iosr-JCE   Volume-1 ~ Issue-2

ABSTRACT : This paper present an approach to recognize human face expression and emotions based on some fuzzy pattern rules. Facial features for this specially eye and lips are extracted an approximated into curves which represents the relationship between the motion of features and change of expression. This paper focuses the concepts like face detections, skin color segmentation, face features extractions and approximation and fuzzy rules formation. Conclusion based on fuzzy patterns never been accurate but still our intension is to put more accurate results.
Keywords -Face Detection, Skin Color Segmentation, Face Futures, Curve Formation and Approximation, Fuzzy Patterns.

[1] Y. Yacoob and L.S. Davis, "Recognizing human facial expressions from long image sequences using optical flow", IEEE Trans. Pattern Analysis & Machine Intelligence, Vol. 18, No 6, pp. 636-642, 1996.
[2] P. Ekman and W. Friesen, "Facial Action Coding System", Consulting Psychologists Press, 1977.
[3] K. Aizawa and T. S. Huang, "Model-based image coding: Advanced video coding techniques for very low bit-rate applications", Proc. IEEE, Vol. 83, No. 2, pp. 259-271, 1995.
[4] S. Kimura and M. Yachida, "Facial expression recognition and its degree estimation", Proc. Computer Vision and Pattern Recognition, pp. 295-300, 1997.
[5] K. Ohba, G. Clary, T. Tsukada, T. Kotoku, and K. Tanie, "Facial expression communication with FES", Proc. International Conference on Pattern Recognition, pp. 1376-1378, 1998.
[6] M.A. Bhuiyan and H. Hama, "Identification of Actors Drawn in Ukiyoe Pictures", Pattern Recognition, Vol. 35, No. 1, pp. 93-102, 2002.
[7] M. B. Hmid and Y.B. Jemaa, Fuzzy Classification, Image Segmentation and Shape Analysis for Human Face Detection. Proc. Of ICSP, vol. 4, 2006.
[8] M. Wang, Y. Iwai, M. Yachida, "Expression Recognition from Time-Sequential Facial Images by use of Expression Change Model", Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 324 – 329, 1998.
[9] M. I. Khan and M. A. Bhuiyan, "Facial Expression recognition for Human-Machine Interface", ICCIT, 2006.

ABSTRACT: Object tracking is an important task within the field of computer vision. It is a challenging problem. Many difficulties arises in tracking the objects due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. This paper selectively reviews the research papers with regard to tracking methods on the basis of the object, their motion representations and all detailed descriptions of representative methods in each category examining their advantages/disadvantages. It also discusses the important issues related to tracking including the use of object representation, tracking, and detection.
Keywords – Object Representation, Object Tracking, Object Detection, Computer Vision.

[1] Pengwei LIU, Huiyuan WANG et al., Motion Compensation Based Detecting and Tracking Targets in Dynamic Scene, IEEE, 2010, 703-706.
[2] Sajjad Torkan, Alireza Behrad, A New Contour Based Tracking Algorithm Using Improved Greedy Snake, IEEE, 2010.
[3] Baiyang Liu, Lin Yang et al., An Adaptive Tracking Algorithm Of Lung Tumors In Fluoroscopy Using Online Learned Collaborative Trackers, IEEE, 2010, 209-212.
[4] Alexander Toshev, Ameesh Makadia, Kostas Daniilidis, Shape-based Object Recognition in Videos Using 3D Synthetic Object Models, IEEE, 2009, 288-295.
[5] Ming-Yu Shih, Yao-Jen Chang, Bwo-Chau Fu, and Ching-Chun Huang, Motion-based Background Modeling for Moving Object Detection on Moving Platforms, IEEE, 2007, 1178-1182.
[6] Mark Ritch, Nishan Canagarajah, Motion-Based Video Object Tracking In The Compressed Domain, IEEE, 2007, 301-304.
[7] Alper Yilmaz, Omar Javed, Mubarak Shah, Object Tracking:A Survey, ACM Computing Surveys, 38(4), 2006.
[8] Rajan Sehgal, Video Image Enhancement and Object Tracking, A Thesis, Thapar Institute of Engineering and Technology, Patiala, ME, 2006.
[9] Minglun Gong, A GPU-based Algorithm for Estimating 3D Geometry and Motion in Near Real-time, IEEE, 2006.
[10] Huiqiong Chen, Derek Rivait and Qigang Gao, Real-Time License Plate Identification by Perceptual Shape Grouping and Tracking, IEEE, 2006, 1352-1357.

