Volume-04 , Special Issue-03 , May - 2016 Go Back

Due to increase in the amount of data, it is important to find useful information from data which is the main objective of data mining. Clustering is one of the techniques of data mining. Data clustering is the process of grouping similar data into same clusters. A Clustering Algorithm partitions a data set into several groups such that similarity within a group is larger than other groups. This paper gives the insight of grouping similar handwritten Devanagari words using STING algorithm. We take a wide view of the possible grouping using different distance measures on STING algorithm, compare their results and try to increase efficiency and decrease fault rate. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered. The most efficient implementation is one with least fault rate and that best distance measure to be considered to cluster the similar handwritten Devanagari scripts using STING algorithm.

Data mining, Distance measures, Clustering, Grouping, Devanagari, STING algorithm

[1] Wei. Wang, Jiong Yang and Richard Muntz. “STING: A statistical information grid approach to spatial data mining”. Proceeding VLDB ’97 proceedings of the 23rd International conference on Very Large Data bases, 1987.
[2] T. Zhang, R. Ramakrishnan and M. Livny. “BIRCH: an efficient data clustering method for very large databases”. Proc. 1996 ACMSIGMOD Int. Conf Management of Data, pp. 103-l14, Montreal, Canada, June 1996.
[3] Martin Ester, Hans-Peter Kriegal, Jorg S, Xiaowei Xu. “A Density-Based Algorithm for clustering in large spatial databases with noise”, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, 1996.
[4] Jiawei Han and M Kamber, Data Mining: Concepts and Techniques, 2001 (Academic Press, San Diego, California, USA).
[5] T Soni Madhulatha. ”An Overview of Clustering Methods” IOSR Journal of engineering Vol. 2(4) pp: 710-725.
[6] R Pushpalatha and Dr K. Meenakshi Sundaram. “Survey paper on clustering techniques in data mining” International journal of advanced research in data mining and cloud computing (ijarcsa) Vol. 3, Issue 2, 2015.
[7] Jaskaranjit Kaur and Gurpreet Kaur, "Clustering Algorithms in Data Mining: A Comprehensive Study", International Journal of Computer Sciences and Engineering, Volume-03, Issue-07, Page No (57-61), Jul -2015, E-ISSN: 2347-269.

Prathima Guruprasad and Vijayalakshmi B , "Grouping of Similar Handwritten Devanagari Scripts Using Different Distance Measures for Grid Based Approach", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.54-57, 2016.

In video or image so many faces will be present. Each name is associated with some names in the corresponding caption. The goal of this project is naming the faces with the correct names. This application used in Face book, Flicker and some news websites like NDTV,TV9 etc…To generate these type of application earlier they using a method like detect the face first and give label to it give name to it. Here dataset are more. To solve this problem here proposing two new methods by learning two discriminative affinity matrices from these Weakly labeled images. First method is regularized low-rank representation by effectively utilizing Weakly supervised information to learn a low-rank reconstruction coefficient matrix while exploring multiple subspace structures of the data. In this method they reducing dataset by taking a training images and converted into affinity matrices. After generating affinity matrices they are using low rank representation method. After generating this low rank representation they provide labeling for the images by using subspace structures. After creating subspace structures generate a affinity matrices. Second method is called ambiguously supervised structural metric learning by using weakly supervised information to seek a discriminative distance metric. This method is used to calculate the distances between the pixels in the image by using mahalanobis distances of data. After calculating the distances it going to create some of the clusters. It is used to create a boundary and also give the features of the faces. These faces will be get in matrix form. From this face we recognizing the correct name for it.

matrix, caption-based face naming, distance metric learning, low-rank representation(LRR

