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

Current Issue Vol. 11 Issue 2

Most Cited

Volume 6 Issue 2 June - August 2019

Research Paper

Gait Based Human Age Classification using Random Forest Classifier

M. Hema* , K. Babulu, N. Balaji*

*-*** Department of Electronics and Communication Engineering, UCEV, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India.

Hema , M., Babulu, K., & Balaji, N. (2019). Gait Based Human Age Classification using Random Forest Classifier. i-manager’s Journal on Pattern Recognition, 6(2), 1-7. https://doi.org/10.26634/jpr.6.2.16607

Abstract

In recent times, classification of human age has gained more attention. In this paper we propose to use gait identification points mainly to classify human's age based on their way of walking style. In this paper, we propose a Gait energy image Projection Model (GPM) for gait representation, which represents both Gait energy image Longitudinal Projection (GLP) and Gait energy image Transverse Projection (GTP) during a gait cycle. The proposed method mainly focuses on four parameters, namely head movement, body size, arm movement, and stride length. Regarding classification of age, OU-ISIR dataset is considered and the random forest is selected as the classifier. Moreover, the obtained experimental results are compared with the existing ones like FED, GEI, and SM. Further, descriptors are fused to check whether they get better results or not.

References

[1]. Cuntoor, N., Kale, A., & Chellappa, R. (2003, April). Combining multiple evidences for gait recognition. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings (ICASSP'03). (Vol. 3, pp. 3-33). IEEE. https://doi.org/10.1 109/ICASSP.2003.1199100

[2]. Dupuis, Y., Savatier, X., & Vasseur, P. (2013). Feature subset selection applied to model-free gait recognition. Image and Vision Computing, 31(8), 580-591. https://doi.org/10.1016/j.imavis.2013.04.001

[3]. Han, H., & Jain, A. K. (2014). Age, gender and race estimation from unconstrained face images. Department of Computer Science Engineering, Michigan State Univ., East Lansing, MI, USA, MSU Tech. Rep (MSU-CSE- 14-5), 87.

[4]. Han, H., Otto, C., & Jain, A. K. (2013, June). Age estimation from face images: Human vs. machine performance. In 2013 International Conference on Biometrics (ICB) (pp. 1-8). IEEE. https://doi.org/10.11 09/ICB.2013.6613022

[5]. Han, J., & Bhanu, B. (2005). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2), 316-322. https://doi.org/10.1109/TPAMI.2006.38

[6]. Hayashi, J. I., Yasumoto, M., Ito, H., & Koshimizu, H. (2002, August). Age and gender estimation based on wrinkle texture and color of facial images. In Object Recognition Supported by User Interaction for Service Robots (Vol. 1, pp. 405-408). IEEE. https://doi.org/10.1109/ ICPR.2002.1044736

[7]. Hema, M., Babulu, K., & Balaji, N. (2019). Gait recognition and classification using random forest algorithm. Journal of Advance Research in Dynamical & Control system, 11(2), 281-289.

[8]. Huang, G., & Wang, Y. (2007, November). Gender classification based on fusion of multi-view gait sequences. In Asian Conference on Computer Vision (pp. 462-471). Springer: Berlin, Heidelberg.

[9]. Huang, P. S. (2001). Automatic gait recognition via statistical approaches for extended template features. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31(5), 818-824 https://doi.org/10. 1109/3477.956044

[10]. Iwama, H., Okumura, M., Makihara, Y., & Yagi, Y. (2012). The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Transactions on Information Forensics and Security, 7(5), 1511-1521. https://doi.org/10.1109/ TIFS.2012.2204253

[11]. Jean, F., Bergevin, R., & Albu, A. B. (2005, May). Body tracking in human walk from monocular video sequences. In the 2^nd Canadian Conference on Computer and Robot Vision (CRV'05) (pp. 144-151). IEEE. https://doi.org/10.1109/CRV.2005.24

