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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 8 Issue 4 October - December 2021

Research Paper

Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN)

J. H. Gan* , P. C. Teh**

*-** Department of Electronic Engineering, University Tunku Abdul Rahman, Malaysia.

Gan, J. H., and Teh, P. C. (2021). Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN). i-manager’s Journal on Image Processing, 8(4), 1-8. https://doi.org/10.26634/jip.8.4.18394

Abstract

Plant diseases are one of the main threats to food security. Various methods for detecting plant diseases have been developed as a means to ensure food security and reduce food waste through early detection. Through these technological advancements, precision farming has been developed, and the application of artificial intelligence is steadily gaining popularity in this technology industry as a means of solving this problem. In this paper, transfer learning of pre-trained deep learning object classification and detection models on 2 plant species, eighteen classes in total, was performed and trained. Various approaches were applied and a classification model with a maximum data set size of 200 per class and a batch size of 32 performed best with 97.9%, 64.5%, 70.4%, and 66.1% for precision, recall, and F1- indicator, respectively, and the object detection model score COCO mAP of 91.48%. Finally, these models have been applied so that the user can insert an image and let the models predict the health of the plant. A graphical interface has also been created to allow the user to select images to be predicted by the models.

References

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[2]. Ali, W. (2019). Plant Classification in Healthy and Disease. Retrieved from https://github.com/waqas-ali/tf_ plant_disease_classification

[3]. Alon, A., & Dioses, J. L. (2020). Machine vision recognition system for iceberg lettuce health condition on raspberry pi 4b: A mobile net ssd v2 inference approach. International Journal of Emerging Trends in Engineering Research, 8(4), 1073-1078. https://doi.org/10.30534/ijeter/ 2020/20842020

[4]. Atila, Ü., Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, 101182. https://doi.org/ 10.1016/j.ecoinf.2020.101182

[5]. Calderone, L. (2019). What is Machine Vision?. Robotics Tomorrow. Retrieved from https://www.robotics tomorrow.com/article/2019/12/what-is-machine-vision/ 14548

[6]. Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y.A. (2020a). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173. https://doi.org/10.1016/j.compag.2020. 105393

[7]. Chen, J., Zhang, D., Nanehkaran, Y. A., & Li, D. (2020b). Detection of rice plant diseases based on deep transfer learning. Journal of the Science of Food and Agriculture, 100(7), 3246-3256. https://doi.org/10.1002/jsfa.10365

[8]. Durmuş, H., Güneş, E. O., & Kırcı, M. (2017, August). Disease detection on the leaves of the tomato plants by using deep learning. In 2017, 6^th International Conference on Agro-Geoinformatics (pp. 1-5). IEEE. https://doi.org/ 10.1109/Agro-Geoinformatics.2017.8047016

[9]. Hassim, S. A., & Chuah, J. H. (2020). Lettuce classification using convolutional neural network. Food Research, 4(6), 118-123. https://doi.org/10.26656/fr.2017. 4(S6).029

[10]. Joshi, R. C., Kaushik, M., Dutta, M. K., Srivastava, A., & Choudhary, N. (2021). VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant. Ecological Informatics, 61, 101197. https:// doi.org/10.1016/j.ecoinf.2020.101197

[11]. Kouhalvandi, L., Gunes, E. O., & Ozoguz, S. (2019, July). Algorithms for speeding-up the deep neural networks for detecting plant disease. In 2019, 8^th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-4). IEEE. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820541

[12]. Kulkarni, O. (2018, August). Crop disease detection using deep learning. In 2018, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (pp. 1-4). IEEE. https://doi.org/ 10.1109/ICCUBEA.2018.8697390

[13]. Lauguico, S., Concepcion, R., Tobias, R. R., Bandala, A., Vicerra, R. R., & Dadios, E. (2020, November). Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks. In 2020, IEEE REGION 10 CONFERENCE (TENCON) (pp. 767-772). IEEE. https://doi.org/10.1109/TEN CON50793.2020.9293866

[14]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. https://doi.org/10.1038/ nature14539

[15]. Li, K., Lin, J., Liu, J., Zhao, Y., Dou, S., Zhang, Y., & Liu, X. (2019, November). Classification of different degrees of ginkgo leaf disease based on deep learning. In 2019, 6^th International Conference on Systems and Informatics (ICSAI) (pp. 1228-1232). IEEE. https://doi.org/10.1109/ICSAI 48974.2019.9010110

