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i-manager's Journal on Pattern Recognition (JPR)

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Volume 1 Issue 4 December - February 2015

Research Paper

Efficient Detection of Suspected areas in Mammographic Breast Cancer Images

Bhagwati Charan Patel* , G. R. Sinha**

* Research Scholar, Department of Information Technology, Shri Shankaracharya Technical Campus Bhilai, India.

** Professor and Associate Director, Shri Shankaracharya Technical Campus Bhilai, India.

Patel, B. C., and Sinha, G. R. (2015). Efficient Detection of Suspected areas in Mammographic Breast Cancer Images. i-manager’s Journal on Pattern Recognition, 1(4), 1-10. https://doi.org/10.26634/jpr.1.4.3305

Abstract

Breast cancer is the most common type of cancers found in women all across the world. Mammography is considered as an effective tool for early detection and diagnosis of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and micro-calcifications or calcifications. The abnormalities present in breast images are characterized by using a range of features that may be missed or misinterpreted by radiologists while reading large amount of mammographic images during cancer screening process. Computer-aided diagnosis (CAD) systems have been developed to assist radiologists to provide an accurate diagnosis. An attempt has been made to improve the classification performance of CAD system in which shape and texture are used in analyzing region of interest (ROI) of mammographic images of breast. The method detects ROI by combining edge and region criteria and then feature extraction method helps extract few statistical parameters such as sensitivity and specificity to evaluate the performance of the proposed method. The sensitivity of the proposed method is 97.5% and specificity is 91.2% that produced an accuracy of 96.6%. Size of tumor is also computed and classification stage of breast cancer is identified.

References

[1]. http://www.cancermission.com/2011/08/breastcancer- in- india.html

[2]. Sinha G.R. and Patel B. C (2014). Medical Image Processing: concepts and applications, PHI Learning Private Limited.

[3]. Cheng, H.D., Shi, X.J., Min, R., Hu, L.M., Cai, X.P and Du, H.N (2006). “Approaches for Automated Detection and Classification of Masses in Mammograms”, Journal of Pattern Recognition, Vol.39 (4), pp. 646–652.

[4]. Rangayyan, R.M., Ayres, F.J., and Desautels, J.E.L (2007). “A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs”, Journal of the Franklin Institute, Vol. 8, pp.312–318.

[5]. Bruce, L. M., & Adhami, R. R (1999). “Classifying Mammographic Mass Shapes Using the Wavelet Transform Modulus-Maxima Method", IEEE Transaction on Medical Imaging, Vol.1, pp. 1170-1177.

[6]. Arnau, O (2007). "Automatic mass segmentation in mammographic images", Department of Electronics, Computer Science and Automatic Control, Girona.

[7]. Mumford D. and Shah J (1989). “Optimal approximation by piecewise smooth functions and associated variation problems”, Journal of Pure Applied Mathematics, Vol.10, pp. 577-585.

[8]. Mumford D., and Shah J (1985). “Boundary Detection by Minimizing Functional”, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, pp.176-185.

[9]. Shah J (1994). “Piecewise smooth approximations of functions”, Calculus of Variations and Partial Differential Equations, Vol.2, pp. 315-328.

[10]. Chan T. F., and Vese L.A (2001). “Active contours without edges”, IEEE Transaction on Image Processing, Vol.10, pp. 266–277.

[11]. Bresson X., Esedoglu S., Vandergheynst P., Thiran J. P. and Osher S (2005). “Fast global minimization of the active contour/ snake model”, Journal of Mathematical Imaging and Vision, Vol.28 (2), pp. 51-167.

[12]. Rousson M., and X (2007). “Convergence approximation to piecewise smooth medical image segmentation”, In Medical Image Computing and Computer Assisted Intervention, Vol. 4, pp. 495-502.

[13]. Cremers D., Schmidt F.R., and Barthel F (2007). “Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions”, In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-6.

[14]. Leventon M.E., Eric W., Grimson L., and Faugeras O (2008). “Statistical shape in_uence in geodesic active contours”, In International Conference on Computer Vision, Vol.1, pp. 317-323.

[15]. Zheng Li and Andrew K. C (2001). “An Artificial Intelligent Algorithm for Tumor Detection in Screening Mammogram”, IEEE Transactions on Medical Imaging, Vol. 20(7), pp. 45-52.