[1] P. Viola and M. J. Jones, “Robust real-time face detection,” Int.J. Comput. Vis., vol. 57, no. 2, pp. 137–154, 2004.
[2] G. Liu, Z. Lin, and Y. Yu, “Robust subspace segmentation by low-rank representation,” in Proc. 27th Int. Conf. Mach. Learn., Haifa, Israel, Jun. 2010, pp. 663–670.
[3] T. L. Berg et al., “Names and faces in the news,” in Proc. 17th IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Washington, DC, USA, Jun./Jul. 2004, pp. II-848–II-854.
[4] D. Ozkan and P. Duygulu, “A graph based approach for naming faces in news photos,” in Proc. 19th IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., New York, NY, USA, Jun. 2006, pp. 1477–1482.
[5] M. Everingham, J. Sivic, and A. Zisserman, “Hello! My name is... Buffy—Automatic naming of characters in TV video,” in Proc. 17th Brit Mach. Vis. Conf., Edinburgh, U.K., Sep. 2006, pp. 899–908.
[6] Z. Zeng et al., “Learning by associating ambiguously labeled images,”in Proc. 26th IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR,USA, Jun. 2013, pp. 708–715.

Dr Naghabhushan , Dr Aravinda T V , Arpitha H S , "Programmed Face Learning To Name Discriminative Fondness Matrices From Weakly Labeled Images", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.58-61, 2016.

Agriculture encompasses agricultural production and the environmental goods and services. Plant species classification, recognition of medicinal value and identification of diseases are most important tasks in agriculture. For these applications a primary requirement is obtaining the target leaf. Thus, leaf extraction is an important step for variety of these applications. But it is still a challenging problem especially for the images with complicated background such as with some interference and overlaps between two adjacent leaves. Hence a leaf extraction algorithm has been developed using two approaches: contour analysis approach and marker controlled watershed segmentation method. The contour analysis approach employs contour regions to detect the boundaries of the objects. The target leaf is obtained using the connected edges of the contour boundary. The second approach, marker-controlled watershed segmentation method is applied on the gradient images of Hue, Intensity and Saturation of the HSI color space, separately. The solidity (integrity) measure is then used to evaluate how well the segmented image is for extraction of the target leaf and determine the final leaf extraction result. The extracted leaf is given as an input to the disease diagnosis system for analysis of disease on the given leaf.

leaf extraction,contour analysis,marker controlled water shed segmentation,solidity measure

[1] Xiaodong Tang, Manhua Liu, Hui Zhao, Wei Tao Department of Instrument Science & Engineering, School of EIEE Shanghai Jiao Tong University Shanghai, PRC “Leaf Extraction from Complicated Background”
[2] Joao Camargo Neto, George E. Meyer, and David D. Jones, “Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm”, Computers and electronics in agriculture 51, Elsevier, USA, 2006, 66-85.
[3] Franz. E., Gaultney, L.D., and Unklesbay, K.B., “Algorithms for extraction leaf boundary information from digital images of plant foliage”, Trans, ASAE 38(2), USA, 1995, pp. 625-633.
[4 ]Franz. E., Gebhardt, M.R., and Unklesbay, K.B., “Shape description of completely visible and partially occluded leaves fro identifying plants in digital images”, Trans, ASAE 4(2), USA, 1991, pp. 673-681.
[5]Xiao-Feng Wang, De-Shuang Huang, Ji-Xiang Du, Huan Xu , Laurent Heutte, “Classification of plant leaf images with complicated background,” Applied Mathematics and Computation, 205(2), pp. 916-926, 2008.
[6]Woebbeck, D.M., Meyer, G.E., VonBargen, K., and Mortensen, D.A.,“Color indices for weed identification under various soil, residue and lighting conditions”, Trans. ASAE 38, USA, 1995, pp. 259-269.
[7] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, USA, pp. 62-66.
[8] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[9] Seber, G. A. F. Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc., 1984.
[10] Spath, H. Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples. Translated by J. Goldschmidt. New York: Halsted Press, 1985.
[11]Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, Digital Image Processing using MATLAB, Publishing House of Electronics Industry, Beijing, 2004.
[12] www.mathworks.com
[13] www.ni.com/LabVIEW/
[14] www.stackoverflow.com
[15] www.wikipedia.com

Shiddalingappa Kadakol, Jyothi B Maned, "Intelligence System for Leaf Extraction and Disease Diagnostic", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.62-66, 2016.