[16]. Lumini, A., & Nanni, L. (2019). Deep learning and transfer learning features for plankton classification. Ecological Informatics, 51, 33-43. https://doi.org/10.1016/ j.ecoinf.2019.02.007

[17]. Marr, B. (2019). What is Machine Vision And How Is It Used In Business Today?. Retrieved from https://www.forbes. com/sites/bernardmarr/2019/10/11/what-is-machinevision- and-how-is-it-used-in-business-today/?sh=2414057 69396

[18]. Noon, S. K., Amjad, M., Qureshi, M. A., & Mannan, A. (2020). Use of deep learning techniques for identification of plant leaf stresses: A review. Sustainable Computing: Informatics and Systems, 100443. https://doi.org/10.1016/ j.suscom.2020.100443

[19]. Razdan, V., & Bateman, R. (2015, June). Investigation into the use of smartphone as a machine vision device for engineering metrology and flaw detection, with focus on drilling. In Automated Visual Inspection and Machine Vision (Vol. 9530, p. 95300C). International Society for Optics and Photonics. https://doi.org/10.1117/12.2183081

[20]. Sagar, A., & Dheeba, J. (2020). On using transfer learning for plant disease detection. bioRxiv. https://doi. org/10.1101/2020.05.22.110957

[21]. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003

[22]. Sharma, P., Berwal, Y. P. S., & Ghai, W. (2018, November). KrishiMitr (Farmer's Friend): Using Machine Learning to Identify Diseases in Plants. In 2018, IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) (pp. 29-34). IEEE. https://doi. org/10.1109/IOTAIS.2018.8600898

[23]. Stafford, J. V. (2000). Implementing precision agriculture in the 21^st century. Journal of agricultural engineering research, 76(3), 267-275. https://doi.org/10.1 006/jaer.2000.0577

[24]. Valdoria, J. C., Caballeo, A. R., Fernandez, B. I. D., & Condino, J. M. M. (2019, October). iDahon: An android based terrestrial plant disease detection mobile application through digital image processing using deep learning neural network algorithm. In 2019, 4^th International Conference on Information Technology (InCIT) (pp. 94-98). IEEE. https://doi.org/10.1109/INCIT.2019.8912053

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Research Paper

Standard Particle Swarm Optimization Algorithm for Image Enhancement

Mani Kumar Jogi* , Y. Srinivasa Rao**

*-** Department of Instrument Technology, Andhra University, Andhra Pradesh, India.

Jogi, M. K., and Rao, Y. S. (2021). Standard Particle Swarm Optimization Algorithm for Image Enhancement. i-manager’s Journal on Image Processing, 8(4), 9-16. https://doi.org/10.26634/jip.8.4.18441

Abstract

Image enhancement plays a crucial role in an image processing system. Improving image quality by maximizing the information content of a given input image is the primary goal of image enhancement. Many of the methods proposed earlier did not achieve a good improvement in quality. Optimization algorithms for solving the image enhancement problem are proposed. The quality of the input image is improved by choosing the optimal parameters based on the objective function formulated for the optimization process. The design of the objective function plays a crucial role in the optimization problem. This paper presents an efficient, objective approach to gray level image enhancement using the Standard Particle Swarm Optimization (SPSO) algorithm, which is an improvement on the simplest particle swarm optimization algorithm. The proposed algorithm has been tested on standard gray-level test images such as cat, stone, and eye. The proposed algorithm is evaluated based on two scenarios for improving the gray-level image and is successful in finding the optimal parameters for improving image quality.

References

[1]. Chen, H., & Tian, J. (2011, August). Using particle swarm optimization algorithm for image enhancement. In 2011, International Conference on Uncertainty Reasoning and Knowledge Engineering (Vol. 1, pp. 154-157). IEEE. https:// doi.org/10.1109/URKE.2011.6007823

[2]. Coelho, L. S., Sauer, J. G., & Rudek, M. (2009). Differential evolution optimization combined with chaotic sequences for image contrast enhancement. Chaos, Solitons & Fractals, 42(1), 522-529. https://doi.org/10.1016/ j.chaos.2009.01.012

[3]. Gorai, A., & Ghosh, A. (2009, December). Gray-level image enhancement by particle swarm optimization. In 2009, World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 72-77). IEEE. https://doi.org/10.1 109/NABIC.2009.5393603

[4]. Hanmadlu, M., Arora, S., Gupta, G., & Singh, L. (2013, August). A novel optimal fuzzy color image enhancement using particle swarm optimization. In 2013, Sixth International Conference on Contemporary Computing (IC3) (pp. 41-46). IEEE. https://doi.org/10.1109/IC3.2013.66 12237

[5]. Hashemi, S., Kiani, S., Noroozi, N., & Moghaddam, M. E. (2010). An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters, 31(13), 1816-1824. https://doi.org/10.1016/j.patrec.2009. 12.006

[6]. Lei, X., Hu, Q., Kong, X., & Xiong, T. (2014). Image enhancement using hybrid intelligent optimization. Optics & Optoelec- tronic Technology, 341–344.