[16]. Singhand S. and Gupta, P. R (2001). “Breast Cancer detection and Classification using Neural Network”, International Journal of Advanced Engineering Sciences and Technologies, Vol.6 (1), pp. 4-9.

[17]. Pagonis N, Cavouras D, and Sidiropoulos K (2010). “Improving The Classification Accuracy of Computer Aided Diagnosis through Multimodality Breast Imaging”, e-Journal of Science & Technology (e-JST), Vol.2 (5), pp. 33-39.

[18]. Kowal M, Filipczuk P, Obuchowicz A. and Józef Korbicz (2011). “Computer-aided Diagnosis of Breast Cancer Using Gaussian Mixture Cytological Image Segmentation”, Journal of Medical Informatics & Technologies, Vol.17, pp. 257-262.

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[20]. Jain, A.K., Duin, R.P.W., and Mao, J (2000). “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22 (1), pp. 4–12.

[21]. Patel B. C. and Sinha G. R (2012), “Energy and Region based Detection and Segmentation of Breast Cancer Mammographic Images”, International Journal of Image, Graphics and Signal Processing, Vol. 6, pp. 44- 51.

[22]. Chunming L, Chiu-Y.K., John C. and Zhaohua D (2010), “Minimization of Region-Scalable Fitting Energy for Image Segmentation”, IEEE Transactions on Image Processing, Vol.17, pp.10-18.

[23]. Lankton S. and Tannenbaum A (2008), “Localizing Region-Based Active Contours”, IEEE Transactions on Image Processing, Vol.17, pp. 27-36.

[24]. Kullback, S.; and Leibler, R.A (2011). “On Information and Sufficiency”, Journal of Mathematical Statistics, Vol.22 (1), pp.79–86.

[25]. Blake C. and Isard G (2006), “Model Based multiview Active Contours for Quality Inspection”, Journal of Computer Vision and Graphics, Vol. 32, pp. 565-574.

[26]. Durbin R., Szeliski R., and Yuille A (1999), “An analysis of the elastic net approach to the travelling salesman problem”, Journal of Neural Computation, Vol.1, pp.348- 358.

[27]. Chenga, H.D.; Shana, J.; Jua, W.; Guoa, Y. and Zhangb, L (2010), “Automated breast cancer detection and classification using ultrasoundimages: A survey”, Journal of Pattern Recognition, Elsevier Academic, Press, Vol.43, pp.299– 317.

[28]. Sampat, M., Markey, M., and Bovik, A (2002), “ Computer-Aided Detection and Diagnosis in Mammography”, In A. Bovik, Handbook of Image and Video Processing, pp.1195-1217.

[29]. Patel B.C. and Sinha G.R (2014), “Mammography Feature Analysis and mass Detection in Breast Cancer Images”, International conference on Electronic System, Signal Processing and Computing Technology (ICESC 2014), IEEE Conference, January 9-11, Nagpur, pp.474- 478.

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Research Paper

An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition

Y.C. Kiran* , V.N. Manjunath Aradhya, C. Naveena*

* Associate Professor, Department of Information Science and Engineering, Dayanada Sagar College of Engineering, Bengaluru, India

Associate Professor, Department of MCA, S. J. College of Engineering, Mysuru, India.

* Professor, Department of Computer Science and Engineering, HKBK College of Engineering, Bengaluru, India.

Kiran, Y. C., Aradhya, V. N. M., and Naveena, C. (2015). An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 1(4), 11-20. https://doi.org/10.26634/jpr.1.4.3306

Abstract

The Ridgelet Transform [6] was introduced as a sparse expansion for functions of continuous spaces that are smooth away from discontinuities along lines. The powerful properties of the ridgelets are catching and representing monodimensional singularities in bi-dimensional space [8]. Using these effective properties, in this paper the authors propose an effect of Ridgelet Transform on various Similarity/Distance Measure Techniques namely Euclidean Distance, Modified Squared Euclidean Distance, Correlation Distance and Angle Distance for an unconstrained bi-lingual handwritten character recognition. Ridgelet Transform is used to extract a character image of low pass energy and is then fed to PCA for feature extraction. We conducted experiment on very large database of bi-lingual handwritten characters (Kannada and English). The database contains the samples of 22,600 and the effect of the proposed method is compared with the standard PCA & FLD methods. Among the above mentioned similarity/distance measure techniques the better recognition accuracy were achieved using angle distance measure.

