i-manager Publications

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

Current Issue Vol. 11 Issue 2

Most Cited

Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

Volume 7 Issue 2 April - June 2020

Research Paper

Automatic Bi-Modal Human Image Recognition System for Personal Identification and Security

Adeboye Olatunbosun* , Bamigboye Oladayo Oladele**

*-** Department of Electrical and Electronic Engineering, University of Ibadan, Nigeria.

Olatunbosun, A., and Oladele, B. O. (2020). Automatic Bi-Modal Human Image Recognition System for Personal Identification and Security. i-manager's Journal on Image Processing, 7(2), 1-6. https://doi.org/10.26634/jip.7.2.17169

Abstract

Biometrics offers greater security and convenience than traditional person authentication methods, such as passwords and ID cards, which are more prone to fraudulent activity. The unimodal biometric system, as part of the biometric systems, has also seen rapid developments in their accuracy of recognition. However, there are drawbacks associated with each unimodal biometric trait, such as noisy sensed data, intra-class variations, lack of individuality, non-university and spoof attacks. These limitations of unimodal systems have set an upper limit on the performance of their recognition. As a result, these limitations lead the research community to come up with more robust and secure biometric systems that will be more difficult to fool than systems based on a single biometry. During the classification phase, the neural network (MLP) is explored for robust decision in the presence of slight variations and noise. The feasibility of all these algorithms has been successfully tested. Bimodal biometric systems have been shown to be more accurate and sound more robust than the unimodal system. The proposed bimodal biometric systems produce promising and better results compared to those of the unimodal biometric systems.

References

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[2]. Chhatbar, J. (2015). A study of iris recognition. International Journal of Research in Advance Engineering, 1(2), 21-24.

[3]. Choudhary, D., Tiwari, S., & Singh, A. K. (2012). A survey: Feature extraction methods for iris recognition. International Journal of Electronics Communication and Computer Technology, 2(6), 275-279.

[4]. Khaw, P. (2002). Iris recognition technology for improved authentication. SANS Institute, 5-8. https://www. sans.org/reading-room/whitepapers/authentication/p aper/132

[5]. Obaje, S. E., & Ibiyemi, T. S. (2010). Automatic Fingerprint and Toe print Recognition for Personal Identification and Forensic Application. (Doctoral dissertation). University of Ilorin, Nigeria.

[6]. Perlin, H. A., & Lopes, H. S. (2013). A Genetic Programming Approach for Image Segmentation. In Computational Intelligence in Image Processing (pp. 71- 90). Heidelberg, Berlin: Springer. https://doi.org/10.1007/ 978-3-642-30621-1_4

[7]. Roger, A. (2012). Process of Recognition of Human Iris: Fast Segmentation of the Iris. (Post graduate Thesis). Florida Institute of Technology, Florida, Melbourne.

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Research Paper

A Novel Approaches in Blind Multiple Water Marking for Color Video using DCT and Arnold Embedding Techniques

Himanshu Mokashe* , Abha Choubey, Siddhartha Choubey*

*-*** Department of Computer Science & Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India.

Mokashe, H., Choubey, A., and Choubey, S. (2020). A Novel Approaches in Blind Multiple Water Marking for Color Video using DCT and Arnold Embedding Techniques. i-manager's Journal on Image Processing, 7(2), 7-12. https://doi.org/10.26634/jip.7.2.17407

Abstract

Digital water marking is an essential technique to secure digital media files in the modern world for the area of data authentication, security and copyright protection. In this paper, we focused on the blind video multiple water marking systems. The non-blind water marking systems, the need for the original host media file in the watermark restoration operation, make insurance over the system support, increases memory capacity, and improves communications bandwidth. In this paper, an advanced blind video multiple-watermarking methods are proposed to solve this problem. This concept is built through image interlacing. In this technique, 2D discrete cosine transform (DCT) is used as a watermark embedding and extracting field, Arnold transform algorithm is used for watermark encryption and decryption method, which works on different varieties of media (gray image, color image, and video) are used as watermarks. The robustness of this technique's is examined by applying the different kinds of interventions such as geometric, noising, format-compression, and image-processing attacks. The simulation results show the effectiveness and outstanding achievement of the proposed technique in saving system resources, memory capacity, and communications bandwidth. In the submitted paper, robustness and quality are tested with video frame parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Normalized Correlation Coefficient (NCC). Further, the proposed paper has a comparison with related Watermarking schemes.