[7]. Merugumalla, M. K., &Navuri, P. K. (2016). Sensorless control of BLDC motor using bio-inspired optimization algorithm and classical methods of tuning PID controller. i-manager's Journal on Instrumentation & Control Engineering, 5(1), 16-23. https://doi.org/10.26634/jic.5.1. 10349

[8]. Merugumalla, M. K., & Navuri, P. K. (2018). Population Algorithms for optimal control of BLDC motor drive. HELIX, 8(3), 3350-3355.

[9]. Merugumalla, M. K., & Navuri, P. K. (2019). Inertia weight strategies in PSO for BLDC motor drive control. In Microelectronics, Electromagnetics and Telecommunications (pp. 475-484). Springer, Singapore. https://doi.org/10.100 7/978-981-13-1906-8_49

[10]. Munteanu, C., & Rosa, A. (2004). Gray-scale image enhancement as an automatic process driven by evolution. IEEE Transactions on Systems, Man, and Cybernetics, Part B (cybernetics), 34(2), 1292-1298. https:// doi.org/10.1109/TSMCB.2003.818533

[11]. Pal, S. K., Bhandari, D., & Kundu, M. K. (1994). Genetic algorithms for optimal image enhancement. Pattern Recognition Letters, 15(3), 261-271. https://doi.org/10.10 16/0167-8655(94)90058-2

[12]. Saitoh, F. (1999, October). Image contrast enhancement using genetic algorithm. In IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028) (Vol. 4, pp. 899-904). IEEE. https://doi.org/10. 1109/ICSMC.1999.812529

[13]. Sarangi, P. P., Mishra, B. S. P., Majhi, B., & Dehuri, S. (2014, February). Gray-level image enhancement using differential evolution optimization algorithm. In 2014, International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 95-100). IEEE. https://doi. org/10.1109/SPIN.2014.6776929

[14]. Zimmerman, J. B., Pizer, S. M., Staab, E. V., Perry, J. R., McCartney, W., & Brenton, B. C. (1988). An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Transactions on Medical Imaging, 7(4), 304-312. https://doi.org/10.1109/42.14513

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Research Paper

Traffic Rule Violation Detection Systems

Praneeti Lohiya* , Himani Uike, Akshay Dubey*, Snehlata Mandal****

*-**** Department of Computer Science and Engineering, Shri Shankaracharya Engineering College, Bhilai, Chhattisgarh, India.

Lohiya, P., Uike, H., Dubey, A., and Mandal, S. (2021). Traffic Rule Violation Detection Systems. i-manager’s Journal on Image Processing, 8(4), 17-20. https://doi.org/10.26634/jip.8.4.18510

Abstract

The globe is becoming more and more urbanized. As a result, the number of automobiles on the roadways of the city has increased significantly, which has led to an increase in traffic violations. This causes serious damage to the environment and property and it increases the number of accidents that can threaten the general public. To cope with an alarming situation and prevent such cases, it is necessary to identify violations of traffic rules, which have unthinkable consequences and requires the implementation of the system.

References

[1]. Bedruz, R. A., Uy, A. C. P., Quiros, A. R., Billones, R. K., Sybingco, E., Bandala, A., & Dadios, E. P. (2019). A robotic model approach of an automated traffic violation detection system with apprehension. In 2018, IEEE 10^th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM) (pp. 1-4). IEEE. https://doi.org/10.1109/HNICEM.2018.8666321

[2]. Ha, P. S., & Shakeri, M. (2016, April). License Plate Automatic Recognition based on edge detection. In 2016, Artificial Intelligence and Robotics (IRANOPEN) (pp. 170-174). IEEE. https://doi.org/10.1109/RIOS.2016.7529509

[3]. Mohamed, S. A. E. (2019, February). Automatic traffic violation recording and reporting system to limit traffic accidents: based on Vehicular Ad-Hoc Networks (VANET). In 2019, International Conference on Innovative Trends in Computer Engineering (ITCE) (pp. 254-259). IEEE. https:// doi.org/10.1109/ITCE.2019.8646449

[4]. Narkhede, A., Nikam, V., Soni, A., Sathe, A., & Chiddarwar, G. G. (2017). Automatic traffic rule violation detection and number plate recognition. IJSTEInternational Journal of Science Technology & Engineering, 3(9), 559-563.