Shpymology, which precisely subsidizes the Acquaintance of Life expectancy using throb weary’, has distributed on the shocking scholarship upon society on perceiving the uneven charismas privileged of our physique, without the utilization of any apparatuses aside from extremities. View of the illustrations of throbs is an indispensable strategy for diagnostics in Ancient medical treatise. Diverse sorts of infirmities can be recognized in ahead of schedule stages by utilizing this throb analysis which is additionally called as Pulse fortitude. This paper studies on different illnesses that can be illustrious by the weary finding or throb analysis and gives the statistics about how throb analysis can be helpful for the epoch of computational illustrations for diverse sicknesses which is appreciated for the premature identification of a few contagions.

Sphygmology, Throb analysis, Vata, Pitta, Kapha, Tridosha

[1] Prajkta Kallurkar, Kalpesh Patil, Gagan Sharma, Shiru Sharma, Neeraj Sharma, “Analysis of Tridosha in Various Physiological Conditions”, Electronics, Computing and Communication Technologies (CONECCT), IEEE International Conference, pp 1-5, INSPEC Accession Number: 15723844, 2015
[2] Sukesh Rao M, Rathnamala Rao, “Investigation on Pulse Reading Using Flexible Pressure Sensor’, International Conference on Industrial Instrumentation and Control (ICIC) College of Engineering Pune, India. May 28-30, 2015
[3] Sharmila Begum M, Nivethitha S, ‘Wrist Energy Fuzzy Assist Cancer Engine WE-FACE”, International Journal of Engineering and Technology (IJET), 2013
[4] B.Persis Urbana Ivy, Dr.R.Rani Hema Malini, “Traditional Medicines - Korea, China, India”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 1282, Volume 2, Issue 1, January 2012
[5] Mikyoung Park,Heejung Kang,Young Huh, “Cuffless and Noninvasive measurement of systolic blood pressure, Diastolic blood pressure, mean arterial pressure and pulse pressure using radial artery tonometry pressure sensor with concept of Korean medicine”, International Conference of the IEEE EMBS Cite International, Lyon, France, August 23-26, 2007
[6] Xu L.S, Wang K.Q, Wang L, Maimin Li. “Pulse Contour Variability Before and After Exercise”, 19th IEEE International Symposium on Computer-Based Medical Systems(CBMS), pp.237, 240,doi: 10.1109/CBMS.2006.1 36, 2006
[7] V. Lad, “Secrets of the pulse: The ancient art of Ayurvedic pulse diagnoses. Motilal Banarsidass, Delhi, 2005.
[8] D.A. Duprez, D.R. Kaiser, W. Whitwam, S. Finkelstein, A. Belalcazar, R. Patterson, S. Glasser, and J.N. Cohn, “Determinants of radial artery pulse wave analysis in asymptomatic individuals”, American Journal of Hypertension, pp.647653, 2004

Akshatha.N, Narendra Kumar and Dr.K.B.Ramesh, "Design and Development of a Primeval Medical Therapy using Applied Electronic Instrumentation by Analyzing Wrist Throb Procurement for Monitoring Human Health Status", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.67-71, 2016.

Human interactive systems have attracted a lot of research interest in recent years, especially for content- based image retrieval systems. Contrary to the early systems, which focused on fully automatic strategies, recent approaches have introduced human-computer interaction. In this paper, we focus on the retrieval of concepts within a large image collection. We assume that a user is looking for a set of images, the query concept, within a database. The aim is to build a fast and efficient strategy to retrieve the query concept. In content-based image retrieval (CBIR), the search may be initiated using a query as an example. The top rank similar images are then presented to the user. Then, the interactive process allows the user to refine his request as much as necessary in a relevance feedback loop. Many kinds of interaction between the user and the system have been proposed, but most of the time, user information consists of binary labels indicating whether or not the image belongs to the desired concept.

