i-manager Publications

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

Current Issue Vol. 11 Issue 3

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 3 July - September 2020

Research Paper

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

bamgboye* , Adeboye Olatunbosun **

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

Oladele, B. O., and Olatunbosun, A. (2020). Security and Personal Identification Automatic Bi-Modal Human Image Recognition System. i-manager's Journal on Image Processing, 7(3), 1-6. https://doi.org/10.26634/jip.7.3.17211

Abstract

Biometrics are safer and more convenient than conventional authentication methods, including vulnerable passwords and ID cards. A unimodal biometric system has also been rapidly evolving in its precision, as part of biometric systems. However, every unimodal biometric feature has a disadvantage, such as loud sensed information, variations intraclasses, absence of individuality, non-universality, and spoof attack. These constraints have established the maximum efficiency of unimodal systems. This means that the research community is developing solid and guaranteed biometric systems that are harder to delude than systems based on a single biometrics. 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. It has been shown that biomodal biometric systems are more accurate and noise robust than unimodal system. Compared with unimodal structures, the suggested bimodal biometric technologies generate promising and improved outcomes.

References

[1]. Abiyev, R. H., & Kilic, K. I. (2011). Robust Feature Extraction and Iris Recognition for Biometric Personal Identification. In Riaz, Z. (Ed.), Biometric Systems, Design and Applications, (pp. 149-168), IntechOpen. https://doi. org/10.5772/18374

[2]. Arora, K. (2012). Real time application of face recognition concept. International Journal of Soft Computing and Engineering (IJSCE), 2(5), 191-196.

[3]. Chavez, R. F. L., Iano, Y., & Sablon, V. B. (2006). Process of recognition of human iris: Fast segmentation of iris. Retrieved from https://www.decom.fee.unicamp.br/~rlarico/iris/ localizationiris.pdf

[4]. Chhatbar, J. (2015). A study of iris recognition. International Journal of Research in Advance Engineering, 1(2), 21-24. https://doi.org/10.26472/ijrae.v1i2.24

[5]. 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.

[6]. Khaw, P. (2002). Iris recognition technology for improved authentication. SANS Institute. Retrieved from https://www. sans.org/reading-room/whitepapers/authentication/irisrecognition- technology-improved-authentication-132

[7]. Obaje, S. E. & Ibiyemi, T. S. (2010). Automatic fingerprint and toe print recognition for personal identification and forensic application (Doctoral Thesis). University of Ilorin, Nigeria.

[8]. 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

[9]. Sharma, A., Chaturvedi, R., Dwivedi, U. K., Kumar, S., & Reddy, S. (2018). Firefly algorithm based effective gray scale image segmentation using multilevel thresholding and Entropy function. International Journal of Pure and Applied Mathematics, 118(5), 437-443.

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Research Paper

Image Data Recovery using User's Judgement Choice Technique

D. Saravanan*

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

Saravanan, D. (2020). Image Data Recovery using User's Judgement Choice Technique. i-manager's Journal on Image Processing, 7(3), 7-13. https://doi.org/10.26634/jip.7.3.17656

Abstract

Target search in content based image retrieval systems refer to finding a needed image based on the users input query such as a particular registered logo or a specific historical photograph. Current method based on the user input system extract relevant images, and based on this extraction user need to specify the search related to the query. This process takes more time and it never brings more picture as per the query submitted by the user input. The proposed technique retrives more number of images and at the same time image directory is also created so that user judgment process has been done in an efficient manner. The outcomes of the investigation confirmed that this approach produces more result than the current method.

References

[1]. Bartolini, I., Ciaccia, P., & Waas, F. (2001, September). Feedback Bypass: A new approach to interactive similarity th query processing. In Proceedings of the 27^th Very Large Data Base (VLDB) (pp. 201-210).