[5]. Reddy, P. S., Nishwa, T., Reddy, R. S. K., Sadviq, C., & Rithvik, K. (2021, July). Traffic rules violation detection using machine learning techniques. In 2021, 6^th International Conference on Communication and Electronics Systems (ICCES) (pp. 1264-1268). IEEE. https://doi.org/10.1109/ICC ES51350.2021.9488998.

[6]. Tripathi, S., Shetty, U., Hasnain, A., & Hallikar, R. (2019, April). Cloud based intelligent traffic system to implement traffic rules violation detection and accident detection units. In 2019, 3^rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 347-351). IEEE. https://doi.org/10.1109/ICOEI.2019.8862694

[7]. Wang, J., Chen, Z., Li, P., Sheng, B., & Chen, R. (2019, May). Real-time non-motor vehicle violation detection in traffic scenes. In 2019, IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (pp. 724-728). IEEE. https://doi.org/10.1109/ICPHYS.2019.8780253

[8]. Wonghabut, P., Kumphong, J., Ung-arunyawee, R., Leelapatra, W., & Satiennam, T. (2018, July). Traffic light color identification for automatic traffic light violation detection system. In 2018, International Conference on Engineering, Applied Sciences, and Technology (ICEAST) (pp. 1-4). IEEE. https://doi.org/10.1109/ICEAST.2018.8434400

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Review Paper

Image Encryption Method for Secure Image Transmission

Neelima Thakur* , Siddhartha Choubey , Abha Choubey*

*-*** Department of Computer Science and Engineering, Shri Shankaracharya Institute of Engineering and Technology, Chhattisgarh, India.

Thakur, N., Choubey, S., and Choubey, A. (2021). Image Encryption Method for Secure Image Transmission. i-manager’s Journal on Image Processing, 8(4), 21-25. https://doi.org/10.26634/jip.8.4.18489

Abstract

This paper is mainly related to the secret writing of various images and secret writing methods. The cryptography of the image is extremely necessary for information security purposes. Security is the most difficult aspect of the network and network applications. Online and networked applications are growing rapidly, increasing the importance and therefore the value of information transmitted over the Internet or other media. The secret letter may be related to improving the security of the image by encrypting the pixels. The encryption itself protects privacy. This application is used in many areas, including network communications, medical imaging, and military communications. Due to some properties of images, such as high information redundancy and volume of information, secret writing of images is different from secret writing of texts. Secret writing is to convert information into a type of encryption code that cannot be easily understood by the user unauthorized persons.

References

[1]. Chang, C. C., Hwang, M. S., & Chen, T. S. (2001). A new encryption algorithm for image cryptosystems. Journal of Systems and Software, 58(2), 83-91. https://doi.org/10.101 6/S0164-1212(01)00029-2

[2]. Yen, J. C., & Guo, J. I. (2000). A new chaotic mirror-like image encryption algorithm and its VLSI architecture. Pattern Recognition and Image Analysis (Advances in Mathematical Theory and Applications), 10(2), 236-247.

[3]. Indrakanti, S. P., & Avadhani, P. S. (2011). Permutationbased image encryption technique. International Journal of Computer Applications, 28(8), 45-47.

[4]. Kester, Q. A. (2013). A cryptographic Image Encryption technique based on the RGB Pixel shuffling. International Journal of Advanced Research in Computer Engineering & Technology, 2(2), 848-854.

[5]. Megashree, B. S., & Sujatha, B. R. (2018). AES based image encryption and decryption using Matlab, International Journal of Engineering Research & Technology (IJERT), 6(13), 1-3.

[6]. Nag, A., Singh, J. P., Khan, S., Ghosh, S., Biswas, S., & Sarkar, D. S. P. (2011). Image encryption using affine transform and XOR operation. In 2011, International Conference on Signal Processing, Communication, Computing and Networking Technologies (pp. 309-312). IEEE.

[7]. Shin, C., Seo, D., Chol, K., Lee, H., & Kim, S. (2003). Multilevel Image encoding by Binary part XOR Operations. IEEE.