[12]. Khryashchev, V., Priorov, A., & Ganin, A. (2014, October). Gender and age recognition for video analytics solution. In 2014 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) (pp. 1-6). IEEE. https://doi.org/10.1109/AIPR.2014.7041914

[13]. Liu, Y., Collins, R., & Tsin, Y. (2002, May). Gait sequence analysis using frieze patterns. In European Conference on Computer Vision (pp. 657-671). Berlin, Heidelberg: Springer. https://doi.org/10.1007/3-540- 47967-8_44

[14]. Lu, J., & Tan, Y. P. (2010). Gait-based human age estimation. IEEE Transactions on Information Forensics and Security, 5(4), 761-770. https://doi.org/10.1109/ TIFS.2010.2069560

[15]. Makihara, Y., Okumura, M., Iwama, H., & Yagi, Y. (2011, October). Gait-based age estimation using a whole-generation gait database. In 2011 International Joint Conference on Biometrics (IJCB) (pp. 1-6). IEEE. https://doi.org/10.1109/IJCB.2011.6117531

[16]. Tafazzoli, F., & Safabakhsh, R. (2010). Model-based human gait recognition using leg and arm movements. Engineering Applications of Artificial Intelligence, 23(8), 1237-1246. https://doi.org/10.1016/j.engappai.2010. 07.004

[17]. Wang, L., Ning, H., Tan, T., & Hu, W. (2004). Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(2), 149-158. https://doi.org/10.1109/ TCSVT.2003.821972

[18]. Zhang, R., Vogler, C., & Metaxas, D. (2004, June). Human gait recognition. In 2004 Conference on Computer Vision and Pattern Recognition Workshop (pp.18-18). IEEE. https://doi.org/10.1109/CVPR.2004.361

[19]. Zhuang, X., Zhou, X., Hasegawa-Johnson, M., & Huang, T. (2008, December). Face age estimation using patch-based hidden markov model supervectors. In 2008 19^th International Conference on Pattern Recognition (pp. 1-4). IEEE. https://doi.org/10.1109/ICPR. 2008.4761364

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Research Paper

Smart Brain Wave Sensor for Paralyzed- A Real Time Implementation

Udigala Basavaraju Mahadevaswamy * , Siraj Ahmed**

* Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

** Sri Jayamarajendra College of Engineering, Mysuru, Karnataka, India.

Mahadevaswamy, U. B., & Ahmed, S. (2019). Smart Brain Wave Sensor for Paralyzed- A Real Time Implementation. i-manager’s Journal on Pattern Recognition, 6(2), 8-16. https://doi.org/10.26634/jpr.6.2.16618

Abstract

The paper describes about brain waves and its uses for various applications based on their frequencies and different parameters, which can be implemented as a real time application using a smart brain wave sensor system for paralyzed patients. Brain wave sensing detects a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brain waves’ sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino, which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment.

References

[1]. Robinson, P. (n.d). Mind-Reading Machines. Retrieved from https://www.cl.cam.ac.uk/research/rainbow/ emotions/mind-reading.html.

[2]. Obermaier, B., & Muller, G. R., & Gert, P. (2004). Virtual keyboard controlled by spontaneous EEG activity. Neural Systems and Rehabilitation Engineering, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11, 422-426. https://doi.org/10.1109/TNSRE. 2003.816866

[3]. Khedaskar, S. V. Rokade, M. A., Patil, B. R., & Tatwadarshi, P. N. (2018). A survey of image processing and identification techniques. VIVA-Tech International Journal for Research and Innovation, 1(1), 1-10.

[4]. Tomar, K. (2014). How mindreading computer work and how it is useful in different working areas?. International Journal of Computer Applications Technology and Research, 3(8), 505-509.

[5]. Aarti, & Renuka. (2017). Mind reading computer. International Journal of Engineering Research & Technology (IJERT), 5(3), 1-5.