[2]. Brunelli, R., & Mich, O. (2000). Image retrieval by examples. IEEE Transactions on Multimedia, 2(3), 164-171. https://doi.org/10.1109/6046.865481

[3]. Chen, L., Özsu, M. T., & Oria, V. (2004). MINDEX: An efficient index structure for salient-object-based queries in video databases. Multimedia Systems, 10(1), 56-71. https://doi.org/10.1007/s00530-004-0137-4

[4]. Fayyad, U. M., Djorgovski, S. G., & Weir, N. (1996). Automating the analysis and cataloging of sky surveys. In Advances in Knowledge Discovery and Data Mining (pp. 471-493).

[5]. Liu, Y., Zhang, D., Lu, G., & Ma, W. Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1), 262-282. https://doi.org/10. 1016/j.patcog.2006.04.045

[6]. Ma, W. Y., & Manjunath, B. S. (1997). Netra: A toolbox for navigating large image databases. In Proceedings of International Conference on Image Processing (pp. 568- 571). https://doi.org/10.1109/ICIP.1997.647976

[7]. Pentland, A., Picard, R. W., & Sclaroff, S. (1996). Photobook: Content-based manipulation of image databases. International Journal of Computer Vision, 18(3), 233-254. https://doi.org/10.1007/BF00123143

[8]. Saravanan, D. (2017a). Video data image retrieval using–BRICH. World Journal of Engineering, 14(4), 318-323. https://doi.org/10.1108/WJE-09-2016-0093

[9]. Saravanan, D. (2017b). Clustering of video in formations using BRICH Methodology. Pakistan Journal of Biotechnology, 14(2), 377-380.

[10]. Saravanan, D. (2017c). Effective video data retrieval using image key frame selection. In Proceedings of the First International Conference on Computational Intelligence and Informatics (pp. 145-155). Singapore: Springer. https:// doi.org/10.1007/978-981-10-2471-9_15

[11]. Saravanan, D. (2018a). Image frame mining using indexing technique. In Data Engineering and Intelligent Computing (pp. 127-137). Singapore: Springer. https://doi. org/10.1007/978-981-10-3223-3_12

[12]. Saravanan, D. (2018b). Effective video content retrieval using image attributes. EAI Endorsed Transactions on Energy Web and Information Technologies, 5(18), 1-5. https://doi.org/10.4108/eai.12-6-2018.154818

[13]. Saravanan, D. (2018c). Efficient video indexing and retrieval using hierarchical clustering technique. In Proceedings of the Second International Conference on Computational Intelligence and Informatics (pp. 1-8). Singapore: Springer. https://doi.org/10.1007/978-981-10-8 228-3_1

[14]. Saravanan, D. (2019a). Image substance extraction using data mining clustering method. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(2), 2735-2739.

[15]. Saravanan, D. (2019b). Information retrieval using image attribute possessions. In Wang J., Reddy G., Prasad V., Reddy V. (Eds.) Soft Computing and Signal Processing (pp 759-767). Advances in Intelligent Systems and Computing, Vol 898. Springer, Singapore. https://doi.org/ 10.1007/978-981-13-3393-4_77

[16]. Saravanan, D., & Joseph, D. (2019). Image data extraction using image similarities. In Panda G., Satapathy S., Biswal B., Bansal R. (Eds.) Microelectronics, Electromagnetics and Telecommunications (pp. 409- 420). Lecture Notes in Electrical Engineering, Vol 521. Springer, Singapore. https://doi.org/10.1007/978-981-13- 1906-8_43

[17]. Stolorz, P., Nakarmura, H., Mesrobian, E., Muntz, R. R., Shek, E. C., Santos, J. R., ... & Farrar, J. D. (1995). Fast spatio-temporal data mining of large gelphysical database. KDD-95 Proceedings (pp. 300-305).

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Research Paper

Image Fusion Method Based on Regional Feature and Improved Bi-Dimensional Intrinsic Mode Function

Radhika Vadhi * , Guru Vishnu Kesari **

* Department of Electronics and Communication Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, Andhra Pradesh, India.

** Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.

Vadhi, R., and Kesari, G. V. (2020). Image Fusion Method Based on Regional Feature and Improved Bi-Dimensional Intrinsic Mode Function. i-manager's Journal on Image Processing, 7(3), 14-24. https://doi.org/10.26634/jip.7.3.17674

Abstract

The disintegration of different source images utilizing Bi-Dimensional Empirical Mode Decomposition (BEMD) frequently delivers crisscrossed Bi-dimensional natural mode work, either by their number, or their recurrence, making image fusion troublesome. The image fusion measure is characterized as an interaction with all the significant data from various images, and their incorporation into single image, normally a solitary one. This single image is more useful and precise than any single source image, and it comprises all the fundamental data. This strategy is dependent on improved Bi- Dimensional Intrinsic Mode Function (BIMF). The greater part of the surface highlight is separated from its edges. BIMF is a novel decay method which is based on assortment of oscillatory mode signal. BIMF technique is for breaking down the indirect and non-fixed signs. The sign is decayed adaptively into natural oscillatory segments called inherent mode capacities. BEMD is a versatile deterioration measure, so the quantity of BIMF is controlled by the image information itself. The last perspective on the technique is to diminish the fogginess in the image and the fused image is acquired.

References

[1]. An, F. P., Lin, D. C., Zhou, X. W., & Sun, Z. (2015). Enhancing image denoising performance of bidimensional empirical mode decomposition by improving the edge effect. International Journal of Antennas and Propogation. https://doi.org/10.1155/2015/769478

[2]. Damerval, C., Meignen, S., & Perrier, V. (2005). A fast algorithm for Bi-dimensional EMD. IEEE Signal Processing Letters. 12(10), 701–704. https://doi.org/10.1109/LSP.2005. 855548

[3]. Dong, W., Li, X. E., Lin, X., & Li, Z. (2014). A bidimensional empirical mode decomposition method for fusion of multispectral and panchromatic remote sensing images. Remote Sensing, 6(9), 8446-8467. https://doi.org/10.3390/rs6098446

[4]. Hariharan, H., Gribok, A., Abidi, M. A., & Koschan, A. (2006). Image fusion and enhancement via empirical mode decomposition. Journal of Pattern Recognition Research, 1(1), 16-32.

[5]. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. https://doi.org/10.1098/ rspa.1998.0193

[6]. Li, H., Chai, Y., & Li, Z. (2013). Multi-focus image fusion based on non sub sampled contour let transform and focused regions detection. Optik, 124(1), 40-51. https://doi.org/10.1016/j.ijleo.2011.11.088

[7]. Liang, L. F., & Ping, Z. L. (2010). Medical image enhancement based on window empirical mode decomposition algorithm. Journal of Optoelectronics Laser, 21(9), 1421-1425.

[8]. Lin, Y. R., & Wang, Q. (2011). Extracting details from images based on 1-DEMD. Journal of Jilin University (Engineering and Technology Edition), 41(6), 1766-1770.

[9]. Linderhed, A. (2002, March). 2D empirical mode decompositions in the spirit of image compression. In Wavelet and Independent Component Analysis Applications IX (Vol. 4738, pp. 1-8). https://doi.org/10.1117/ 12.458772

[10]. Nunes, J. C., Bouaoune, Y., Delechelle, E., Niang, O., & Bunel, P. (2003). Image analysis by bidimensional empirical mode decomposition. Image and Vision Computing, 21(12), 1019-1026. https://doi.org/10.1016/S0 262-8856(03)00094-5

[11]. Nunes, J. C., Guyot, S., & Deléchelle, E. (2005). Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Machine Vision and Applications, 16(3), 177-188. https://doi. org/10.1007/s00138-004-0170-5

[12]. Pajares, G., & De La Cruz, J. M. (2004). A waveletbased image fusion tutorial. Pattern Recognition, 37(9), 1855-1872. https://doi.org/10.1016/j.patcog.2004.03.010

[13]. Qiao, L. H., Peng, L. Z., Guo, W., & Yuan, W. T. (2008, July). A novel image fusion algorithm based on 2D EMD and IHS. In 2008 International Conference on Machine Learning and Cybernetics (Vol. 7, pp. 4040-4044). IEEE. https://doi.org/10.1109/ICMLC.2008.4621109

[14]. Radhika, V., Veeraswamy, K., & Kumar, S. S. (2018). Digital image fusion using HVS in block based transforms. Journal of Signal Processing Systems, 90(6), 947-957. https://doi.org/10.1007/s11265-017-1252-8

[15]. Tanaka, T., & Mandic, D. P. (2007). Complex empirical mode decomposition. IEEE Signal Processing Letters, 14(2), 101-104. https://doi.org/10.1109/LSP.2006.882107

[16]. Xu, G. L., Wang, X. T., Xu, X. G., & Zhu, T. (2006). Image enhancement algorithm based on neighborhood limited empirical mode decomposition. DianziXuebao (Acta Electronica Sinica), 34(9), 1635-1639.

