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i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 10 Issue 4

Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino

Most Cited

Volume 4 Issue 4 October - December 2017

Research Paper

Qualitative Performance Metrics For Object Tracking Algorithms

Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla, Abdul-Karim*

* Associate Professor, Aditya Institute of Technology and Management, Hyderabad, India.

Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.

* Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2017). Qualitative Performance Metrics for Object Tracking Algorithms. i-manager’s Journal on Image Processing, 4(4), 1-15. https://doi.org/10.26634/jip.4.4.14164

Abstract

In this paper, Qualitative metrics for object tracking algorithms is introduced. The two algorithms are subjectively analyzed, and four different types of datasets based on occlusion strength, contrast, illumination, and clutter are taken. Each dataset is of simple and hard type. Gaussian Mixture Model (GMM) and Weighted Running Window Background (WRWB) model based GMM are the two human tracking algorithms taken for subjective analysis. The algorithms are verified for both easy and hard type datasets. The performance metrics show that these parameters are best to analyze any object tracking algorithms subjectively. Precision and recall parameters are considered for subjective analysis.

References

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[3]. Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The CLEAR MOT metrics. Journal on Image and Video Processing, 2008, 1-10.

[4]. Birchfield, S. T., & Rangarajan, S. (2005). Spatiograms versus histograms for region-based tracking. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 2, pp. 1158- 1163). IEEE.

[5]. Brown, L. M., Senior, A. W., Tian, Y. L., Connell, J., Hampapur, A., Shu, C. F., ... & Lu, M. (2005). Performance evaluation of sur veillance systems under var ying conditions. In Proceedings of IEEE PETS Workshop (pp. 1- 8). IEEE.

[6]. Chau, D. P., Brémond, F., Thonnat, M., & Corvée, E. (2011). Robust mobile object tracking based on multiple feature similarity and trajectory filtering. In VISAPP11 (pp. 569-574).

[7]. Chu, D. M., & Smeulders, A. W. (2010). Thirteen hard cases in visual tracking. In Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on IEEE (pp. 103-110). IEEE.

[8]. Comaniciu, D., Ramesh, V., & Meer, P. (2003). Kernelbased object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 564-577.

[9]. Corvee, E., & Bremond, F. (2011). Haar like and LBP based features for face, head and people detection in video sequences. In International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011) (p. 10).

[10]. Dadi, H. S., Pillutla, G. K. H., & Makkena, M. L. (2017a). Human tracking using weighted running window background model based GMM. i-manager's Journal on Software Engineering, 12(2), 44-54.

[11]. Dadi, H. S., Pillutla, G. K. M., & Latha Makkena, M. (2017b). Human tracking under severe occlusions. imanager's Journal on Software Engineering, 12(1), 29-37.

[12]. Hager, G. D., Dewan, M., & Stewart, C. V. (2004). Multiple kernel tracking with SSD. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (Vol. 1, pp. 1-10). IEEE.

[13]. Lee, D. S. (2005). Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 827- 832.

[14]. Li, Y., Huang, C., & Nevatia, R. (2009). Learning to associate: Hybridboosted multi-target tracker for crowded scene. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE (pp. 2953-2960). IEEE.

[15]. Nghiem, A. T., Bremond, F., Thonnat, M., & Ma, R. (2007). A new evaluation approach for video processing algorithms. In Motion and Video Computing, 2007. WMVC'07. IEEE Workshop on (pp. 1-8). IEEE.

[16]. Paalanen, P., Kamarainen, J. K., Ilonen, J., & Kälviäinen, H. (2006). Feature representation and discrimination based on Gaussian Mixture Model probability densities-practices and algorithms. Pattern Recognition, 39(7), 1346-1358.

[17]. Rafael C. Gonzalez, Richard E. Woods, & Steven L. Eddins. (2011). Digital Image Processing using MATLAB®, nd 2 edition TMH Publications, New Delhi.

[18]. Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision (pp. 17-35). Springer, Cham.

[19]. Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2), 17-17.

[20]. Sankaranarayanan, S., Bremond, F., & Tax, D. (2012). Qualitative evaluation of detection and tracking performance. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on (pp. 362-367). IEEE.

[21]. Santosh, D. H., & Mohan, P. K. (2014a). Tracking manifold objects in motion using Gaussian mixture model and blob analysis. In IEEE International Conference on Convergence of Technology-2014 (pp. 1-7). IEEE.

[22]. Santosh, D. H., & Mohan, P. K. (2014b). Multiple objects tracking using extended Kalman Filter, GMM and Mean Shift algorithm - A comparative study. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1484- 1488). IEEE.