[6]. Nichols, S.B. (2001). Mindreading and the cognitive architecture underlying altruistic motivation, Mind and Language, 16(4), 425-455. https://doi.org/10.1111/1468- 0017.00178

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Research Paper

Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) System For Speech Impaired People

N. Poornima* , M. Murugan , Saravana kumar N.*

, Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Poornima, N., Murugan, M., & Kumar, N. S. (2019). Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) System for Speech Impaired People. i-manager’s Journal on Pattern Recognition, 6(2), 17-24. https://doi.org/10.26634/jpr.6.2.16754

Abstract

The paper proposes an Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) system for speech impaired people that would act as a personal assistant for them. The proposed system first recognizes the sign language performed by the impeded individuals and converts it to text and speech format. The converted speech format will be given as input to the available assistant system such as Amazon Alexa. Then the VPA system responds to the speech, which would act as a personal assistant for speech impaired people. The previous method, Static and Dynamic Hand Gesture Recognition in Depth Data using Dynamic Time Warping also helps the speech impaired persons with 71.9% of gesture identification precision. To increase the gesture identification precision, IGP-VPA has been proposed. IGP-VPA uses iterative optimization process using Convolutional Neural Network (CNN), which overcomes drawbacks, such as memory intensive storage problem, poor generalization, and performance drops when training with voluminous samples. IGP-VPA is trained with 2262 gestures, each with approximately 1500 image. Indoctrinating with the huge dataset makes a difference to realize the precision of the recognition. The experimental results concluded the average precision in gesture identification as 82.9%, which is 11.78% higher compared to the Static and Dynamic Hand Gesture Recognition in Depth Data using Dynamic Time Warping.

References

[1]. Aiswarya, V., Raju, N. N., Joy, S. S. J., Nagarajan, T., & Vijayalakshmi, P. (2018, March). Hidden Markov Model-based Sign Language to Speech Conversion System In Tamil. In 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) (pp. 206- 212). IEEE. https://doi.org/10.1109/ICBSII.2018.8524802

[2]. Avola, D., Bernardi, M., Cinque, L., Foresti, G. L., & Massaroni, C. (2018). Exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures. IEEE Transactions on Multimedia, 21(1), 234-245. https://doi. org/10.1109/TMM.2018.2856094

[3]. Bao, P., Maqueda, A. I., del-Blanco, C. R., & García, N. (2017). Tiny hand gesture recognition without localization via a deep convolutional network. IEEE Transactions on Consumer Electronics, 63(3), 251-257. https://doi.org/ 10.1109/tce.2017.014971

[4]. Chakraborty, B. K., Sarma, D., Bhuyan, M. K., & MacDorman, K. F. (2017). Review of constraints on vision-based gesture recognition for Human-Computer Interaction. IET Computer Vision, 12(1), 3-15. https://doi. org/10.1049/iet-cvi.2017.0052

[5]. Cui, R., Liu, H., & Zhang, C. (2019). A deep neural framework for continuous sign language recognition by iterative training. IEEE Transactions on Multimedia, 21( 7), 1880-1891. https://doi.org/10.1109/TMM.2018.2889563

[6]. Dreuw, P., Rybach, D., Deselaers, T., Zahedi, M., & Ney, H. (2007). Speech recognition techniques for a sign language recognition system. In Eighth Annual Conference of the International Speech Communication Association, (pp. 2513-2516).