[17]. Zhang, B., Zhang, C., Wu, J., & Liu, H. (2014). A medical image fusion method based on energy classification of BEMD components. Optik, 125(1), 146- 153. https://doi.org/10.1016/j.ijleo.2013.06.075

[18]. Zheng, Y., & Qin, Z. (2009). Region-based image fusion method using bidimensional empirical mode decomposition. Journal of Electronic Imaging, 18(1), 013008. https://doi.org/10.1117/1.3099703

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Research Paper

Brain Tumor Detection using MRI and CT Image

Abid Khan*

Deparment of Electronics and Telecommunication Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.

Khan, A. (2020). Brain Tumor Detection using MRI and CT Image. i-manager's Journal on Image Processing, 7(3), 25-28. https://doi.org/10.26634/jip.7.3.17141

Abstract

It is very important to detect brain tumor at right time in brain tumor diagnosis. Image fusion for CT and MRI images have been used in this paper. Image fusion is used to get more information for brain tumor diagnosis. Resultant fused image of CT and MRI images will improve accuracy of tumor detection. MRI and CT scan images are very useful in tumor disease diagnosis. Brain image is easily degraded by noises. For noise minimizing and enhancing the image quality we use discrete wavelet transform method. It provides enhanced image quality. Here we are using image fusion method and morphological image processing for increasing image intensity. Image segmentation has been used to detect tumor portion accurately and indicate about growing area of tumor.

References

[1]. Ambily, P. K., James, S. P., & Mohan, R. R. (2015). Brain tumor detection using image fusion and neural network. International Journal of Engineering Research and General Science, 3(2), 1383-1388.

[2]. Anbumozhi, S., & Manoharan, P. S. (2014). Performance analysis of medical image fusion using multimodal graph cut algorithm. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 3(3), 802-807.

[3]. Angoth, V., Dwith, C. Y. N., & Singh, A. (2013). A novel wavelet based image fusion for brain tumor detection. International Journal of Computer Vision and Signal Processing, 2(1), 1-7.

[4]. Borole, V. Y., Nimbhore, S. S., & Kawthekar, D. S. S. (2015). Image processing techniques for brain tumor detection: A review. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 4(2), 1-14.

[5]. Davis, L. S. (1995). Edge detection techniques. Computer Graphics Image Process, 4, 248-270.

[6]. Maini, R., & Sohal, J. S. (2006). Performance evaluation of Prewitt edge detector for noisy images. International Journal on Graphics, Vision and Image Processing, 6(3), 39-46.

[7]. Senthilkumaran, N., & Thimmiaraja, J. (2014, February). Histogram equalization for image enhancement using MRI brain images. In 2014, World Congress on Computing and Communication Technologies (pp. 80-83). IEEE. https://doi. org/10.1109/WCCCT.2014.45

[8]. Shruti, S., & Jamuna, S. (2016). Brain image segmentation and tumour detection using adaptive clustering and RBF-SVM classifier. International Journal of Recent Trends in Engineering & Research (IJRTER), 2(4), 274-278.

[9]. Singh, A., Bajpai, S., Karanam, S., Choubey, A., & Raviteja, T. (2011). Malignant brain tumor detection. International Journal of Computer Theory and Engineering, 4(6), 163-167.

[10]. Singh, A., Bajpai, S., Karanam, S., Choubey, A., & Raviteja, T. (2012). Malignant brain tumor detection. International Journal of Computer Theory and Engineering, 4(6), 1002-1006.