References

[1]. Anfinogenov, S., Korzhik, V. I., & Morales-Luna, G. (2012). A multiple robust digital watermarking system for still images. International Journal of Computer Science and Applications, 9(3), 37-46.

[2]. Hernandez, J. R., Amado, M., & Perez-Gonzalez, F. (2000). DCT-domain watermarking techniques for still images: Detector performance analysis and a new structure. IEEE Transactions on Image Processing, 9(1), 55- 68. https://doi.org/10.1109/83.817598

[3]. Jaiswal, R. J., & Patil, N. N. (2012). Multiple watermarking for text documents: A review. World Journal of Science and Technology, 2(3), 176-179.

[4]. Langelaar, G. C., & Lagendijk, R. L. (2001). Optimal differential energy watermarking of DCT encoded images and video. IEEE Transactions on Image Processing, 10(1), 148-158. https://doi.org/10.1109/83.892451

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[6]. Li, Y., & Wang, X. (2009, May). A novel multiple watermarking algorithm based on bandelet transform. In 2009, WRI Global Congress on Intelligent Systems (Vol. 4, pp. 238-242). IEEE. https://doi.org/10.1109/GCIS.2009.354

[7]. Lin, C. Y., Wu, M., Bloom, J. A., Cox, I. J., Miller, M. L., & Lui, Y. M. (2001). Rotation, scale, and translation resilient watermarking for images. IEEE Transactions on image processing, 10(5), 767-782. https://doi.org/10.1109/83.91 8569

[8]. Mascher-Kampfer, A., Stögner, H., & Uhl, A. (2006). Multiple re-watermarking scenarios. In Proceedings of the 13th International Conference on Systems, Signals, and Image Processing (IWSSIP 2006) (pp. 53-56). Budapest, Hungary.

[9]. Nasir, I., Weng, Y., & Jiang, J. (2008, April). Novel multiple spatial watermarking technique in color images. In Fifth International Conference on Information Technology: New Generations (ITNG 2008) (pp. 777-782). IEEE. https:// doi.org/10.1109/ITNG.2008.66

[10]. Piva, A., Bartolini, F., & Barni, M. (2002). Managing copyright in open networks. IEEE Internet Computing, 6(3), 18-26. https://doi.org/10.1109/MIC.2002.1003126

[11]. Rai, V., Rai, J., & Rana, M. (2011). Effective Multiple Image watermarking based on dither quantization. In Proceedings of the 5th National Conference (INDIACom) Computing for Nation Development.

[12]. Sheppard, N. P., Shafavi-Naini, R., & Ogunbona, P. (2001). On multiple watermarking. In Proceedings of the 2001 workshop on Multimedia and security: new challenges (MM & Sec '01), (pp. 3-6). Newyork, USA: Association for Computing Machinery Press. https://doi.org/10.1145/1232 454.1232458

[13]. Solachidis, V., & Pitas, I. (2001). Circularly symmetric watermark embedding in 2-D DFT domain. IEEE Transactions on Image Processing. 10(11), 1741–1753. https://doi.org/10.1109/83.967401

[14]. Surekha, B., Swamy, G., & Rao, K. S. (2010). A multiple watermarking technique for images based on visual cryptography. International Journal of Computer Applications, 1(11), 66-71.

[15]. Wheeler, G. E., Sheppard, N. P., & Safavi-Naini, R. (2004). Weighted segmented digital watermarking, In Third International Workshop on Digital Watermarking (IWDW 2004) (pp. 89-100). Heidelberg, Berlin: Springer-Verlag. https://doi.org/10.1007/978-3-540-31805-7_8

[16]. Wong, P. W., & Memon, N. (2001). Secret and public key image watermarking schemes for image authentication and ownership verification. IEEE Transactions on Image Processing, 10(10), 1593-1601. https://doi.org/10.1109/ 83.951543

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Research Paper

Deep Learning Technique for Transfer of Artistic Style to Images and Videos

V. V. Rama Prasad* , G. Vineela Ratna**

*-** Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh, India.

