Parvinder S. Sandhu

Performance Evaluation of Edge Detection Techniques for Images in Spatial Domain

Abstract —Edges characterize boundaries and are therefore considered for prime importance in image processing. Edge detection filters out useless data, noise and frequencies while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection methods. In this paper the comparative analysis of various Image Edge Detection methods is presented. The evidence for the best detector type is judged by studying the edge maps relative to each other through statistical evaluation. Upon this evaluation, an edge detection method can be employed to characterize edges to represent the image for further analysis and implementation. It has been shown that the Canny’s edge detection algorithm performs better than all these operators under almost all scenarios. Index Terms —About four key words or phrases in alphabetical order, separated by commas. I. I NTRODUCTION

RFID Technology Principles, Advantages, Limitations & Its Applications

This paper gives an overview of the current state of radio frequency identification (RFID) technology. Aside from a brief introduction to the principles of the technology, major current and envisaged fields of application, as well as advantages, and limitations of use are discussed. Radio frequency identification (RFID) is a generic term that is used to describe a system that transmits the identity (in the form of a unique serial number) of an object or person wirelessly, using radio waves. Its grouped under the broad category of automatic identification technologies. RFID is increasingly used with biometric technologies for security. In this paper Basic Principles of RFID technology along with its types are discussed.

Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption

This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools.

An Improved LSB Based Steganography Technique for RGB Color Images

This paper proposes an improved LSB(least Significant bit) based Steganography technique for images imparting better information security .It presents an embedding algorithm for hiding encrypted messages in nonadjacent and random pixel locations in edges and smooth areas of images. It first encrypts the secret message, and detects edges in the cover-image using improved edge detection filter. Message bits are then, embedded in the least significant byte of randomly selected edge area pixels and 1-3-4 LSBs of red, green, blue components respectively across randomly selected pixels across smooth area of image. It ensures that the eavesdroppers will not have any suspicion that message bits are hidden in the image and standard steganography detection methods can not estimate the length of the secret message correctly. The Proposed approach is better in PSNR value and Capacity as shown experimentally than existing techniques.

A New Approach for Information Security using an Improved Steganography Technique

Abstract —This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB’s (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.

Design and Development of an Improved Adaptive Median Filtering Method for Impulse Noise Detection

An Impulse noise detection & removal with adaptive filtering approach is proposed to restore images corrupted by salt & pepper noise. The proposed algorithm works well for suppressing impulse noise with noise density from 5 to 60% while preserving image details. The difference of current central pixel with median of local neighborhood pixels is used to classify the central pixel as noisy or noise-free. The noise is attenuated by estimating the values of the noisy pixels with a switching based median filter applied exclusively to those neighborhood pixels not labeled as noisy. The size of filtering window is adaptive in nature, and it depends on the number of noise-free pixels in current filtering window. Simulation results indicate that this filter is better able to preserve 2-D edge structures of the image and delivers better performance with less computational complexity as compared to other denoising algorithms existing in literature.

Improved LSB based Steganography Techniques for Color Images in Spatial Domain

This research paper aims to propose a new improved approach for Information Security in RGB Color Images using a Hybrid Feature detection technique; Two Component based Least Significant Bit (LSB) Substitution Technique and Adaptive LSB substitution technique for data hiding. Advanced Encryption Standard (AES) is used to provide Two Tier Security; Random Pixel Embedding imparts resistant to attacks and Hybrid Filtering makes it immune to various disturbances like noise. An image is combination of edge and smooth areas which gives an ample opportunity to hide information in it. The proposed work is direct implementation of the principle that edge areas being high in contrast, color, density and frequency can tolerate more changes in their pixel values than smooth areas, so can be embedded with a large number of secret data while retaining the original characteristics of image. The proposed approach achieved Improved Imperceptibility, Capacity than the various existing techniques along with Better Resistance to various Steganalysis attacks like Histogram Analysis, Chi-Square and RS Analysis as proven experimentally.

An Improvement in Apriori Algorithm Using Profit and Quantity

Association rule mining has been an area of active research in the field of knowledge discovery and numerous algorithms have been developed to this end. Of late, data mining researchers have improved upon the quality of association rule mining for business development by incorporating the influential factors like value (utility), quantity of items sold (weight) and more, for the mining of association patterns. In this paper, we propose an efficient approach based on weight factor and utility for effectual mining of significant association rules. Initially, the proposed approach makes use of the traditional Apriori algorithm to generate a set of association rules from a database. The proposed approach exploits the anti-monotone property of the Apriori algorithm, which states that for a k-itemset to be frequent all (k-1) subsets of this itemset also have to be frequent. Subsequently, the set of association rules mined are subjected to weight age (W-gain) and utility (U-gain) constraints, and for every association rule mined, a combined Utility Weighted Score (UW-Score) is computed. Ultimately, we determine a subset of valuable association rules based on the UW-Score computed. The experimental results demonstrate the effectiveness of the proposed approach in generating high utility association rules that can be lucratively applied for business development.

A Neuro-Fuzzy Based Software Reusability Evaluation System with Optimized Rule Selection

There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. We critically analyzed the CK metrics, tried to remove the inconsistencies and devised neuro-fuzzy framework that gets input in form of tuned WMC, DIT, NOC, CBO, LCOM values of a software component and output can be obtained in terms of reusability. This paper also shows how a small number of fuzzy rules can be selected for designing initial fuzzy rule-base for neuro-fuzzy systems. It consists of two phases: generation of candidate rules by IDS decision tree algorithm and rule pruning by evaluation of rules with help of two rule evaluation criteria. The developed reusability evaluation system has produced high precision results. Hence, the developed system can be used for identification and extraction of OO based reusable components from legacy systems and evaluation of developed or developing reusable components

An empirical approach for software fault prediction

Measuring software quality in terms of fault proneness of data can help the tomorrows programmers to predict the fault prone areas in the projects before development. Knowing the faulty areas early from previous developed projects can be used to allocate experienced professionals for development of fault prone modules. Experienced persons can emphasize the faulty areas and can get the solutions in minimum time and budget that in turn increases software quality and customer satisfaction. We have used Fuzzy C Means clustering technique for the prediction of faulty/ non-faulty modules in the project. The datasets used for training and testing modules available from NASA projects namely CM1, PC1 and JM1 include requirement and code metrics which are then combined to get a combination metric model. These three models are then compared with each other and the results show that combination metric model is found to be the best prediction model among three. Also, this approach is compared with others in the literature and is proved to be more accurate. This approach has been implemented in MATLAB 7.9.