[11]. Singh, S. (2014). Classification of human brain tumors th from MRI using K-NN algorithm. In Proceedings of 16 IRF International Conference (pp. 1-14).

[12]. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., ... & Willsky, A. (2003). A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging, 22(2), 137-154. https:// doi.org/10.1109/TMI.2002.808355

[13]. Velusamy, V., Karnan, M., Sivakumar, R., & Nandhagopal, N. (2014). Enhancement techniques and methods: A review. International Journal of Computer Science and Information Technologies, 5(1), 397-403.

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Review Paper

A Review of Document Image Noise Removal Techniques

Lalit Prakash Saxena*

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

Saxena, L. P. (2020). A Review of Document Image Noise Removal Techniques. i-manager's Journal on Image Processing, 7(3), 29-35. https://doi.org/10.26634/jip.7.3.17324

Abstract

The digitization of documents are popular with its enhanced portability, efficient storage, and easy retrieval. Digital acquisition of documents gets carried over through transmission with added noise. The process of removing the noises from digital images using the image processing techniques or image analysis methods or filters is referred to as noise removal methods, or techniques, or algorithms. This paper presents the review of literatures of noise removal methods efficient enough in removing unwanted noise from the document images. This paper discusses the methodology, contributions, advantages, and disadvantages of the reviewed methods. Further, it also highlights the problems in document image noise removal with the future scope.

References

[1]. Abreu, E., Lightstone, M., Mitra, S. K., & Arakawa, K. (1996). A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE Transactions on Image Processing, 5(6), 1012-1025. https:// doi.org/10.1109/83.503916

[2]. Badekas, E., & Papamarkos, N. (2007). Document binarisation using Kohonen SOM. IET Image Processing, 1(1), 67-84. https://doi.org/10.1049/iet-ipr:20050311

[3]. Bar, L., Brook, A., Sochen, N., & Kiryati, N. (2005a, October). Color image deblurring with impulsive noise. In International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision (pp. 49-60). Heidelberg, Berlin: Springer. https://doi.org/10.1007/11567 646_5

[4]. Bar, L., Sochen, N., & Kiryati, N. (2005b, April). Image deblurring in the presence of salt-and-pepper noise. In International Conference on Scale-Space Theories in Computer Vision (pp. 107-118). Heidelberg, Berlin: Springer. https://doi.org/10.1007/11408031_10

[5]. Bar-Yosef, I. (2005). Input sensitive thresholding for ancient Hebrew manuscript. Pattern Recognition Letters, 26(8), 1168-1173. https://doi.org/10.1016/j.patrec.2004. 07.014

[6]. Chan, R. H., Ho, C. W., & Nikolova, M. (2005). Salt-andpepper noise removal by median-type noise detectors and detail-preserving regularization. IEEE Transactions on Image Processing, 14(10), 1479-1485. https://doi.org/10. 1109/TIP.2005.852196

[7]. Chen, G., Chen, Q., Zhu, X., & Chen, Y. (2017, October). A study of historical documents denoising. In 2017, 10^th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISPBMEI) (pp. 1-4). IEEE. https://doi.org/10.1109/CISP-BMEI. 2017.8301947

[8]. Chinnasarn, K., Rangsanseri, Y., & Thitimajshima, P. (1998, November). Removing salt-and-pepper noise in text/graphics images. In IEEE Asia-Pacific Conference on Circuits and Systems APCCAS 1998 (pp. 459-462). IEEE. https://doi.org/10.1109/APCCAS.1998.743809

[9]. Gonzalez, R., & Woods, E. (2002). Digital Image nd Processing (2^nded). Upper Saddle River, New Jersey: Prentice Hall.

[10]. Khaffaf, H., Talib, A., & Salam, R. (2008). Removing salt-and-pepper noise from binary images of engineering drawings. In 19^th International Conference on Pattern Recognition, ICPR 2008 (pp. 1-4). IEEE.

