3 Jodhpur University (Jodhpur), IN

Abstract

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed.. This ever increasing amount of multimedia data creates a need for new stylish methods to get back the information one is looking for. Thus content-based image retrieval attracted many researchers of various fields. Retrieval of Images from Image library using appropriate features extracted from the content of Image is currently an active research area. For the intention of content-based image retrieval (CBIR) an up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is discussed. In this paper we propose A New Approach for Image Retrieval with interactive Relevance feedback based classification by Using Kernel Based Classifier .This Approach is applied to improve retrieval performance. Our aim is to select the most informative images with respect to the query image by ranking the retrieved images. This approach uses suitable feedback to repeatedly train the Histogram Intersection Kernel based Classifier. Proposed Approach retrieves mostly informative and correlated images.

Keywords

CBIR, Relevance Feedback, Color, Texture, Shape Feature Extraction.

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Shiv Kumar ¹, R. K. Singh ², Manish Srivastava ³, Dinesh Varshney ⁴, Vijay K. Chaudhari ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 MATS University, Raipur (C.G.), IN
3 Department of Computer Science and Engineering, Guru Ghansidas Central University, Bilaspur (C.G.), IN
4 Multimedia Regional Centre, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN

Abstract

In this paper titled "A New Approach for Tree-Structured Wavelet Transform based Texture Retrieval Analysis (TRA) by Using MatLab" is define Wavelet transform-based texture analysis, as I found in the different research, uses sub-band energy values as features, but not the order of energy values. In fact, a textured image, after a wavelet decomposition, yields an energy distribution which can be rank ordered with respect to the sub-bands. It has been found that the combination of the sub-band energy value and its ranking order leads to a more efficient texture retrieval mechanism.

Keywords

MatLab7.0, Wavelet Toolbox, Image Processing Toolbox, Algorithm.

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