[7]. Elpeltagy, M., Abdelwahab, M., Hussein, M. E., Shoukry, A., Shoala, A., & Galal, M. (2018). Multi-modality- based Arabic sign language recognition. IET Computer Vision, 12(7), 1031-1039. https://doi.org/10. 1049/iet-cvi.2017.0598

[8]. Ghotkar, A. S., Khatal, R., Khupase, S., Asati, S., & Hadap, M. (2012, January). Hand gesture recognition for Indian Sign Language. In 2012 International Conference on Computer Communication and Informatics (pp. 1-4). IEEE. https://doi.org/10.1109/ICCCI.2012.6158807

[9]. Hussain, S., Saxena, R., Han, X., Khan, J. A., & Shin, H. (2017). Hand gesture recognition using deep learning. 2017 International SoC Design Conference (ISOCC). https://doi.org/10.1109/isocc.2017.8368821

[10]. Jimenez, J., Martin, A., Uc, V., & Espinosa, A. (2017). Mexican Sign Language Alphanumerical gestures recognition using 3D Haar-like Features. IEEE Latin America Transactions, 15(10), 2000-2005. https://doi.org/ 10.1109/TLA.2017.8071247

[11]. Lee, D., Yoon, H., & Kim, J. (2016). Continuous gesture recognition by using gesture spotting. 2016 16^th International Conference on Control, Automation and Systems (ICCAS). https://doi.org/10.1109/iccas.2016.78 32502

[12]. Shenoy, K., Dastane, T., Rao, V., & Vyavaharkar, D. (2018, July). Real-time Indian Sign Language (ISL) Recognition. In 2018 9^th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-9). IEEE. https://doi.org/ 10.1109/ICCCNT.2018.8493808

[13]. Simão, M. A., Neto, P., & Gibaru, O. (2016). Unsupervised gesture segmentation by motion detection of a real-time data stream. IEEE Transactions on Industrial Informatics, 13(2), 473-481. https://doi.org/10.1109/TII. 2016.2613683

[14]. Truong, V. N., Yang, C. K., & Tran, Q. V. (2016, October). A translator for American sign language to text and speech. In 2016 IEEE 5^th Global Conference on Consumer Electronics (pp. 1-2). IEEE. https://doi.org/10. 1109/GCCE.2016.7800427

[15]. Wu, D., Pigou, L., Kindermans, P.-J., Le, N. D.-H., Shao, L., Dambre, J., & Odobez, J.-M. (2016). Deep dynamic neural networks for multimodal gesture segmentation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8), 1583- 1597. https://doi.org/ 10.1109/tpami.2016.2537340

[16]. Wu, Y., Wu, Z., & Fu, C. (2018). Continuous Arm Gesture Recognition Based on Natural Features and Logistic Regression. IEEE Sensors Journal, 18(19), 8143- 8153. https://doi.org/10.1109/JSEN.2018.2863044

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Research Paper

Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers

A. Murugan* , S. Anu H. Nair, K. P. Sanal Kumar*

* Department of Computer Science Engineering, Thiru Kolanjiappar Govt. Arts College, Vridhachalam, Tamil Nadu, India.

Department of Computer Science Engineering, Annamalai University, Chidambaram (Deputed to WPT), Tamil Nadu, India.

* Department of Computer Science Engineering, R.V. Government Arts College, Chengalpattu, Tamil Nadu, India.

Murugan, A., Nair, S. A. H., & Kumar, K. P. S. (2019). Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers. i-manager’s Journal on Pattern Recognition, 6(2), 25-31. https://doi.org/10.26634/jpr.6.2.16761

Abstract

In today’s advanced world, skin cancer is the most common cause of death amongst humans. Skin cancer is the abnormal development of skin cells. It develops on the body always exposed to daylight, however it can happen anywhere on the body too. The greater part of the skin cancers is they are curable at early stages. So an early and fast detection of skin cancer can save a patient's life. Skin cancer (Melanoma) is one among the most deadly cancers. Using the improvement of image preprocessing in noise removal, the images of skin disease are obtained. The images are then segmented using K-means clustering technique. Segmented images are input to the feature extraction phase and the extracted images are classified by using classification techniques like Naive Bayes and PNN.

References

[1]. Ahmed, I. N., & Chaya, P. (2014). Segmentation and classification of skin cancer images. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5),1349-1353.