Multimedia information retrieval,Content based image retreival,Image search,Interactive search,Relavance feedback

[1] Andre P, Cutrell E, Tan D, Smith G (2009) Designing novel image search interfaces by understanding unique characteristics and usage. In: Proceedings of international conference on human– computer interaction
[2] Aggarwal G, Ashwin TV, Ghosal S (2002) An image retrieval system with automatic query modification. IEEE Trans. on Multimedia 4(2):201–214
[3] Bian W, Tao D (2010) Biased discriminant Euclidean embedding for content-based image retrieval. IEEE Trans Image Process 19(2):545–554
[4] Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2): 1–60
[5] Datta R, Li J, Wang JZ (2005) Content-based image retrieval: approaches and trends of the new age. In: Proceedings of ACM international workshop on multimedia, information retrieval, pp 253–262
[6] Lew MS, Sebe N, Djeraba C, Jain R(2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimedia Comput Commun Appl 2(1):1–19
[7] Ren K, Sarvas R, Calic J (2010) Interactive search and browsing interface for large-scale visual repositories. Multimedia Tools Appl 49:513–528
[8] Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Machine Intell 22(12):1349–1380

Balaram Joshi S M, Vinayak V Naik and Siddalingappa Kadakol, "Active Learning Methods for Interactive Image Retrieval", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.72-77, 2016.

Detection and tracking of moving objects in a video has been emerging as a demanding research in the domain of computer vision and image processing in the resent years. It has been used in various applications like visual surveillance, traffic monitoring etc for tracking interested objects. An efficient method for object detection and tracking is proposed in this work. Two discriminative visual features like spatial edges and temporal motion boundaries as indicators for foreground object locations are considered. Initially frame wise spatiotemporal saliency maps by making use of geodesic distance indicators are created. Geodesic distance also provides an initial estimation for background and foreground by building on the observation that foreground areas are surrounded by the regions with high patio temporal edge values. Coherent object segmentation is done by combining all this spatio temporal maps. Finally the segmented object is tracked using Kalman filter get efficient result

Spatial edges, Temporal motion boundaries, Spatiotemporal saliency maps, Geodesic distance, Kalman filter, Visual surveillance, Pixel segmentation, super pixels.

[1] Radhakrishna Achanta, Appu Shaji, Kevin Smith,Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, “SLIC Superpixels Compared to State-of-the-art Super pixel Methods”, Journal of latex class files, Vol. 6, Issue 1, 2011.
[2] C. Beyan A and Temizel, “Adaptive Mean-Shift for Automated Multi Object Tracking”, The Institution of Engineering and Technology, 2011.
[3] Houari Sabirin and Munchurl Kim, “Moving Object Detection and Tracking Using a Spatio-Temporal Graph in H.264/AVC Bit streams for Video Surveillance”, IEEE transactions on multimedia, Vol. 14, Issue 3, 2012.
[4] Kinjal A Joshi and Darshak G. Thakore, “A Survey on Moving Object Detection and Tracking in Video Surveillance System”, International Journal of Soft Computing and Engineering, Vol. 2, Issue 3, 2012.
[5] Bahadir Karasulum and Serdar Korukoglu, “Moving object detection and tracking by using Annealed background subtraction method in Videos: Performance Optimization”, Elsevier, pp. 33–43, 2012.
[6] Alexander Schick, Martin Baum and Rainer Stiefelhagen, “Improving Foreground Segmentations with Probabilistic Superpixel Markov Random Fields”, IEEE, pp. 27 – 31, 2012.
[7] Xiaofang Wang, Huibin Li, Simon Masnou, Liming Chen, “Sparse Coding and Mid-Level Superpixel-Feature for l0-Graph Based Unsupervised Image Segmentation”, HAL, pp. 160-168, 2013.
[8] Sayed Hossein Khatoonabadi and Ivan V. Baji´c, Senior Member, IEEE, “Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields”, IEEE, Vol. 22, Issue 1, 2013.
[9] Hitesh A Patel and Darshak G Thakore, “Moving Object Tracking Using Kalman Filter”, IJCSMC, Vol. 2, Issue 4, pp. 326 – 332, 2013.
[10] Dan Oneata, Erome Revaud, Jakob Verbeek and Cordelia Schmid T, “Spatio-Temporal Object Detection Proposals”, HAL, pp.737-752, 2014.
[11] Wenguan Wang, Jianbing Shen and Fatih Porikli, “Saliency-Aware Geodesic Video Object Segmentation”, IEEE, 3395 – 3402, 2015.
[12] Divyani Prajapati and Hiren J Galiyawala, “A Review on Moving Object Detection and Tracking”, International Journal of Computer Application, Vol 5, Issue 3, 2015.
[13] Sanjivani Shantaiya, Kesari Verma and Kamal Mehta, “Multiple Object Tracking using Kalman Filter and Optical Flow”, European Journal of Advances in Engineering and Technology, Vol. 2, Issue 2, pp.34-39, 2015.