[23]. Santosh, D. H., & Mohan, P. K. (2015). Multiple human tracking and prediction under severe occlusions using GMM and Kalman filter. IJAER, 10(11), 29385-29404.

[24]. Wang, Y., Zhang, J., Wu, L., & Zhou, Z. (2010). Mean shift tracking algorithm based on multi-feature space and grey model. Journal of Computational Information Systems, 6(11), 3731-3739.

[25]. Wu, H., Sankaranarayanan, A. C., & Chellappa, R. (2010). Online empirical evaluation of tracking algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1443-1458.

[26]. Yao, J., & Odobez, J. M. (2008, October). Fast Human Detection from Videos using Covariance Features. In The Eighth International Workshop on Visual Surveillance-VS2008.

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Research Paper

Classification of Tumor Using Gaussian Filter and Canny Edge Detection Technique

C.Rethinam*

Department of Computer Science and Engineering, Nehru Institute of Technology, Coimbatore, Tamil Nadu, India.

Rethinam, C. (2017). Classification of Tumor Using Gaussian Filter and Canny Edge Detection Technique. i-manager’s Journal on Image Processing, 4(4), 16-21. https://doi.org/10.26634/jip.4.4.14163

Abstract

Brain tumor refers to the growth of abnormal tissues in human brain. Brain tumor can be cancerous or non-cancerous which may affect people of any age group. In this work, Gaussian Filter and Canny Edge Detection Technique for brain MRI segmentation are used. Gaussian filter is utilized to remove the noise and provide high quality images, whereas the Canny Edge Detector is used for Edge Detection process to detect all edges accurately and avoid the missing edges. The results obtained from this research work provide easy identification of tumor and to find out the exact location of tumor in human brain.

References

[1]. Abdulrazzaq, M. M., & Noah, S. A. (2015). Improving the Annotation accuracy of Medical Images in Image CLEF med 2005 using K-Nearest Neighbor (kNN) Classifier. Asian Journal of Applied Sciences, 8(1), 16-26.

[2]. Abirami, N., Karthik, S., & Kanimozhi, M. (2015). Automated Brain Tumor Detection And Identification using Medical Imaging. International Journal of Research in Computer Applications and Robotics, 3(9), 85-91.

[3]. Balasubramaniam, D., Saranya, S., Sutha, E., & Umamaheswari, U. (2016). Tumor Segmentation in Brain MRI using a Fuzzy Approach with Class Center Priors. International Journal of Advanced Research in Computer and Communication Engineering, 5(3), 898-899.

[4]. Dharshini, R., & Hemanandhini, S. (2016). Brain tumor segmentation based on Self Organising Map and Discrete Wavelet Transform. In Computer Communication and Informatics (ICCCI), 2016 International Conference on (pp. 1-9). IEEE.

[5]. Gupta, T., Manocha, P., Gandhi, T. K., Gupta, R. K., & Panigrahi, B. K. (2017). Tumor Classification and Segmentation of MR Brain Images. arXiv preprint arXiv:1710.11309.

[6]. Janani, D., & Saravanakumar, S. (2017). Minutiae Extraction for Fingerprint and Palm Print Images using Convolutional Neural Networks. IOSR Journal of Electronics and Communication Engineering, 51-59.

[7]. Jose, A., Ravi, S., & Sambath, M. (2014). Brain tumor segmentation using k-means clustering and Fuzzy CMeans algorithms and its area calculation. International Journal of Innovative Research in Computer and Communication Engineering, 2(3), 3496-3501.

[8]. Nomikos, P. V., & Antoniadis, I. S. (2014). Introduction to Brain Tumors. In Imaging in Clinical Oncology (pp. 119- 121). Springer, Milano.

[9]. Rathi, V. P., & Palani, S. (2012). Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. arXiv preprint arXiv:1208. 2128.

[10]. Selkar, R. G., Thakare, M. N., & Chilke, B. J. (2014). Review on detection and segmentation of brain tumor using watershed and thresholding algorithm. IORD Journal of Science & Technology. 1(3), 321-324.

[11]. Vanitha, U., Deepak, P. P., Nageswaran, N. P., & Sathappan, R. (2015). Tumor Detection in Brain using Morphological Image Processing. Journal of Applied Science and Engineering Methodologies, 1(1), 131-136.

