Journal of advancements in robotics (vol1 issue2) by STM Journals Publication

Journal of Advancements

in Robotics May - August 2014

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Journal of Advancements in Robotics Focus and Scope Covers ? Human Robot Interaction and Social Robotics ? Sensor Integration Robot ? Vision.- Robot Programming ? Medical Robotics ? Humanoid Robots ? Autonomous Helicopters

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Prof. Bankim Chandra Ray

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Prof.D. N. Rao

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Dr. Rakesh Kumar

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Prof. Subash Chandra Mishra

Prof. Sundara Ramaprabhu

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Editorial Board

Prof. Hsu-Yung Cheng Associate Professor Department of Computer Science and Information Engineering National Central University, Taiwan.

Gora Nandi Prof. in IIIT Allahabad India.

Sreekumar Assistant Professor Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai.

Dr. Bijan Karimi Professor College: Tagliatela College of Engineering Dept: Electrical & Computer Engineering and Computer Science India.

Amit Kumar Pandey Researcher in Robotics and AI(Human Robot Interaction and Socially Intelligent Robots) LAAS (Laboratory for Analysis and Architecture of Systems) CNRS (French National Center for Scientific Research) Paris, France

Dr. T. S. B. Sudarshan Professor and Chair, Dept. of Computer Science & Engg, Amrita VishwaVidya Peetham (University),Carmelaram P.O Bangalore.

Mihir Kumar Sutar Research Scholar Robotics & Control Lab Indian Institute of Technology Roorkee.

Director's Desk

STM JOURNALS

I take the privilege to present the print version for the Volume 1 Issue (2) of Journal of Advancements in Robotics. The intension of JoARB is to create an atmosphere that stimulates creativeness, research and growth in the area of Robotics. The development and growth of the mankind is the consequence of brilliant Research done by eminent Scientists and Engineers in every field. JoARB provides an outlet for Research findings and reviews in areas of Robotics found to be relevant for National and International recent developments & research initiative. The aim and scope of the Journal is to provide an academic medium and an important reference for the advancement and dissemination of Research results that support high level learning, teaching and research in the domain of Robotics. Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for their continued support and invaluable contributions and suggestions in the form of authoring write ups/ reviewing and providing constructive comments for the advancement of the journals. With regards to their due continuous support and co-operation, we have been able to publish quality Research/Reviews findings for our customers base. I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra Director STM Journals

Journal of Advancements in Robotics

Contents

1. On Reconnection of Broken Ridges and Binarization for Fingerprint Images Paridhi Munshi, Suman K. Mitra

2. Attribute based Level Adaptive Thresholding Algorithm for Object Extraction Ankush Rai

3. A Novel Approach for Confidence Estimation using Support Vector Machines for More Accurate Value Prediction Snigdha M. Mohapatra, Pradipta K. Mishra, Ajit Kumar Das

Journal of Advancement in Robotics Volume 1, Issue 2 www.stmjournals.com

On Reconnection of Broken Ridges and Binarization for Fingerprint Images Paridhi Munshi*, Suman K. Mitra Dhirubhai Ambani Institute of Information & communication Technology, Gandhinagar, Gujarat, India Abstract With the increase in security threats, use of biometrics for person identification and authentication has been increased. One of the oldest and most widely used forms of biometric is, Fingerprint. Since it is mainly used in forensic science, accuracy in the fingerprint identification is highly important. Most of the fingerprint identification systems are based on minutiae matching and a critical step in correct matching of fingerprint is extracting minutiae reliably from adequate quality images. However, fingerprint quality may be degraded and corrupted due to variations in skin, pressure and impression conditions. One of these degradations is due to the presence of unwanted breaks or creases on the surface of the image. Moreover most of the feature extraction algorithms work on binary images instead of the gray scale image and results of the feature extraction depends on the quality of binary image used. Keeping these points in mind, an enhancement technique for reconnection of broken ridges and binarization are proposed in this paper. Both of these pre-processing are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations and there by expect to get a robust matching performance.

Keywords: Fingerprint recognition, minutiae, enhancement, binarization

Journal of Advancement in Robotics Volume 1, Issue 2 www.stmjournals.com

Attribute based Level Adaptive Thresholding Algorithm for Object Extraction Ankush Rai* Department of Applied Science, CRIAD Laboratories, Chhattisgrah, India Abstract Image processing plays a vital role in the computer vision because most of the scenarios require object extraction and recognition. In order to utilize these images for the procedural analysis of the environmental entities in computational means, it must be a noiseless one. However, the images are affected through noises caused by the various acquisition techniques and hence an effective technique for denoising and segmentation is necessary in Computational Tomography. In order to achieve this objective we propose an effective denoising technique which confiscates the advantages offered in thresholding methods.

Keywords: Computer Vision, Image Segmentation, Image denoising

Journal of Advancement in Robotics Volume 1, Issue 2 www.stmjournals.com

A Novel Approach for Confidence Estimation using Support Vector Machines for More Accurate Value Prediction Snigdha M. Mohapatra, Pradipta K. Mishra*, Ajit Kumar Das Department of Computer Science, Centurion University of Technology and Management, Khordha, Odisha, India Abstract Data dependencies create hurdles in exploiting instruction-level parallelism (ILP) among instructions. To overcome them, data value predictors are used which guess instructionsâ&#x20AC;&#x2122; result before it is actually executed. Thus, future instructions which depend on the outcome of that instruction executes sooner. But, since value prediction accuracy is very crucial in determining the amount of parallelism that can be exploited, confidence estimation is used along with it to lessen the value prediction misprediction penalty by guessing whether or not to use a value prediction result. Previous confidence estimators were based on perceptrons which had the limitation of learning only linearly separable functions [1, 2]. But sometimes linear inseparability may arise when a correct prediction on a past instruction causes the current instruction to predict incorrectly [3]. As Support Vector Machines (SVMs) belong to a family of generalized linear classifier and can be interpreted as extension of perceptron, they are both linear and nonlinear classifiers and are computationally more efficient than perceptrons. Thus, we propose a confidence estimator using SVMs in which the prediction accuracy of previous instructions is used to estimate the confidence of current prediction and decide based on its results whether or not the prediction is likely to be correct. The classification algorithm of SVM is implemented using MATLAB platform, and its novel learning methods have been applied on different data sets having two classes.

Keywords: value prediction, confidence estimation, SVM