References

[1]. D. Acharya, N.V.S. Reddy, and K. Makkithaya (2008). “Multilevel classifiers in Recognition of Handwritten Kannada Numerals”. In the Proceedings of world Academy of Science, Engineering and Technology, pp. 308–313.

[2]. J.H. Alkhateed, J. Ren, J. Jiang, and H.A. Muhtaseb (2011). “Offline Handwritten Arabic Cursive Text Recognition using Hidden Markov Models and Reranking”. Pattern Recognition Letters, Vol. 32, pp.1081–1088.

[3]. V. N. M. Aradhya, G. H. Kumar, and S. Noushath (2007). “Robust Unconstrained Handwritten Digit Recognition using Radon Transform”. In the Proceedings of International Conference on Signal Processing, Communications and Networking (ICSCN’ 07), pp. 626 – 629.

[4]. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman (1997). “Eigenfaces versus Fisherfaces: Recognition using class Specific Linear Projection”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, pp. 711–729.

[5]. U. Bhattacharya and B.B.Chaudhari (2009). “Handwritten Numeral databases of Indian Scripts and Multistage Recognition of Mixed Numerals”. IEEE Transaction on Pattern Analysis and machine Intelligence, Vol. 31, pp. 444–457.

[6]. E.J. Candes and D.L. Donoho (1999). “Ridgelets: a key to higher dimensional Intermittency?” Philosophical Transaction of the Royal Society, pp. 2495–2509.

[7]. M.N. Do and M. Vetterli (2002). Finite Ridgelet Transform for Image Representation”. In IEEE Transactions on Image Processing.

[8]. L. Granai, F. Moschetti, and P. Vandergheynst (2003). “Ridgelet Tansform applied to Motion Comprensated Images”. In the Proceedings of ICASSP2003, pp. 381–384.

[9]. M. Li, C. Wang, and R. Dai (2008). “Unconstrained handwritten character recognition based on WEDF and Neural Network”. In IEEE Transaction on Image Processing Theory, pp. 1143–1148.

[10]. S. A. Mahmoud and M. H. Abu-Amara (2010). “The use of Radon Transform in handwritten Arabic (Indian) Numerals Recognition”. Journal of World Scientific and Engineering Academy and Society (WSEAS), Vol. 9(3), pp. 252–267.

[11]. D. Nasaien, H. Haron, and S.S. Yahunaiz (2010). “Support Vector Machine (SVM) for english handwritten nd character recognition”. In the Proceedings of 2 International conference on Computer Engineering and Applications, pp. 249–252.

[12]. C. Naveena and V. N. Manjunath Aradhya (2011). “An impact of Ridgelet Transform in handwritten recognition: A study on very large dataset of kannada script”. In the Proceedings of International Conference on World Congress on Information and Communication Technologies (WICT-2011), pp. 622 – 625.

[13]. H. Nemmour and Y. Chibani (2011). “Handwritten Arabic Word Recognition based on Ridgelet Transform and Support Vector Machines”. In the Proceedings of International Conference on High Performance Computing and Simulation (HPCS), IEEE Computer Society, pp. 357 – 361.

[14]. S.K. Niranjan, V. Kumar, G. Hemantha Kumar, and V.N.M Aradhya (2009). “FLD based Unconstrained Handwritten Kannada Character Recognition”. International Journal of Database Theroy and Application, Vol. 2, pp. 21–26.

[15]. S.K. Sangame, R.J. Ramteke, and R. Benne (2009). “Recognition of Isolated Handwritten Kannada Vowels”. In the Proceedings of Advances in Computational Research, pp. 52–55.

[16]. N. Shanthi and K. Duriswamy (2009). “A Novel SVMbased Handwritten Tamil Character Recognition System”. Pattern Analysis Application, (Springer), Vol. 13, pp. 173–180.