Rakshitha N, Mangala C N, "Saliency Aware Video Object Detection and Tracking", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.78-81, 2016.

Face recognition has become a field of interest in pattern recognition and artificial intelligence. One of the vital steps involved in face recognition is that of ‘Feature Extraction’. Feature extraction is imperative because handling data whose dimensions are inherently high, is rather a tedious process and therefore we adopt strategies for the purpose of dimensionality reduction. This process of studying data by reducing dimensions is called subspace analysis. Two such subspace methods are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA extracts the most significant components or those components which are more informative and less redundant, from the original data. While LDA is used to find a linear combination of features that characterizes or separates two or more classes in the data. Both PCA and LDA are studied in this paper. For our data set, distance measure is used as a classifier. Euclidean distance, Manhattan distance, Chi square distance are some examples for distance measures.

Face recognition, Feature extraction, Dimensionality reduction, Subspace methods, PCA, LDA, Classification

[1] Zhou C, Wang L, Zhang Q, Wei X. Face recognition based on PCA image reconstruction and LDA. Optik-International Journal for Light and Electron Optics. 2013 Nov 30;124(22):5599-603.
[2] Divesh N. Agrawal and Deepak Kapgate, "Face Recognition Using PCA Technique", International Journal of Computer Sciences and Engineering, Volume-02, Issue-10, Page No (59-61), Oct -2014, E-ISSN: 2347-2693
[3] Kim HC, Kim D, Bang SY. Face recognition using LDA mixture model. Pattern Recognition Letters. 2003 Nov 30;24(15):2815-21.
[4] Nivedita Verma and Sanyam Shukla, "A Review paper on different Pose Invariant Face Recognition Techniques using Neural Networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-05, Page No (98-104), May -2015, E-ISSN: 2347-2693
[5] Jadhav DV, Holambe RS. Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition. Signal Processing. 2008 Oct 31;88(10):2604-9.
[6] Choi SI, Choi CH, Jeong GM, Kwak N. Pixel selection based on discriminant features with application to face recognition. Pattern Recognition Letters. 2012 Jul 1;33(9):1083-92.
[7] P. S. Hiremath and Manjunatha Hiremath, "Symbolic Factorial Discriminant Analysis for 3D Face Recognition", International Journal of Computer Sciences and Engineering, Volume-02, Issue-01, Page No (6-12), Jan -2014, E-ISSN: 2347-2693
[8] Swati Kamble and R. K. Krishna , "A Review: Video Face Recognition under Occlusion", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, Page No (148-155), Mar -2015, E-ISSN: 2347-2693
[9] Pong KH, Lam KM. Multi-resolution feature fusion for face recognition. Pattern Recognition. 2014 Feb 28;47(2):556-67.
[10] Jing XY, Wong HS, Zhang D. Face recognition based on discriminant fractional Fourier feature extraction. Pattern Recognition Letters. 2006 Oct 1;27(13):1465-71.
[11] Wasim Shaikh, Hemant Shinde and Grishma Sharma, "Face Recognition Using Multi-Agent System", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (55-58), Apr -2016, E-ISSN: 2347-2693
[12]. Neelam Mahale, Dr. Manoj S. Nagmode and Prajakta S. Ghatol, "Face Recognition Using Principal Component Analysis Method", International Journal of Computer Sciences and Engineering, Volume-02, Issue-07, Page No (57-61), Jul -2014, E-ISSN: 2347-2693
[13] Naji SA, Zainuddin R, Jalab HA. Skin segmentation based on multi pixel color clustering models. Digital Signal Processing. 2012 Dec 31;22(6):933-40.
[14] Yan S, Wang H, Liu J, Tang X, Huang TS. Misalignment-robust face recognition. Image Processing, IEEE Transactions on. 2010 Apr;19(4):1087-96.
[15] Nitesh Pandey, Abhishek Dubey and Bhavesh Pandekar, "Face Recognition Using Robotics", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (86-90), Apr -2016, E-ISSN: 2347-2693