[2]. Ali, A. R. A., & Deserno, T. M. (2012, February). A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. In Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment (Vol. 8318, p. 83181I). International Society for Optics and Photonics. https://doi.org/10.1117/12.912389

[3]. Barata, C., Ruela, M., Francisco, M., Mendonça, T., & Marques, J. S. (2013). Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 8(3), 965-979. https://doi.org/10.1109/JSYST.2013.2271540

[4]. Hoshyar, A. N., Al-Jumaily, A., & Sulaiman, R. (2011, June). Review on automatic early skin cancer detection. In 2011 International Conference on Computer Science and Service System (CSSS) (pp. 4036-4039). IEEE. https://doi.org/10.1109/CSSS.2011.5974581

[5]. Indira, D. N. V. S. L. S., & Suprya, P. J. (2011). Detection & analysis of skin cancer using wavelet techniques. International Journal of Computer Science and Information Technologies, 2(5), 1927-1932.

[6]. Jadhav, S. R., Kamat, D. K. (2014). Segmentation based detection of skin cancer. IRF International Conference.

[7]. Jaleel, J. A., Salim, S., & Aswin, R. B. (2013, March). Computer aided detection of skin cancer. In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT) (pp. 1137-1142). IEEE. https://doi.org/10.1109/ICCPCT.2013.6528879

[8]. Mahmoud, M. K. A., Al-Jumaily, A., & Takruri, M. (2011, December). The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification. In 2011 11^th International Conference on Hybrid Intelligent Systems (HIS) (pp. 680- 685). IEEE. https://doi.org/10.1109/HIS.2011.6122188

[9]. Ramya, V. J., Navarajan, J., Prathipa, R., & Kumar, L. A. (2015). Detection of melanoma skin cancer using digital camera images. ARPN Journal of Engineering and Applied Sciences, 10(7), 3082-3085.

[10]. Ruiz, D., Berenguer, V., Soriano, A., & SáNchez, B. (2011). A decision support system for the diagnosis of melanoma: A comparative approach. Expert Systems with Applications, 38(12), 15217-15223. https://doi.org/ 10.1016/j.eswa.2011.05.079

[11]. Sadeghi, M., Razmara, M., Lee, T. K., & Atkins, M. S. (2011). A novel method for detection of pigment network in dermoscopic images using graphs. Computerized Medical Imaging and Graphics, 35(2), 137-143. https://doi.org/10.1016/j.compmedimag.2010.07.002

[12]. Salah, B., Alshraideh, M., Beidas, R., & Hayajneh, F. (2011). Skin cancer recognition by using a neuro-fuzzy system. Cancer Informatics, 10, CIN-S5950.

[13]. Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3(1), 109-118. https://doi.org/10.1016 /0893-6080(90)90049-Q

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Review Paper

Distorted Image Registration for Image Fusion – A Literature Review

K. Elaiyaraja * , M. Senthil Kumar, B. Chidambara Rajan*

* Department of Information Technology, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Department of Computer Science Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

* Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.

Elaiyaraja , K., Kumar, M. S., & Rajan, B. C. (2019). Distorted Image Registration for Image Fusion – A Literature Review. i-manager’s Journal on Pattern Recognition, 6(2), 32-40. https://doi.org/10.26634/jpr.6.2.16728

Abstract

Fusion image is obtained by processing information from various sources of images. Raw images are attained from different source or same source images. In the medical field, images are collected from several modalities like CT, MRI, etc., and merged into a single fused image. This fused image has significant features and information which can be used to diagnose easily. The fundamental or major task for medical image fusion is image registration. The image registration is done as a raw or distorted image. Distorted image registration methods are overviewed and empowering advancements in this field are done through this paper. The techniques and components for registering images are identified and given in an efficient way. The main impact of this paper is to present various techniques used for image registration in a systematic approach.