[12]. What are the mean and median filters? (2001). Retrieved from https://www.markschulze.net/java/ meanmed.html

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Research Paper

Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Sri Harsha* , S. Phani Kumar**

* PG Scholar, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

** Professor and Head, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

Harsha, A.S. and Kumar, S.P. (2017). Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems. i-manager’s Journal on Image Processing, 4(4), 22-30. https://doi.org/10.26634/jip.4.4.14161

Abstract

Accuracy is one of the major concerns for any face recognition algorithm. A good preprocessing technique can increase the accuracy of a face recognition system. For this purpose, the authors present a preprocessing method for face recognition, which is helpful for training under-sampled images. For this purpose, different preprocessing techniques are taken and those methods are used during training and the resultant images are added back to the training set. Each individual preprocessing technique is helpful to tackle different conditions like lighting, disguise, etc. The training will be done using a deep convolutional neural network and a Voting Classifier. Further the results of the existing preprocessing methods are compared with the proposed model on LFW dataset. They have also used a technique to increase the accuracy of the system.

References

[1]. Ahmadi, A. H., & Jamzad, M. (2011). Face recognition under varying illumination based on histogram equalized processed images. In Image and Signal Processing and Analysis (ISPA), 2011 ^7th International Symposium on (pp. 349-354). IEEE.

[2]. Ahonen, T., Hadid, A., & Pietikäinen, M. (2004, May). Face recognition with local binary patterns. In European Conference on Computer Vision (pp. 469-481). Springer, Berlin, Heidelberg.

[3]. Amos, B., Ludwiczuk, B., & Satyanarayanan, M. (2016). Openface: A general-purpose face recognition librar y with mobile applications. CMU School of Computer Science (pp. 1-18).

[4]. Chande, H. D., & Shah, Z. H. (2012). Illumination Invariant Face Recognition System. International Journal of Computer Science and Information Technologies, 3(3), 4010-4014.

[5]. Deng, W., Hu, J., & Guo, J. (2012). Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1864-1870.

[6]. Dharavath, K., Talukdar, F. A., & Laskar, R. H. (2014). Improving face recognition rate with image preprocessing. Indian Journal of Science and Technology, 7(8), 1170-1175.

[7]. Han, H., Shan, S., Chen, X., & Gao, W. (2013). A comparative study on illumination preprocessing in face recognition. Pattern Recognition, 46(6), 1691-1699.

[8]. Huang, G. B., Mattar, M., Berg, T., & Learned-Miller, E. (2008). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. In Workshop on Faces in 'Real-Life'Images: Detection, Alignment, and Recognition.

[9]. Jafri, R., & Arabnia, H. R. (2009). A survey of face recognition techniques. Journal of Information Processing Systems, 5(2), 41-68.

[10]. Karthikeyan, V., Vijayalakshmi, K., & Jeyakumar, P. (2012). An Efficient Method for Face Recognition System in various assorted conditions. Biometrics and Bioinformatics, 4(12), 543-547.

[11]. Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, 8(1), 98- 113.

[12]. Tensor Face. (2018). Retrieved from https://github. com/AKSHAYUBHAT/TensorFace/blob/master/openface/ models/dlib/shape_predictor_68_face_landmarks.dat

[13]. Wei, C. P., & Wang, Y. C. F. (2013, July). Learning auxiliary dictionaries for undersampled face recognition. In Multimedia and Expo (ICME), 2013 IEEE International Conference on (pp. 1-6). IEEE.

[14]. Wei, C. P., & Wang, Y. C. F. (2015). Undersampled face recognition via robust auxiliary dictionary learning. IEEE Transactions on Image Processing, 24(6), 1722-1734.

[15]. Zhang, H. (2011). Image Preprocessing Methods in Face Recognition. Symposium on Photonics and Optoelectronics (SOPO), (pp. 1-4.

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Research Paper

Histogram Based Face Recognition

C. H. Srinivasa Reddy* , G.Ravi Kumar, S. Chinni Krishna Reddy*

- Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India.

PG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

Reddy, S., Kumar, G.R. and Reddy, C.K. (2017). Histogram Based Face Recognition. i-manager’s Journal on Image Processing, 4(4), 31-35. https://doi.org/10.26634/jip.4.4.14162

Abstract

Human face conveys more information about identification, expression, and emotions of a person. In today's world every individual in the society wants to be more secure from unauthorized authentication. In order to provide more security, “Facial Recognition” has come into the picture and lead a most challenging role of detecting the face with more accurate results without any false identities. To increase the efficiency of the face recognition, histogram based facial recognition is chosen, where a face region is fragmented into a number of regions and histogram values are extracted and they are linked together into a single vector. This vector is compared for the similarities between the facial images and provides a most efficient outcome.