[8]. Sinha, A., & Singh, K. (2003). A technique for image encryption using digital signature. Optics Communications, 218(4-6), 229-234. https://doi.org/10.1016/S0030-4018 (03)01261-6

[9]. Sun, Q., Yan, W., Huang, J., & Ma, W. (2012, April). Image encryption based on bit-plane decomposition and random scrambling. In 2012, 2^nd International Conference on Consumer Electronics, Communications and Networks (CECNet) (pp. 2630-2633). IEEE. https://doi.org/ 10.1109/CECNet.2012.6201673

[10]. Sun, Y., & Wang, G. (2011, October). An image encryption scheme based on the modified logistic map. In 2011, Fourth International Workshop on Chaos-Fractals Theories and Applications (pp. 179-182). IEEE. https://doi.org/10.1109/IWCFTA.2011.56

[11]. Younes, M. A. B., & Jantan, A. (2008). An image encryption approach using a combination of permutation techniques followed by encryption. IJCSNS International Journal of Computer Science and Network Security, 8(4), 191-197.

[12]. Bani, M. A., & Jantan, A. (2008). Image encryption using block-based transformation algorithm. IJCSNS International Journal of Computer Science and Network Security, 8(4), 191-197.

[13]. Zhou, Y., & Agaian, S. (2011, June). Image encryption using the image steganography concept and PLIP model. In Proceedings 2011 International Conference on System Science and Engineering (pp. 699-703). IEEE. https://doi.org/10.1109/ICSSE.2011.5961993

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Review Paper

Human Recognition System using Behavioral and Physical Biometrics

Ragini Pathari* , Snehlata Barde**

*-** Department of Computer Science, Shantiniketan College, Raipur, Chhatisgarh, India.

Pathari, R., and Barde, S. (2021). Human Recognition System using Behavioral and Physical Biometrics. i-manager’s Journal on Image Processing, 8(4), 26-31. https://doi.org/10.26634/jip.8.4.18466

Abstract

The use of biometric systems has grown rapidly in recent decades. This paper mainly deals with multi-modal biometric systems, fake identification, etc., and sheds light on how to achieve privacy and security in a biometric system. Comprehensive and critical studies of various detection algorithms are presented. The first step in detection is to isolate the face recognition system from the background. The paper also presents several applications in different fields, viz. basic content imaging, image acquisition, and video encoding. The article focused on various multimodal systems using various modalities and algorithms for human recognition through verification, identification, and authentication.

References

[1]. Barde, S. (2017). Multimodal Biometrics: Most Appropriate for Person Identification. i-manager's Journal on Pattern Recognition, 4(3), 1-8. https://doi.org/10.2663 4/jpr.4.3.13881

[2]. Barde, S., & Agrawal, A. (2019). Classification of Biometrics and Implementation Strategies. In Advances in Biometrics (pp. 307-332). Springer, Cham. https://doi.org/ 10.1007/978-3-030-30436-2_15

[3]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014a). Multimodal Biometrics using Face, Ear and Iris Modalities. International Journal of Computer Applications (IJCA), Recent Advances in Information Technology NCRAIT, 9-15.

[4]. Barde, S., Zadgaonkar, A. S., & Sinha, G. R. (2014b). PCA based multimodal biometrics using ear and face modalities. International Journal of Information Technology and Computer Science, 6(5), 43-49. https://doi.org/10.5 815/ijitcs.2014.05.06

[5]. Chaudhary, S., & Nath, R. (2015). A new multimodal biometric recognition system integrating iris, face and voice. International Journal of Advanced Research in Computer Science and Software Engineering, 5(4), 145- 150.

[6]. Gopal, N., & Selvakumar, R. K. (2016). Multimodal biometric identification system-An overview. International Journal of Engineering Trends and Technology (IJETT)–Vol. 33, 351-355.

[7]. Hadid, A., Pietikainen, M., & Ahonen, T. (2004, June). A discriminative feature space for detecting and recognizing faces. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 2, pp. II-II). IEEE. https://doi.org/10.1 109/CVPR.2004.1315246

[8]. Impedovo, D., & Pirlo, G. (2008). Automatic signature verification: The state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(5), 609-635. https://doi.org/10.1109/TSMCC. 2008.923866

[9]. Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1), 4-20. https://doi.org/10.1109/TCSVT.2003.818349

[10]. Kazi, M. M., Rode, Y. S., Dabhade, S. B., Al-Dawla, N. N. H., Mane, A. V., Manza, R. R., & Kake, K. V. (2012). Multimodal biometric system using face and signature: a score level fusion approach. Advances in Computational Research, 4(1), 99-103.

[11]. Kumar, G. V., Raju, G. V., & Valli,A. N. M. V. S. (2013). Multimodal Biometrics Based System for Efficient Human Recognition. International Journal of Advanced Trends in Computer Science and Engineering, 2(6), 26-31.