References

[1]. Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage, 54(3), 2033-2044. https://doi.org/10.1016/j. neuroimage.2010.09.025

[2]. Bajcsy, R., & Kovačič, S. (1989). Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing, 46(1), 1-21. https://doi.org/10.1016/S0734- 189X(89)80014-3

[3]. Bookstein, F. L. (1989). Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), 567-585. https://doi.org/10.1109/34. 24792

[4]. Broit, C. (1981). Optimal registration of deformed images. University of Pennsylvania Philadelphia.

[5]. Bro-Nielsen, M., & Gramkow, C. (1996, September). Fast fluid registration of medical images. In International Conference on Visualization in Biomedical Computing (pp. 265-276). Springer, Berlin, Heidelberg. https://doi.org/ 10.1007/BFb0046964

[6]. Brown, L. G. (1992). A survey of image registration techniques. ACM Computing Surveys (CSUR), 24(4), 325- 376. https://doi.org/10.1145/146370.146374

[7]. Cao, X., Yang, J., Gao, Y., Guo, Y., Wu, G., & Shen, D. (2017a). Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Medical Image Analysis, 41, 18-31. https://doi.org/10. 1016/j.media.2017.05.004

[8]. Cao, X., Yang, J., Zhang, J., Nie, D., Kim, M., Wang, Q., & Shen, D. (2017b, September). Deformable image registration based on similarity-steered CNN regression. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 300-308). Springer, Cham. https://doi.org/10.1007/978-3-319- 66182-7_35

[9]. Cifor, A., Risser, L., Chung, D., Anderson, E. M., & Schnabel, J. A. (2012, May). Hybrid feature-based logdemons registration for tumour tracking in 2-D liver ultrasound images. In 2012 9^thIEEE International Symposium on Biomedical Imaging (ISBI) (pp. 724-727). IEEE. https://doi.org/10.1109/ISBI.2012.6235650

[10]. Guo, F., Zhao, X., Zou, B., & Liang, Y. (2017). Automatic retinal image registration using blood vessel segmentation and SIFT feature. International Journal of Pattern Recognition and Artificial Intelligence, 31(11), 1757006. https://doi.org/10.1142/S0218001417570063

[11]. Han, L., Hipwell, J. H., Tanner, C., Taylor, Z., Mertzanidou, T., Cardoso, J., ... & Hawkes, D. J. (2012). Development of patient-specific biomechanical models for predicting large breast deformation. Physics in Medicine and Biology, 57(2), 455-472. https://doi.org/10. 1088/0031-9155/57/2/455

[12]. Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012). MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis, 16(7), 1423-1435. https://doi.org/10.1016/j. media.2012.05.008

[13]. Heinrich, M. P., Jenkinson, M., Brady, J. M., & Schnabel, J. A. (2011, July). Non-rigid image registration through efficient discrete optimization. In MIUA (pp. 187- 192).

[14]. Kim, M., Han, D. K., & Ko, H. (2016). Joint patch clustering-based dictionary learning for multimodal image fusion. Information Fusion, 27, 198-214. https://doi.org/10.1016/j.inffus.2015.03.003

[15]. Kim, M., Wu, G., Yap, P. T., & Shen, D. (2012). A general fast registration framework by Learning Deformation–Appearance Correlation. IEEE Trans. Image Process, 21(4), 1823-1833. https://doi.org/10.1109/ TIP.2011.2170698

[16]. Li, H., Qiu, H., Yu, Z., & Li, B. (2017). Multifocus image fusion via fixed window technique of multiscale images and non-local means filtering. Signal Processing, 138, 71- 85. https://doi.org/10.1016/j.sigpro.2017.03.008

[17]. Li, H., Yu, Z., & Mao, C. (2016). Fractional differential and variational method for image fusion and superresolution. Neurocomputing, 171, 138-148. https://doi. org/10.1016/j.neucom.2015.06.035