[17]. T.H. Su, T.W. Zhang, D.J. Guan, and H.J. Huang (2009). “Offline Recognition of Realistic Chinese handwriting using Segmentation-Free Stratergy”. Pattern Recognition, Vol. 42, pp. 167–182.

[18]. M. Turk and A. Pentland (1991). “Eigenfaces for Recognition”. Journal of Cognition and Neuroscience, Vol. 3, pp. 71–86.

[19]. H. Zhang, J. Yang, W. Deng, and J. Guo (2008). “Handwritten Chinese Character Recognition using Local Discriminant Projection with prior information”. In the th Proceedings of 19 International Conference on Pattern Recognition (ICPR 2008), pp. 1–4.

[20]. C. Zhong, Y. Ding, and J. Fu (2010). “Handwritten Character Recognition based on 13-point feature of Skeleton and Self-organizing Competition Network”. In the Proceedings of International conference on Intelligent Computation Technology and Automation, pp. 414–417.

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Research Paper

Pattern Analysis Based CRF Segmentation and MRF Classification for Skin Lesions in Dermoscopic Images

Delfin Ruby S* , Subbulakshmi. N, S.Allwin Devara*

* P.G.Scholar, Department of Information Technology, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur.

Assistant Professor, Department of Information Technology, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur.

* Assistant Professor, Department of Electronics and Communication Engineering, Francis Xavier Engineering College, Tirunelveli.

Ruby, S. D., Subbulakshmi, N., and Devaraj, S. A. (2015). Pattern Analysis Based CRF Segmentation and MRF Classification for Skin Lesions in Dermoscopic Images. i-manager’s Journal on Pattern Recognition, 1(4), 21-27. https://doi.org/10.26634/jpr.1.4.3307

Abstract

In this paper, Conditional Random Field Based Segmentation and different model-based Markov Random Field(MRF) classification for skin lesions in dermoscopic images are proposed. This method is used in the pattern analysis framework for diagnosis of melanoma by dermatologists. A Dermoscopic image is smoothened by Wiener Filer Method and converted into Grayscale Image. Then the image is diluted which gives the contour of an image. The input image is segmented by Conditional Random Field Technique. The Estimated CPU time is calculated which gives less Processing Time. Then classification is carried out by an image retrieval approach with different distance metrics. These features are supposed to follow Gaussian Model, Gaussian Mixture Model, and Bag-of-features Histogram Model. The main aim of this paper is the classification of an entire pigmented lesion and analysis of the texture of an image. The image database is extracted from a public Atlas of Dermoscopy. Receiver Operating Characteristics (ROC) Curve is used to evaluate the performance of Segmentation Process which gives more accuracy. Finally, the skin lesions with their levels were analysed.

References

[1]. G. Argenziano, H. Soyer, and Chimentiet al., (2003). “Dermoscopy of pigmentedskin lesions: Results of a consensus meeting via the Internet,”J. Am. Acad. Dermatol., Vol. 48, No. 5, pp. 679–693.

[2]. H. Pehamberger, A. Steiner, and K. Wolff, (1987). “In vivo epiluminescencemicroscopy of pigmented skin lesions.I. Pattern analysis of pigmentedskin lesions,” Journal of the American Acadamy of Dermatology, Vol. 17, No. 4, pp. 571–583.

[3]. G. Argenziano and H. Soyeret al.,(2000). “Interactive Atlas of Dermoscopy”. Milan, Italy: EDRA-Medical Publishing New Media.

[4]. G. Rezze, B. De Sá, and R. Neves, (2006). “Dermoscopy: The Pattern Analysis”, AnaisBrasileiros Dermatologia, Vol. 81, No. 3, pp. 261–268.

[5]. T. Tanaka, S. Torii, I. Kabuta, K. Shimizu, and M. Tanaka, (2008). “Pattern Classification of Nevus with Texture Analysis,” IEEJ Transaction on Electrical and Electronics Engineering, Vol. 3, No. 1, pp. 143–150.

[6]. A. GolaIsasi, B. GarcíaZapirain, and A. MéndezZorrilla, (2011). “Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms,” Computers in Biology and Medicine, Vol. 41, No. 9, pp. 742–755.