References

[1]. Ahmad, F., Najam, A., & Ahmed, Z. (2013). Image based Face Detection and Recognition. State of Art. International Journal of Computer Science, 9(6), 169- 180.

[2]. Arubas, E. (2013). Face Detection and Recognition (Theory and Practice). Retrieved from http://eyalarubas. com/face-detection-and-recognition.html

[3]. Eigen Faces Algorithm. (n.d.). opencv.com. Retrieved from https://docs.opencv.org/2.4/modules/ contrib/doc/facerec/facerec_tutorial.html# eigenfaces

[4]. Face Recognition. (n.d.). In Wikipedia. Retrieved from: https://en.wikipedia.org/wiki/Facial_recognition_ system.

[5]. Face recognition using OpenCV and Python. (n.d.). Retrieved from https://www.superdatascience.com/ opencv-face-recognition/

[6]. Fisher Faces Algorithm. (n.d.). opencv.com. Retrieved from https://docs.opencv.org/2.4/modules/ contrib/doc/ facerec/facerec_tutorial.html#fisherfaces

[7]. LBP Algorithm. (n.d.). opencv.com. Retrieved from https://docs.opencv.org/2.4/modules/contrib/doc/facer e c / f a c e r e c _ t u t o r i a l. h tml # l o c a l - b i n a r y- p a t t e r n s - histograms

[8]. Li, S. Z., & Jain, A. K. (2005). Handbook of Face Recognition. Springer-Verlag, Secaucus, NJ.

[9]. Local Binary Pattern. (n.d.). In Wikipedia. Retrieved from: https://en.wikipedia.org/wiki/Local_binary_patterns

[10]. Local Binary Pattern with OpenCV and Python. (n.d.). Pyimagesearch.com. Retrieved from https://www. pyimagesearch.com/2015/12/07/local-binary-patternswith- python-opencv/

[11]. Local Binary Patterns. (n.d). In Scholarpedia. Retrieved from http://www.scholarpedia.org/article/ Local_Binary_Patterns

[12]. Siswanto, A. R. S., Nugroho, A. S., & Galinium, M. (2014). Implementation of face recognition algorithm for biometrics based time attendance system. In ICT For Smart Society (ICISS), 2014 International Conference on (pp. 149-154).

i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 10 Issue 4

Most Read

Most Cited

Volume 4 Issue 4 October - December 2017

Qualitative Performance Metrics For Object Tracking Algorithms

Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla**, Abdul-Karim***

* Associate Professor, Aditya Institute of Technology and Management, Hyderabad, India. ** Professor, Institute of Aeronautical College of Engineering, Hyderabad, India. *** Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2017). Qualitative Performance Metrics for Object Tracking Algorithms. i-manager’s Journal on Image Processing, 4(4), 1-15. https://doi.org/10.26634/jip.4.4.14164

Abstract

References

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Classification of Tumor Using Gaussian Filter and Canny Edge Detection Technique

C.Rethinam*

Department of Computer Science and Engineering, Nehru Institute of Technology, Coimbatore, Tamil Nadu, India.

Rethinam, C. (2017). Classification of Tumor Using Gaussian Filter and Canny Edge Detection Technique. i-manager’s Journal on Image Processing, 4(4), 16-21. https://doi.org/10.26634/jip.4.4.14163

Abstract

References

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Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems

Sri Harsha* , S. Phani Kumar**

* PG Scholar, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India. ** Professor and Head, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

Harsha, A.S. and Kumar, S.P. (2017). Face Recognition: Dealing With Under-Sampled Data and Image Variation Problems. i-manager’s Journal on Image Processing, 4(4), 22-30. https://doi.org/10.26634/jip.4.4.14161

Abstract

References

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Histogram Based Face Recognition

C. H. Srinivasa Reddy* , G.Ravi Kumar**, S. Chinni Krishna Reddy***

*-** Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India. *** PG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

Reddy, S., Kumar, G.R. and Reddy, C.K. (2017). Histogram Based Face Recognition. i-manager’s Journal on Image Processing, 4(4), 31-35. https://doi.org/10.26634/jip.4.4.14162

Abstract

References

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Harihara Santosh Dadi* , Gopala Krishna Mohan Pillutla, Abdul-Karim*

* Associate Professor, Aditya Institute of Technology and Management, Hyderabad, India.

Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.

* Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.

* PG Scholar, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

** Professor and Head, Department of Computer Science and Engineering, GITAM School of Technology, Hyderabad, India.

C. H. Srinivasa Reddy* , G.Ravi Kumar, S. Chinni Krishna Reddy*

- Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India.

PG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.