ABSTRACT:Opinion retrieval has established itself as an important part of search engines ratings, opinion trends and representative opinions enrich the search experience ofusers when combined with traditional document retrieval by revealing more insights about a subject.In the past years we have witnessed Sentiment Analysis and OpinionMining becoming increasingly popular topics in InformationRetrieval and Web data analysis.With the rapid growth of the user-generated content on the Web.Opinion retrieval is a document retrieving and ranking process, a relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval; it classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. In this study, we review the development of opinion search and retrieval during the last years, and also discuss the evolution of a relatively newresearch directionand we try to layout the futureresearch directions in the field.
Keywords:Opinion mining, Opinion Retrieval, Opinion Identification, Text Mining,

[1] Ana-Maria Popescu and Oren Etzioni.Extractingproduct features and opinions from reviews..In Proceedings of Human LanguageTechnology Conference and Conference on Empirical Methods in Natural Lan-guage(HLT/EMNLP),2005, 339-346.
[2] G. Amati. Probabilistic models for informationretrieval based on Divergence from Randomness.PhDthesis, University of Glasgow, 2003.
[3] Bing Liu,"sentimentanalysi and subjectivity" to appear in Handbook of Natural Language Processing,Second Edition.(editors: N. Indurkhya and F.J. Damerau),2010
[4] BenHe&JiyinHeIadhOunis"An Effective Statistical Approach to Blog Post OpinionRetrieval"CIKM'08, October 26–30, 2008, Napa Valley, California, USA.
[5] Bo Pang and Lillian Lee. A sentiment education: Senti-ment analysis using subjectivity summarization based on minimum cuts. InProceedings of the 42nd Annual Meeting of the Association for ComputationalLinguistics(ACL)2004. 271-278.
[6] Binali,H.,Potdar, V.1 and Chen wul.(2009).A state of the art opinion Mining and Its application domains. IEEE International Conference on Industrial Technology.01/01/2009.
[7] A. Esuli and F. Sebastiani, "Determining the semantic orientation of terms through glossanalysis," Proceedings of the ACM Conference on Information and Knowledge Management(CIKM), 2005.
[8] A. Esuli and F. Sebastiani, "Determining term subjectivity and term orientation for Opinionmining,"Proceedings of the European Chapter of the Association for Computational Linguistics(EACL), 2006
[9] A. Esuli and F. Sebastiani, "SentiWordNet: A publicly available lexical resource for pinionmining," Proceedings of Language Resources and Evaluation(LREC), 2006.
[10] A. Esuli and F. Sebastiani, "PageRankingWordNetsynsets: An application to opinion mining,"

Abstract : This paper provides the design of a Document Management System designed for a Small to Medium scale enterprises with a special emphasis on security, it also describes a new symmetric encryption algorithm Secured Quick Crypt and its unique block chaining mechanism. Along with this, various other small enhancements in terms of Compression, Abstraction and File Versioning have also been described in the paper.
Keywords-compression; document management system; file versioning; level of abstraction; security