[7]. Q.Abbas, M. Celebi, C. Serrano, I. FondónGarcía, and G. Ma, (2013). “Pattern Classification of Dermoscopy Images: A Perceptually Uniform Model”, Pattern Recognition., Vol. 46, No. 1, pp. 86–97.

[8]. C. Serrano and B. Acha, (2009). “Pattern Analysis of Dermoscopic Images based on Markov Random Fields,” Pattern Recognition., Vol. 42, No. 6, pp.1052–1057.

[9]. C. Mendoza, C. Serrano, and B. Acha, (2009). “Pattern Analysis of Dermoscopic Images based on FSCM Color Markov Random Fields,” in Advanced Concepts for Intelligent Vision Systems. New York: Springer, Vol. 5807, Lecture Notes in Computer Science, pp. 676–685.

[10]. A. Sáez, C. S. Mendoza, B. Acha, and C. Serrano, (2013). “Development and Evaluation of perceptually adapted Colour gradients,” IET Image Process Vol.7, No.4, pp.355-363.

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Research Paper

Single Instruction Multiple Data (SIMD) approach for Efficient Fractal Image Encoding using Distributed Architecture

Akhilesh Kumar* , G. R. Sinha, Vikas Dilliwar*

* PG Student, Department of Computer Science & Engineering, Shri Shankarachrya Technical Campus Bhilai, India.

Professor and Associate Director, Shri Shankarachrya Technical Campus Bhilai, India.

* Research Scholar, Department of Computer Science & Engineering, Chhattisgarh Institute of Technology, Rajnandgaon, India.

Kumar, A., Sinha, G. R., and Dilliwar, V. (2015). Single Instruction Multiple Data (SIMD) approach for Efficient Fractal Image Encoding using Distributed Architecture. i-manager’s Journal on Pattern Recognition, 1(4), 28-34. https://doi.org/10.26634/jpr.1.4.3308

Abstract

There are several application areas where tremendous computational resources are required including image processing, big data and genetic mapping which are computationally intensive areas. Huge computing resources are required to solve such complex problems and powerful computing environment is needed. An emphasis is made on fractal image compression, which requires higher computing needs to solve. Single Instruction Multiple Data approach is followed using distributed architecture. The research compares the performance on the basis of speed up and encoding time. It was found that image compression requires more computing power to solve in lesser time. In this paper, the parallel algorithms are developed using Distributed Fractal Image Encoding Architecture (DFIE) as Single Instruction Multiple Data (SIMD) approach

References

[1]. M. Barnsley (1988), Fractals Everywhere. Academic Press Inc., San Diego.

[2]. A. Jacquin (1992), “Image coding based on a fractal theory of iterated contractive image transformations," IEEE Transaction on Image Processing, Vol.1, No.1 pp. 18- 30, January.

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[4]. B. Hurtgen (1994), ”On the Convergence of Fractal Transforms”, IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP, Vol. 5, pp. 561- 564.

[5]. Mitt Xue, Timothy Hansott, and Alain Merigot (1994), “A Massively Parallel Implementation of Fractal Image Compression”, Proceedings of IEEE International Conference on Image Processing, November 13-16, Austin, Texas.

[6]. Jackson, D.J. and Blom T (1995). “A parallel fractal image compression algorithm for hypercube th multiprocessors”, Proceedings of IEEE 27 International Southeastern Synopsium on System Theory, pp. 274-278, March 12-14, Starkville, Mississippi

[7]. Toh Guan Nge and Wong Kin Keong (1997), “Parallel implementation of fractal image compression”, Proceedings of IEEE International Symposium on Consumer Electronics, pp. 169-172

[8]. S.K Chow, M. Gillies and S.L. Chan (1997), “Parallel Implementation of Fractal Image Compression Using Multiple Digital Signal Processors” Lecture Notes on Computer Science, Vol. 1751, pp.714-721. Springerverlog.

[9]. Kevin P. Acken, Mary Jane Irwin and Robert M. Owens (1998), “A Parallel ASIC Architecture for Efficient Fractal Image Coding”, Journal of VLSI signal processing systems for signal, image and video technology, Vol. 19, No. 2, pp 97-113 , Springer-verlog.