Prasad, V. V. R., and Ratna, G. V. (2020). Deep Learning Technique for Transfer of Artistic Style to Images and Videos. i-manager's Journal on Image Processing, 7(2), 13-21. https://doi.org/10.26634/jip.7.2.17555

Abstract

By composing a complex interplay between the content and style of an image, humanity has mastered the ability to create unique visual experience in fine art, particularly painting. Transfer of the artistic style is a problem in which image style is transformed into image content and generates image stylization. Style transformation can be applied over the entire video sequence by adding image style to video. We use perceptual loss functions to train feed-forward neural network and extract high-level features from the trained networks. We show the effects of image style transfer and video style transfer, through training feed forward network. Our network delivers faster results when compared with Gatys proposed optimization-based method. Resnet is added to the network as an improvisation to transformation network. Pruned Resnet is used, and it gives high computation speed, less size of memory and good performance.

References

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[4]. Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. Journal of Vision. Retrieved from https://arxiv.org/abs/1508.06576v2

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[7]. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440).

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Research Paper

Image Content Extraction using Advance Image Attribute Method

D. Saravanan*

Faculty of Operations & IT, ICFAI Business School (IBS), Hyderabad, Telangana, India.

Saravanan, D. (2020). Image Content Extraction using Advance Image Attribute Method. i-manager's Journal on Image Processing, 7(2), 22-27. https://doi.org/10.26634/jip.7.2.17655

Abstract

Extracting defined information from the huge datasets are challenging task for many researchers, especially input dataset like images are too complex, because image data consists of motion, time, text, audio and pixel difference. From this huge complex dataset, extracting the domain knowledge will take more time. This image extraction differs from traditional text mining, due to the nature of image datasets. Extracting information from image data requires domain knowledge and users have to concentrate more on the domain. Advancement of technology allows the user to create more and more image datasets with no guarantee of quality. This paper focuses on image mining performance with help of a hierarchical clustering technique. In the proposed techniques, video data are grouped into frames, then duplicate reaming frames are eliminated and stored in the database for further operations. Entire works is divided into client and server side operations. The proposed technique works well and the experimental results are also verified.

References

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[7]. Saravanan, D. (2016a). Design and implementation of feature matching procedure for video frame retrieval. International Journal of Control Theory and Applications, 9(7), 3283-3293.

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[10]. Saravanan, D. (2017). Image frame mining using indexing technique. In Data Engineering and Intelligent Computing: Proceedings of IC3T 2016, 542, 127-137.

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Review Paper

Fibers Image Analysis: A Review of Fibers Processing Techniques

Lalit Prakash Saxena*

Applied Research Section, Combo Consultancy, Sonebhadra, Uttar Pradesh, India.

Saxena, L. P. (2020). Fibers Image Analysis: A Review of Fibers Processing Techniques. i-manager's Journal on Image Processing, 7(2), 28-40. https://doi.org/10.26634/jip.7.2.17034

Abstract

Fibers image analysis is a new approach used in textile industries based on image processing techniques to enhance fiber imaging applications. These applications include fibers defects detection, fabrics wrinkle measurement, and fabric surface roughness evaluation that employ image processing techniques. This paper presents a review of fibers and fabrics image analysis applications using image processing techniques. These analysis applications outlay digital image processing techniques and employ specific procedures for specific purposes. Some of these procedures are morphological and mathematical operations, and soft-computing techniques finitely used for the processing of the fibers and fabrics images. This reduces manual labor in locating or detecting the defects in the fibers as well as in the processed fabrics. The advantage of using imaging applications is in monitoring the fibers from its preparation, through the knitting for fabric formation to final packaging of the fabrics. It is evident from this review that there is more to achieve in fibers analysis using image processing. As far as the images are concerned, a standard database of fabrics and fibers images is the need of the current textile industries to test and evaluate new innovative methods and imaging applications.

References

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