Sanjay G, "A Comparative Study on Face Recognition using Subspace Analysis", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.82-86, 2016.

Face recognition has become a field of interest in pattern recognition and artificial intelligence. One of the vital steps involved in face recognition is that of ‘Feature Extraction’. Feature extraction is imperative because handling data whose dimensions are inherently high, is rather a tedious process and therefore we adopt strategies for the purpose of dimensionality reduction. This process of studying data by reducing dimensions is called subspace analysis. Two such subspace methods are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA extracts the most significant components or those components which are more informative and less redundant, from the original data. While LDA is used to find a linear combination of features that characterizes or separates two or more classes in the data. Both PCA and LDA are studied in this paper. For our data set, distance measure is used as a classifier. Euclidean distance, Manhattan distance, Chi square distance are some examples for distance measures.

Face recognition, Feature extraction, Dimensionality reduction, Subspace methods, PCA, LDA, Classification

[1] Zhou C, Wang L, Zhang Q, Wei X. Face recognition based on PCA image reconstruction and LDA. Optik-International Journal for Light and Electron Optics. 2013 Nov 30;124(22):5599-603.
[2] Divesh N. Agrawal and Deepak Kapgate, "Face Recognition Using PCA Technique", International Journal of Computer Sciences and Engineering, Volume-02, Issue-10, Page No (59-61), Oct -2014, E-ISSN: 2347-2693
[3] Kim HC, Kim D, Bang SY. Face recognition using LDA mixture model. Pattern Recognition Letters. 2003 Nov 30;24(15):2815-21.
[4] Nivedita Verma and Sanyam Shukla, "A Review paper on different Pose Invariant Face Recognition Techniques using Neural Networks", International Journal of Computer Sciences and Engineering, Volume-03, Issue-05, Page No (98-104), May -2015, E-ISSN: 2347-2693
[5] Jadhav DV, Holambe RS. Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition. Signal Processing. 2008 Oct 31;88(10):2604-9.
[6] Choi SI, Choi CH, Jeong GM, Kwak N. Pixel selection based on discriminant features with application to face recognition. Pattern Recognition Letters. 2012 Jul 1;33(9):1083-92.
[7] P. S. Hiremath and Manjunatha Hiremath, "Symbolic Factorial Discriminant Analysis for 3D Face Recognition", International Journal of Computer Sciences and Engineering, Volume-02, Issue-01, Page No (6-12), Jan -2014, E-ISSN: 2347-2693
[8] Swati Kamble and R. K. Krishna , "A Review: Video Face Recognition under Occlusion", International Journal of Computer Sciences and Engineering, Volume-03, Issue-03, Page No (148-155), Mar -2015, E-ISSN: 2347-2693
[9] Pong KH, Lam KM. Multi-resolution feature fusion for face recognition. Pattern Recognition. 2014 Feb 28;47(2):556-67.
[10] Jing XY, Wong HS, Zhang D. Face recognition based on discriminant fractional Fourier feature extraction. Pattern Recognition Letters. 2006 Oct 1;27(13):1465-71.
[11] Wasim Shaikh, Hemant Shinde and Grishma Sharma, "Face Recognition Using Multi-Agent System", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (55-58), Apr -2016, E-ISSN: 2347-2693
[12]. Neelam Mahale, Dr. Manoj S. Nagmode and Prajakta S. Ghatol, "Face Recognition Using Principal Component Analysis Method", International Journal of Computer Sciences and Engineering, Volume-02, Issue-07, Page No (57-61), Jul -2014, E-ISSN: 2347-2693
[13] Naji SA, Zainuddin R, Jalab HA. Skin segmentation based on multi pixel color clustering models. Digital Signal Processing. 2012 Dec 31;22(6):933-40.
[14] Yan S, Wang H, Liu J, Tang X, Huang TS. Misalignment-robust face recognition. Image Processing, IEEE Transactions on. 2010 Apr;19(4):1087-96.
[15] Nitesh Pandey, Abhishek Dubey and Bhavesh Pandekar, "Face Recognition Using Robotics", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (86-90), Apr -2016, E-ISSN: 2347-2693

