Amanjot Kaur

Color Image Segmentation In CIELab Space Using Hill Climbing Algorithm

Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. We contend that this problem can be tackled by mapping the pixels into various feature spaces, whereupon the are subjected to various grouping algorithms In this paper we present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input image.

Approximation of Missing Values in DNA Microarray Gene Expression Data

In the past few years, there has been a detonation of data in the field of biotechnology. Gene expression microarray experiments produce datasets with numerous missing expression values due to various reasons, e.g. insufficient resolution, image corruption, dust or scratches on the slides, or experimental error during the laboratory process.. To improve these missing values, many algorithms for gene expression analysis oblige a complete matrix of gene array values as input, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. Accurate estimation of missing values is an important requirement for efficient data analysis. Main problem of existing methods for microarray data is that there is no external information but the estimation is based exclusively on the expression data. We conjectured that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. Robust missing value estimation methods are required since many algorithms for gene expression analysis entail a complete matrix of gene array values. Either genes with missing values can be removed, or the missing values can be replaced using prediction. Current methods for estimating the missing values include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation methods depends on the actual gene expression has not been thoroughly investigated. Under this setting, we examine how the accuracy depends on the actual expression level and propose new method that provides improvements in accuracy relative to the current methods in certain ranges of gene expression.

Using Cluster Analysis for Protein Secondary Structure Prediction

As biomedical research and healthcare continue to progress in the genomic/post genomic era, a number of important challenges and opportunities exist in the broad area of bioinformatics. In the broader context, the key challenges to bioinformatics essentially all relate to the current flood of raw data, aggregate information, and evolving knowledge arising from the study of the genome and its manifestation. Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms. The experimental methods used to determine the structures of proteins demand sophisticated equipment and time. A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results. The present work focuses on secondary structure prediction of proteins. The data mining model is implemented to predict the various parameters related to the secondary structure. These parameters include the alpha helix, beta sheets and hairpin turn. Cluster analysis is used to implement the secondary structure prediction.

Opportunistic Maximum-Likelihood Channel Assignment in Mesh Based Networks

Wireless mesh innovation is creating itself as entire world under one rooftop. Mesh Networks are proficiently and viably and besides remotely joining urban areas with economical existing innovation. Couple of years back client joined with one another utilizing some wired access focuses or remote hotspots. Stations automatically choose their safest path to travel from one mesh to another. Yet, in today’s reality network innovation is serving many clients at one time crosswise over substantial range. The maximum likelihood has been determining the high priority and free channel to send information to multi-hop mesh based networks. This channel assignment approach is investigating and removing channel fading problem. We have been proposing the notion that recognizes the lapses because of increment the interference in the stations.

Localizing Optic Disk using LBP, SIFT and ICA

arious eye diseases such as diabetic retinopathy and glaucoma are very chronic , they has to be detected in early stage , so that harmful effects of such diseases can be minimized, also biometrics authentication plays a crucial role in daily life activities. So retinal fundus photography is commonly used in above mentioned area of problems. Because of Time-consuming and resource -intensive process, degradation of such images takes place .This paper presents a novel method to automatically localize one such feature: the optic disk. The proposed method consists of various steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means preprocessing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest and we done this feature extraction with the SIFT and LBP algorithm. Initial results on a database of fundus images show that the proposed method is effective and favorable in relation to comparable techniques. The whole simulation result takes place in the MATLAB environment.

Steganography using Social Impact Theory based Optimization (SITO)

a great advancement in science and technology, efficient techniques are needed for the purpose of security and copyright protection of the digital information being transmitted over the internet and for secret data communication. Thus, Steganography solves this purpose which has been used widely. Even though, a Stego-object may be exposed to noise or compression due to which the secret data cannot be extracted correctly at the receivers end, when the transmission occours. This paper presents an efficient image hiding scheme, Social Impact theory based Optimization (SITO). Here, a fitness function is computed based on certain texture properties and entropy of a host image. According to this, the block holding the most relevant fitness value is the place where embedding of the secret data (secret image) is done. Thus, a stego-image is retrieved at the other end, which is not only good in quality but is also able to sustain certain noise and compression attacks during the transmission. The objective function is defined in such a manner that both quality and robustness of the stego image are acceptable, for which the performance analysis parameter values of the stego-image are also determined. The results, when compared with some other data hiding technique show better stego image quality along with distortion tolerance.