[10]. Paolo Palazzari, Moreno Coli and GuglielmoLulli (1999), “Massively parallel processing approach to fractal image Compression with near-optimal coefficient quantization”, Journal of Systems Architecture: the EUROMICRO Journal - Special issue on parallel image processing (PIP), Vol. 45, No. 10, pp. 765-779, Elsevier , April.

[11]. Shinhaeng Lee and HirotomoAso (1999), “A parallel Architecture for High Speed Fractal Image Coding”, th Proceedings of IEEE 4 International Symposium on Parallel Architectures, Algorithms, and Networks (I-SPAN '99), pp.88-93, Perth/Fremantle, WA

[12]. Hua Cao, Xi-jinGu (2010), “OpenMP Parallelization of Jacquin Fractal Image Encoding”, IEEE International Conference on E-Product E-Service and E-Entertainment (ICEEE,) pp.1-4, Nov 7-9, Henan.

[13]. Yan Fang, Hang Cheng, Meiqing Wang (2011), “Parallel Implementation of Fractal Image Compression th in Web Service Environment”, Proceedings of IEEE 10 [13]. Yan Fang, Hang Cheng, Meiqing Wang (2011), “Parallel Implementation of Fractal Image Compression th in Web Service Environment”, Proceedings of IEEE 10

[14]. Wakatani A, Mede M. and Tanaka K.,(2011). “GPGPU implementation of adaptive fractal image coding algorithm using index vectors”, IEEE International Conference on Industrial Informatics (INDIN), pp. 316 – 321, July 26-29, Caparica, Lisbon.

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[17]. BhagwatiCharan Patel and G.R.Sinha (2010), “Early detection of breast cancer using self similar fractal method”, International Journal of Computer Application, New York USA, Vol. 10, No. 4, pp. 39-43, November.

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[20]. Akhilesh Kumar, G R Sinha and VikasDilliwar (2014), “Distributed Parallel Method for Efficient Fractal Image Encoding”. IJCA Proceedings on National Conference on Recent Advances in Information Technology NCRAIT, Vol. 2, pp. 32-37, February. Published by Foundation of Computer Science, New York, USA.

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Research Paper

FPGA based parallel hardware architecture for real time object classification

Sabarinathan E.* , E. Manoj**

* PG Student, Department of Electrical and Electronics Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, India.

** Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India.

Sabarinathan, E., and Manoj, E. (2014). FPGA based parallel hardware architecture for real time object classification. i-manager’s Journal on Pattern Recognition, 1(4), 35-44. https://doi.org/10.26634/jpr.1.4.3309

Abstract

Efficacious Recognition and consistent identification of visual features is an important problem in applications, such as Object Recognition, Structure from Motion, Image Indexing and Visual Localization. The input data takes, many forms such as Video Sequences, views from Multiple Cameras or Multi-dimensional data from a scanner. Concurrent performance is a perilous demand to utmost of these applications, which necessitate the finding and corresponding of the visual features in real time. Although Feature Recognition and Identification Methods have been studied in the Literature, due to their Computational Intricacy, pure software execution by Unique Hardware is far from suitable in their performance for Real Time Applications. The existing system consists of Scale Invariant Feature Transform (SIFT) for Feature Detection and Binary Robust Independent Elementary Features (BRIEF) for Feature Description and Matching. This system fails to detect the features which are invariant to scale, change in viewpoint and illumination, and the addition of noise. The proposed system consists of Wavelet Feature Extraction Method, and for classification process, Subtractive Clustering is used. This system reduces time consumption and overall system complexity. This paper focuses on a different hardware design to enable real-time performance of founding correspondences between idealistic sequential frames of high-resolution 720 p (1280 x 720) video. Due to these assistances, the proposed system attains feature detection and matching at 60 frame/s for 720-p video. Its processing speed can encounter and even overdo the demand of most realistic concurrent video analytics applications.

References

[1]. Ameer Mohammed Baqer, Hind Rostom Mohammed (2013). “Color Based Segmentation Iris image for Secure Distributed Systems”. In International Journal of Scientific and Engineering Research, Vol. 4, No. 12.

[2]. Agrawal,M.,(2008). “Konolige,K.,Blas,M.: Censure: Center surround extremas for Real-Time Feature Detection and Matching”. In Proceedings on European Conference Computer Vision, Vol. 5305, pp. 102–115.