[18]. Li, S., Kang, X., Fang, L., Hu, J., & Yin, H. (2017). Pixellevel image fusion: A survey of the state of the art. Information Fusion, 33, 100-112. https://doi.org/10.1016/ j.inffus.2016.05.004

[19]. Li, Y., & Verma, R. (2011). Multichannel image registration by feature-based information fusion. IEEE Transactions on Medical Imaging, 30(3), 707-720. https://doi.org/10.1109/tmi.2010.2093908

[20]. Liao, S., & Chung, A. C. (2011). Nonrigid brain MR image registration using uniform spherical region descriptor. IEEE Transactions on Image Processing, 21(1), 157-169. https://doi.org/10.1109/TIP.2011.2159615

[21]. Liu, C., Yuen, J., & Torralba, A. (2011). SIFT flow: Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 978-994. https://doi.org/10. 1109/ tpami.2010.147

[22]. Mansi, T., Pennec, X., Sermesant, M., Delingette, H., & Ayache, N. (2011). iLogDemons: A demons-based registration algorithm for tracking incompressible elastic biological tissues. International Journal of Computer Vision, 92(1), 92-111. https://doi.org/10.1007/s11263- 010-0405-z

[23]. Pennec, X., Stefanescu, R., Arsigny, V., Fillard, P., & Ayache, N. (2005, October). Riemannian elasticity: A statistical regularization framework for non-linear registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 943-950). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_116

[24]. Rabbitt, R. D., Weiss, J. A., Christensen, G. E., & Miller, M. I. (1995, August). Mapping of hyperelastic deformable templates using the finite element method. In Vision Geometry IV (Vol. 2573, pp. 252-265). International Society for Optics and Photonics. https://doi.org/10.1 117/12.216419

[25]. Rivaz, H., Karimaghaloo, Z., Fonov, V. S., & Collins, D. L. (2014). Nonrigid registration of ultrasound and MRI using contextual conditioned mutual information. IEEE Transactions on Medical Imaging, 33(3), 708-725. https://doi.org/10.1109/tmi.2013.2294630

[26]. Roy, S., Wang, W. T., Carass, A., Prince, J. L., Butman, J. A., & Pham, D. L. (2014). PET attenuation correction using synthetic CT from ultrashort echo-time MR imaging. Journal of Nuclear Medicine, 55(12), 2071-2077.

[27]. So, R. W., Tang, T. W., & Chung, A. C. (2011). Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognition, 44(10-11), 2450- 2467. https://doi.org/10.1016/j.patcog.2011.04.008

[28]. Toews, M., & Wells III, W. M. (2013). Efficient and robust model-to-image alignment using 3D scaleinvariant features. Medical Image Analysis, 17(3), 271- 282. https://doi.org/10.1016/j.media.2012.11.002

[29]. Tu, Z., & Bai, X. (2010). Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(10), 1744-1757. https://doi.org/ 10.1109/tpami.2009.186

[30]. Vercauteren, T., Pennec, X., Perchant, A., & Ayache, N. (2009). Diffeomorphic demons: Efficient nonparametric image registration. NeuroImage, 45(1), S61- S72. https://doi.org/10.1016/j.neuroimage.2008.10.040

[31]. Wachinger, C., & Navab, N. (2012). Entropy and Laplacian images: Structural representations for multimodal registration. Medical Image Analysis, 16(1), 1-17. https://doi.org/10.1016/j.media.2011.03.001

[32]. Wang, K., Qi, G., Zhu, Z., & Chai, Y. (2017). A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy, 19(7), 306. https://doi.org/10.3390/e19070306

[33]. Wei, L., Cao, X., Wang, Z., Gao, Y., Hu, S., Wang, L., ... & Shen, D. (2017). Learning based deformable registration for infant MRI by integrating random forest with auto context model. Medical Physics, 44(12), 6289-6303. https://doi.org/10.1002/mp.12578