[12]. Kumar, K., & Farik, M. (2016). A review of multimodal biometric authentication systems. International Journal Of Scientific & Technology Research, 5(12), 5-9.

[13]. Ong, M. G. K., Connie, T., Jin, A. T. B., & Ling, D. N. C. (2003, November). A single-sensor hand geometry and palmprint verification system. In Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, WBMA (pp. 100-106). https://doi.org/10.11 45/982507.982526

[14]. Rattani, A., & Tistarelli, M. (2009, June). Robust multimodal and multi-unit feature level fusion of face and iris biometrics. In International Conference on biometrics (pp. 960-969). Springer, Berlin, Heidelberg. https://doi.org/10.1 007/978-3-642-01793-3_97

[15]. Selvi, P. T., & Radha, N. (2010). Multimodal biometrics based authentication against dictionary attacks. International Journal on Computer Science and Engineering, 2(8), 2652-2658.

[16]. Siddiqui, A. M., Telgad, R., & Deshmukh, P. D. (2014). Multimodal biometric systems: Study to improve accuracy and performance. International Journal of Current Engineering and Technology, 4(1), 165-171.

[17]. Sim, T., Zhang, S., Janakiraman, R., & Kumar, S. (2007). Continuous verification using multimodal biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 687-700. https://doi.org/10. 1109/TPAMI.2007.1010

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[20]. Yadav, S. S., Gothwal, J. K., & Singh, R. (2011). Multimodal biometric authentication system: Challenges and Solutions. Global Journal of Computer Science and Technology, 11(6).

[21]. Zhang, D., Guo, Z., Lu, G., Zhang, L., & Zuo, W. (2009). An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement, 59(2), 480-490. https://doi.org/10.1109/TIM.2009.2028772

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

Current Issue Vol. 11 Issue 2

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Volume 8 Issue 4 October - December 2021

Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN)

J. H. Gan* , P. C. Teh**

*-** Department of Electronic Engineering, University Tunku Abdul Rahman, Malaysia.

Gan, J. H., and Teh, P. C. (2021). Detection of Plant Diseases using Deep Learning Based on Convolution Neural Network (CNN). i-manager’s Journal on Image Processing, 8(4), 1-8. https://doi.org/10.26634/jip.8.4.18394

Abstract

References

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Standard Particle Swarm Optimization Algorithm for Image Enhancement

Mani Kumar Jogi* , Y. Srinivasa Rao**

*-** Department of Instrument Technology, Andhra University, Andhra Pradesh, India.

Jogi, M. K., and Rao, Y. S. (2021). Standard Particle Swarm Optimization Algorithm for Image Enhancement. i-manager’s Journal on Image Processing, 8(4), 9-16. https://doi.org/10.26634/jip.8.4.18441

Abstract

References

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Traffic Rule Violation Detection Systems

Praneeti Lohiya* , Himani Uike**, Akshay Dubey***, Snehlata Mandal****

*-**** Department of Computer Science and Engineering, Shri Shankaracharya Engineering College, Bhilai, Chhattisgarh, India.

Lohiya, P., Uike, H., Dubey, A., and Mandal, S. (2021). Traffic Rule Violation Detection Systems. i-manager’s Journal on Image Processing, 8(4), 17-20. https://doi.org/10.26634/jip.8.4.18510

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Image Encryption Method for Secure Image Transmission

Neelima Thakur* , Siddhartha Choubey **, Abha Choubey***

*-*** Department of Computer Science and Engineering, Shri Shankaracharya Institute of Engineering and Technology, Chhattisgarh, India.

Thakur, N., Choubey, S., and Choubey, A. (2021). Image Encryption Method for Secure Image Transmission. i-manager’s Journal on Image Processing, 8(4), 21-25. https://doi.org/10.26634/jip.8.4.18489

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Human Recognition System using Behavioral and Physical Biometrics

Ragini Pathari* , Snehlata Barde**

*-** Department of Computer Science, Shantiniketan College, Raipur, Chhatisgarh, India.

Pathari, R., and Barde, S. (2021). Human Recognition System using Behavioral and Physical Biometrics. i-manager’s Journal on Image Processing, 8(4), 26-31. https://doi.org/10.26634/jip.8.4.18466

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Praneeti Lohiya* , Himani Uike, Akshay Dubey*, Snehlata Mandal****

Neelima Thakur* , Siddhartha Choubey , Abha Choubey*