Sanjay G, "A Comparative Study on Face Recognition using Subspace Analysis", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.82-86, 2016.

The problems of locomotion exist not only for the aged, but also for the physically challenged, who might still be in the prime of their youth. The earlier versions of the wheelchair demand much of the manual effort to move the wheelchair. This paper presents a Sparsh Glove control system, aiming to resolve these issues, by allowing the user to control a wheelchair using natural gestures. The Sparsh Glove takes advantage of a multitude of sensors to capture hand movements and uses this information to control a wheelchair which also has provisions for seat elevation and various other features. Further, the wheelchair is designed to warn the user whenever there is an obstacle in the path of the wheel, and if there is any obstruction behind the user.

SparshGlove, Arduino Uno, Arduino IDE, XBee transceiver, Smart wheel chair,DC motor, Flex sensors, Seat elevation.

[1] Vaishali S. Kulkarni, and Dr. S.D.Lokhande, “Appearance Based Recognition of American Sign Language Using Gesture Segmentation”, (IJCSE) International Journal on Computer Science and Engineering, Volume - 02, Issue- 03, 2010, pp. 560 - 565.
[2] Koontz, A.M., Boninger, M.L., Baldwin, M.A., Cooper, R.A., and O'Connor, T.J.,"Effect of Vinyl Coated Pushrims on Wheelchair Propulsion Kinetics", RESNA Conference Proceedings, 1998
[3] http://www.postgazette.com. Accessed April, 1999
[4] Aleksandar Pajkanović, and Branko Dokić : “Wheelchair Control by Head Motion," Serbian Journal of electrical engineering, Volume-10, Issue-1, 2013, pp. 135-151.
[5] Kohei Arai and Ronny Mardiyanto, “Eyes Based Eletric Wheel Chair Control System”, International Journal of Advanced Computer Science and Applications, Volume - 2, Issue- 12, 2011, pp. 98-105.
[6] G Azam and M T Islam, “Design and Fabrication of a Voice Controlled Wheelchair for Physically Disabled People”, International Conference on Physics Sustainable Development & Technology (ICPSDT-2015), Volume – 01, 2015, pp. 81 – 90.
[7] S. D. Suryawanshi, J. S. Chitode and S. S. Pethakar, “Voice Operated Intelligent Wheelchair”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume - 3, Issue - 5, 2013, pp. 487 – 490.
[8] http://www.youtube.com/watch?v=Ywo6WR9NUpE.
[9] http://www.engadget.com/2009/12/18/peregrinegaming-glove-modeled-calibrated-anddemoed-onvideo/.
[10] http://fibretronic.com/news/Reusch%20Sonic%20Contr ol%20Glove.
[11] http://en.wikipedia.org/wiki/Power_Glove.
[12] http://www.instructables.com/id/Power-Glove-20thAnniversary-Edition/.
[13] http://www.readwriteweb.com/archives/minority_report_in_your_living_room_gestural_inter.php.
[14] Kamlesh H. Solanki, Hemangi Pujara, “Brain wave controlled robot", International Research Journal of Engineering and Technology (IRJET), Volume – 02, Issue – 04, 2015, pp. 609 –-612.
[15] http://hillcrestlabs.com/products/loop.php
http://www.youtube.com/watch?v=RnWPoaLU1i4.