[1] HEDIYEH BABAN,SALIMAH MOKHTAR, "ONLINE DOCUMENT MANAGEMENT SYSTEM FOR ACADEMIC INSTITUTES", 28 NOV. 2010, VOLUME : 4,315-319
[2] Konecki, M.; Kudelic, R.; Lovrencic, A., "Efficiency of lossless data compression",23-27 May 2011, 810-815
[3] Zia, Z.K.; Rahman, D.F.; Rahman, C.M., "Two-Level Dictionary-Based Text Compression Scheme", 24-27 Dec. 2008, 13 – 18
[4] Jonghyun Lee, Marianne Winslett, Xiaosong Ma, Shengke Yu, "Enhancing Data Migration Performance via Parallel Data Compression", 2002, 444 – 451
[5] Xin Zhou ; Xiaofei Tang, "Research and implementation of RSA algorithm for encryption and decryption", 22-24 Aug. 2011, Volume : 2, 1118 – 1121
[6] Bhargav Balakrishnan, "Three Tier Encryption Algorithm for Secured File",19-21 Mar 2010, Volume : 2, 259 – 263
[7] Gang Hu, "Study of File Encryption and Decryption System using Security Key",16-18 April 2010, Volume : 7, V7-121 - V7-124
[8] Hiroki Endo, Yoshihiro Kawahara, and Tohru Asami, "A Self-Encryption Based Private Storage System Over P2P Distribution File Sharing Infrastrure", 12-13 May 2008, 69 – 76
[9] Xiang Xiao-Jia; Shu Ji-Wu; Xue Wei; Zheng Wei-Min, "Design and Implementation of an Efficient Multi-version File System", 29-31 July 2007, 277 – 278
[10] Maohua Lu; Chiueh, T., "File Versioning for Block-Level Continuous Data Protection", 22-26 June 2009, 327 - 334

Abstract : The estimation of lexical semantic relatedness has numerous applications in NLP. Several measures are available for the evaluation of lexical semantic relatedness. This paper presents two approaches for measuring semantic similarity/distance between words and concepts with the help of WordNet-Telugu. The edge-based approach of the edge counting scheme and the node-based approach of the information content calculation have been explored. In the field of concepts, the measure of Wu and Palmer has the advantage of being simple to implement and have good performances compared to the other similarity measures. The obtained results show that the Wu and Palmer approach presents a better performance in terms of relevance.
Key words: NLP, Semantic Measure, Word Net-Telugu, Information content and Relevance

Books:
1. [Baeza]: R. Baeza-Yates, B. Riberio-Neto, "Modern Information retrieval", ACM Press; Addison-Wesley: New York; Harlow, England: Reading, Mass., 1999
Chapters in Books: 2
Theses:
2. [Resn a]: P. Resnik, "Selection and Information: A Class based Approach to Lexical Relationships", PhD dissertation at the University of Pennsylvania. Also appears as Technical Report 93-42, November 1993.
3. [Ray]: Ray Richardson and Alan F. Smeaton, " Using WordNet in a Knowledge-Based Approach to Information retrieval", Dublin University.
4. [Rich]: R.Richardson, "A Semantic based Approach to Information Processing", Ph.D. thesis School of Computer Applications, Dublin University,1994.
Proceedings Papers:
5. [Jay] : Jay J. Jiang, David W. Conrath : " Semantic Similarity Based on Corpus statistical and Lexical Taxonomy", In proceedings of International conference Research on Computational Linguistics (ROCLING X), 1997, Taiwan.
6. [Resn]: P.resnik(1995), " Using Information Content to evaluate semantic similarity in taxonomy", In proceedings of 14th international JointConference on Artificial Intelligence,Montreal, 1995.

Abstract- Detecting moving objects using stationary cameras has been responsible for reducing the reliability of many computer vision algorithms, including segmentation, object detection, scene analysis, stereo, tracking, etc. Therefore, detecting moving object is an important pre-processing for improving performance of such vision tasks. In this paper we proposed a method to extract the key frames, detect the moving objects . Firstly the video is segmented into shots after that the key frames are extracted. The moving objects is detected using background subtraction.
Key words: moving object detection, background subtraction