[34]. Woo, J., Stone, M., & Prince, J. L. (2015). Multimodal registration via mutual information incorporating geometric and spatial context. IEEE Transactions on Image Processing, 24(2), 757-759. https://doi.org/10. 1109/TIP.2014.2387019

[35]. Wu, G., Kim, M., Wang, Q., & Shen, D. (2013). SHAMMER: Hierarchical attribute guided, symmetric diffeomorphic registration for MR brain images. Human Brain Mapping, 35(3), 1044-1060. https://doi.org/10. 1002/hbm.22233

[36]. Wu, G., Qi, F., & Shen, D. (2006). Learning-based deformable registration of MR brain images. IEEE Transactions on Medical Imaging, 25(9), 1145-1157. https://doi.org/10.1109/TMI.2006.879320

[37]. Wu, G., Yap, P. T., Kim, M., & Shen, D. (2010). TPSHAMMER: Improving HAMMER registration algorithm by soft correspondence matching and thin-plate splines based deformation interpolation. NeuroImage, 49(3), 2225-2233. https://doi.org/10.1016/j.neuroimage.2009. 10.065

[38]. Zhang, J., Gao, Y., Park, S. H., Zong, X., Lin, W., & Shen, D. (2017). Structured learning for 3-D perivascular space segmentation using vascular features. IEEE Transactions on Biomedical Engineering, 64(12), 2803- 2812. https://doi.org/10.1109/TBME.2016.2638918

[39]. Zhu, Z., Qi, G., Chai, Y., & Li, P. (2017). A geometric dictionary learning based approach for fluorescence spectroscopy image fusion. Applied Sciences, 7(2), 161. https://doi.org/10.3390/app7020161

[40]. Zhuang, X., Arridge, S., Hawkes, D. J., & Ourselin, S. (2011). A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Transactions on Medical Imaging, 30(10), 1819-1828. https://doi.org/10.1109/TMI.2011.21 50240

[41]. Zitova, B., & Flusser, J. (2003). Image registration methods: A survey. Image and Vision Computing, 21(11), 977-1000. https://doi.org/10.1016/S0262-8856(03) 00137-9

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

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Volume 6 Issue 2 June - August 2019

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Udigala Basavaraju Mahadevaswamy * , Siraj Ahmed**

* Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India. ** Sri Jayamarajendra College of Engineering, Mysuru, Karnataka, India.

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Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) System For Speech Impaired People

N. Poornima* , M. Murugan **, Saravana kumar N.***

*,*** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India. ** Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Poornima, N., Murugan, M., & Kumar, N. S. (2019). Improved Gesture Precision Virtual Personal Assistant (IGP-VPA) System for Speech Impaired People. i-manager’s Journal on Pattern Recognition, 6(2), 17-24. https://doi.org/10.26634/jpr.6.2.16754

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Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers

A. Murugan* , S. Anu H. Nair**, K. P. Sanal Kumar***

Murugan, A., Nair, S. A. H., & Kumar, K. P. S. (2019). Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers. i-manager’s Journal on Pattern Recognition, 6(2), 25-31. https://doi.org/10.26634/jpr.6.2.16761

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Distorted Image Registration for Image Fusion – A Literature Review

K. Elaiyaraja * , M. Senthil Kumar**, B. Chidambara Rajan***

Elaiyaraja , K., Kumar, M. S., & Rajan, B. C. (2019). Distorted Image Registration for Image Fusion – A Literature Review. i-manager’s Journal on Pattern Recognition, 6(2), 32-40. https://doi.org/10.26634/jpr.6.2.16728

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M. Hema* , K. Babulu, N. Balaji*

* Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.

** Sri Jayamarajendra College of Engineering, Mysuru, Karnataka, India.

N. Poornima* , M. Murugan , Saravana kumar N.*

, Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

A. Murugan* , S. Anu H. Nair, K. P. Sanal Kumar*

K. Elaiyaraja * , M. Senthil Kumar, B. Chidambara Rajan*