Siri.T.Bhat1, B. Surekha and Shreesha Raghavan, "Sparsh Glove: A Gesture-Based Hardware Control for a Multipurpose Wheelchair", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.87-90, 2016.

Noncausal Markov model for a 2D signal is one of the inactive methods for spliced image. Image splicing is an image copies or merge a portion of image to same images or different images. The way Noncausal Markov model differs from traditional Markov model is the proposed methodology models a image as a 2-D noncausal signal and captures and analyzes the underlying dependencies between the current node and its neighbors in all directions. These dependencies are obtained through Discrete Cosine Transform and Discrete Wavelet Transform. These parameters give features to differentiate the natural ones with the features of spliced images. The noncausal Markov Model considers the input of block discrete cosine transformation domain, the discrete wavelet transform domain, and the cross-domain features for classification. The Expectation Maximization which is the classifier which classifies based on maximum likelihood of images. The dataset used is UCID dataset where we have uncompressed color images.

Noncausal Markov Model, Discrete Cosine Transformation (DCT), Discrete Wavelet Transform(DWT), inactive image splicing recognition, Expectation Maximization(EM).

[1] Xudong Zhao, Shilin Wang, Shenghong Li and Jianhua Li, “Passive Image-Splicing Detection by a 2-D Noncausal Markov Model”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 25, No. 2, February 2015.
[2] J. Li, A. Najmi and R. M. Gray, “Image classification by a twodimensional hidden Markov model,” IEEE Trans. Signal Process, Vol. 48, No. 2, pp. 517–533, Feb. 2000.
[3] Y. Q. Shi, C. Chen, G. Xuan, and W. Su, “Steganalysis versus splicing detection,” in Proc. 6th Int. Workshop Digit. Watermarking (IWDW), Guangzhou, China, pp. 158–172, 2008.
[4] P. Sutthiwan, Y.-Q. Shi, J. Dong, T. Tan, and T.-T. Ng, “New developments in color image tampering detection,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), Paris, France, pp. 3064–3067, May/Jun. 2010.
[5] W. Wang, J. Dong, and T. Tan, “Image tampering detection based on stationary distribution of Markov chain,” in Proc. 17th IEEE Int. Conf. Image Process. (ICIP), Hong Kong, pp. 2101–2104, Sep. 2010.
[6] S. Patchara, Y. Q. Shi, H. Zhao, T.-T. Ng, and W. Su, “Markovian rake transform for digital image tampering detection,” Trans. Data Hiding Multimedia Secur. VI, Vol. 6730, pp. 1–17, Jul. 2011.
[7] Preeti Yadav, Yogesh Rathore, ”Detection of Copy-Move forgery of images using Discrete Wavelet Transform”, International Journal on Computer Science and Engineering (IJCSE), Vol. 4 , pp 565-570, April 2012.

Thofa Aysha, Manjesh R, "Inactive Method of Noncausal 2D Image Splice Recognition Model using Markov Model", International Journal of Computer Sciences and Engineering, Vol.04, Issue.03, pp.91-96, 2016.