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[6]. Fezza, S., Larabi, M, Faraoun, K (2014). “Feature Based Color Correction of Multiview Video for Coding and Rendering Enhancement”. In IEEE Transactions on Circuits and Video Technology, pp. 1486 – 1498.

[7]. Jianhui Wang, Sheng Zhong, Luxin Yan, Zhiguo Cao (2014). “An Embedded System on Chip Architecture for Real time Visual Detection and Matching”. In IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No.3.

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[12]. Schaeferling,M., Kiefer,G (2011). “Object recognition on a chip: A complete SURF-based system on a single FPGA”. In Proceedings on International Conference FPGAs Reconfiguration, pp. 49–54.

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[15]. Sabarinathan,E., Senthilkumar,M (2015). “An Embedded System-on-Chip Architecture for Real Time Visual Detection and Matching”. In Proceedings on National Conference on Futuristic Computing and Communication Technologies.

[16]. Sabarinathan. E and Senthilkumar. M (2015). “FPGA Implementation of Real time Visual detection and Matching”. In Proceedings on International Conference on Recent Trends in Information and Communication, pp.101-107.

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i-manager's Journal on Pattern Recognition (JPR)

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Volume 1 Issue 4 December - February 2015

Efficient Detection of Suspected areas in Mammographic Breast Cancer Images

Bhagwati Charan Patel* , G. R. Sinha**

* Research Scholar, Department of Information Technology, Shri Shankaracharya Technical Campus Bhilai, India. ** Professor and Associate Director, Shri Shankaracharya Technical Campus Bhilai, India.

Patel, B. C., and Sinha, G. R. (2015). Efficient Detection of Suspected areas in Mammographic Breast Cancer Images. i-manager’s Journal on Pattern Recognition, 1(4), 1-10. https://doi.org/10.26634/jpr.1.4.3305

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An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition

Y.C. Kiran* , V.N. Manjunath Aradhya**, C. Naveena***

Kiran, Y. C., Aradhya, V. N. M., and Naveena, C. (2015). An Effect of Ridgelet Transform on Various Distance Measure Techniques in Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 1(4), 11-20. https://doi.org/10.26634/jpr.1.4.3306

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Pattern Analysis Based CRF Segmentation and MRF Classification for Skin Lesions in Dermoscopic Images

Delfin Ruby S* , Subbulakshmi. N**, S.Allwin Devara***

Ruby, S. D., Subbulakshmi, N., and Devaraj, S. A. (2015). Pattern Analysis Based CRF Segmentation and MRF Classification for Skin Lesions in Dermoscopic Images. i-manager’s Journal on Pattern Recognition, 1(4), 21-27. https://doi.org/10.26634/jpr.1.4.3307

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Single Instruction Multiple Data (SIMD) approach for Efficient Fractal Image Encoding using Distributed Architecture

Akhilesh Kumar* , G. R. Sinha**, Vikas Dilliwar***

Kumar, A., Sinha, G. R., and Dilliwar, V. (2015). Single Instruction Multiple Data (SIMD) approach for Efficient Fractal Image Encoding using Distributed Architecture. i-manager’s Journal on Pattern Recognition, 1(4), 28-34. https://doi.org/10.26634/jpr.1.4.3308

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FPGA based parallel hardware architecture for real time object classification

Sabarinathan E.* , E. Manoj**

* PG Student, Department of Electrical and Electronics Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, India. ** Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India.

Sabarinathan, E., and Manoj, E. (2014). FPGA based parallel hardware architecture for real time object classification. i-manager’s Journal on Pattern Recognition, 1(4), 35-44. https://doi.org/10.26634/jpr.1.4.3309

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* Research Scholar, Department of Information Technology, Shri Shankaracharya Technical Campus Bhilai, India.

** Professor and Associate Director, Shri Shankaracharya Technical Campus Bhilai, India.

Y.C. Kiran* , V.N. Manjunath Aradhya, C. Naveena*

Delfin Ruby S* , Subbulakshmi. N, S.Allwin Devara*

Akhilesh Kumar* , G. R. Sinha, Vikas Dilliwar*

* PG Student, Department of Electrical and Electronics Engineering, K.S.R. College of Engineering, Tiruchengode, Namakkal, India.

** Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, India.