[1] B. V. Patel, B. B. Meshram (2007), "Retrieving and Summarizing Images from PDF Documents", International Conference on Soft computing and Intelligent Systems (ICSCSI-07),Jabalpur, India.
[2] "AN ENHANCED CONTENT-BASED VIDEO RETRIEVAL SYSTEM BASED ON QUERY CLIP‟ T.N.SHANMUGAMand PRIYA RAJENDRAN International Journal of Research and Reviews in Applied Sciences (December 2009)
[3] 3rd International congress on Image and Signal Processing (CSIP) 2010: Battlefield video target mining based image retrieval at the end of the early years."
[4] "Content Based Video retrieval with Motion vectors and the RGB Colour Model‟. Forensic Science Journal 2007.
[5] H. T. Chen, H. H. Lin, T. L. Liu. "Multi-object tracking using dynamical graph matching,‟ IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR‟01) - Vol. 2, p.210, 2001.
[6] S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and. H. Wechsler, "Tracking groups of people,‟ Computer Vision and Image Understanding 80, 42–56, 2000. [7] J. R. Smithand S.-F. Chang, Single color extraction and image query, in Proc. IEEE Int. Conf. on Image Proc., 1995.
[7] S. Jabri, Z. Duric, H. Wechsler, A. Rosenfeld. "Detection and location of people in video images using adaptive fusion of color and edge information,‟ 15th International Conference on Pattern Recognition ,Vol. 4, p.4627, 2000.

Abstract:This paper informs the reader how an innocent looking digital image hides a deadly terrorist plan. It analyses the strengths of image steganography and the reasons why terrorists are relying on it. It also aims to generate interest among the scientific community about Image steganography so that experts from multiple dis-ciplines may join hands for devising better steganalysis techniques against the global terrorism. In this paper a basic digital image is analyzed and based on this analysis an LSB based steganographic algorithm is designed. Using this algorithm a software is developed and its performance is compared with various other steganograph-ic tools available on the internet. The result of this comparison explains us why steganography is so much pre-ferred by terrorists. Various images and image related tables used in this paper are generated using Matlab Si-mulation Environment.
Keywords: CIA Compliance, Cyber Crime, Image Steganography, Information Security, LSB Insertion, Terrorism.

[1]. Infosecurity Magazine article dated 02 May 2012 reports that Al-Qaeda uses Steganography to hide documents.
http://www.infosecurity-magazine.com/view/25524/alqaeda-uses-steganography-documents-hidden-in-porn-videos-found-on-memory-stick
[2] Daily Mail Online, UK article dated 01 May 2012 reported that a Treasure trove of Intelligence was embedded in porn video.
http://www.dailymail.co.uk/news/article-2137848/Porn-video-reveals-Al-Qaeda-planns-hijack-cruise-ships-execute-passengers.html#ixzz1uIgxpire
[3].The New York Times article dated Oct 30, 2001 with title "Veiled Messages of Terror May Lurk in Cyberspace" claims 9/11 attacks planned using Steganography.
[4]Wired article dated 02nd July, 2001 nicknamed Bin Laden as "the Steganography Master" http://www.wired.com/politics/law/news/2001/02/41658?currentPage=all
[5] In image processing the image matrix is represented as Columns by Row. Thus as per strict computer terminology this image will be called as 8 x 11 pixels image. But since this paper is meant for the readers of all backgrounds so we are following conventions of mathemat-ics and hence we denote the image as Row by Column.
[6] William Stallings- Cryptography and Network Security, Principles and Practices (5th Edition, Section 2.5,Pg 53).
[7] Jonathan Watkins, Steganography - Messages Hidden in Bits (15th December,2001)
http://mms.ecs.soton.ac.uk/mms2002/papers/6.pdf
[8] krypt3ia article dated 13th March 2010 with the title "Al-Qaida goes "Old School" With Tradecraft and Steganography" . http://krypt3ia.wordpress.com/2010/03/13/al-qaida-goes-old-school-with-tradecraft-and-steganography/
[9]Fabien A. P. Petitcolas,Information Hiding 5th International WorkshopOct 2002,Netherlands (Pg 20, Volue V) mentions FBI Agent Robert Hanssen using
steganography as electronic dead drop tool. http://books.google.co.in/books/about/Information_Hiding.html?id=jCKL2tSwwVMC&redir_esc=y
[10] Peter Wayner - Disappearing Cryptography: Information Hiding : Steganography & Watermarking Second Edition (Pg 413) also men-tions use of steganography as electronic dead drop tool.
http://dl.acm.org/citation.cfm?id=560589