[11]. Kim, G., Kim, J. G., Kang, K., & Yoo, W. S. (2019). Image-based quantitative analysis of foxing stains on old printed paper documents. Heritage, 2(3), 2665-2677. https://doi.org/10.3390/heritage2030164

[12]. Kollia, Z., Sarantopoulou, E., Cefalas, A. C., Kobe, S., & Samardzija, Z. (2004). Nanometric size control and treatment of historic paper manuscript and prints with laser light at 157 nm. Applied Physics A, 79(2), 379-382. https:// doi.org/10.1007/s00339-004-2539-8

[13]. Li, J., Li, Y., Luo, Y., Teri, G., Jia, Z., & Fu, P. (2020). th Characterization of an early 20 century Chinese manuscript with foxing stains. BioResources, 15(4), 9212- 9227.

[14]. Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., & Freeman, W. T. (2007). Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 299-314.

[15]. López-Rubio, E. (2010). Restoration of images corrupted by Gaussian and uniform impulsive noise. Pattern Recognition, 43(5), 1835-1846. https://doi.org/10. 1016/j.patcog.2009.11.017

[16]. Modica, A., Bruno, M., Di Bella, M., Alberghina, M. F., Brai, M., Fontana, D., & Tranchina, L. (2019). Characterization of foxing stains in early twentieth century photographic and paper materials. Natural Product Research, 33(7), 987- 996.

[17]. Pratt, W. (2001). Digital Image Processing: PIKS Inside (3^rd ed.). John Wiley & Sons.

[18]. Russ, J. (2007). The image processing handbook (5^th ed.). FL, USA: CRC Press.

[19]. Saba, T., Rehman, A., Al-Dhelaan, A., & Al-Rodhaan, M. (2014). Evaluation of current documents image denoising techniques: A comparative study. Applied Artificial Intelligence, 28(9), 879-887. https://doi.org/10.10 80/08839514.2014.954344

[20]. Saxena, L. P. (2014). An effective binarization method for readability improvement of stain-affected (degraded) palm leaf and other types of manuscripts. Current Science, 107(3), 489-496.

[21]. Saxena, L. P. (2019). Niblack's binarization method and its modifications to real-time applications: A review. Artificial Intelligence Review, 51(4), 673-705.

[22]. Singh, B., Chand, V., Mittal, A., & Ghosh, D. (2012). A comparative study of different approaches of noise removal for document images. In Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) (pp. 847-854). Springer, India.

[23]. Szulc, J., Otlewska, A., Ruman, T., Kubiak, K., Karbowska-Berent, J., Kozielec, T., & Gutarowska, B. (2018). Analysis of paper foxing by newly available omics techniques. International Biodeterioration & Biodegradation, 132, 157-165.

[24]. Tappen, M. F., Liu, C., Adelson, E. H., & Freeman, W. T. (2007, June). Learning gaussian conditional random fields for low-level vision. In 2007 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE. https://doi.org/10.1109/CVPR.2007.382979

[25]. Yang, Y., & Yan, H. (2000). An adaptive logical method for binarization of degraded document images. Pattern Recognition, 33(5), 787-807.

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

Current Issue Vol. 11 Issue 3

Most Read

Most Cited

Volume 7 Issue 3 July - September 2020

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

bamgboye* , Adeboye Olatunbosun **

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

Oladele, B. O., and Olatunbosun, A. (2020). Security and Personal Identification Automatic Bi-Modal Human Image Recognition System. i-manager's Journal on Image Processing, 7(3), 1-6. https://doi.org/10.26634/jip.7.3.17211

Abstract

References

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Image Data Recovery using User's Judgement Choice Technique

D. Saravanan*

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

Saravanan, D. (2020). Image Data Recovery using User's Judgement Choice Technique. i-manager's Journal on Image Processing, 7(3), 7-13. https://doi.org/10.26634/jip.7.3.17656

Abstract

References

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Image Fusion Method Based on Regional Feature and Improved Bi-Dimensional Intrinsic Mode Function

Radhika Vadhi * , Guru Vishnu Kesari **

* Department of Electronics and Communication Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, Andhra Pradesh, India. ** Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.

Vadhi, R., and Kesari, G. V. (2020). Image Fusion Method Based on Regional Feature and Improved Bi-Dimensional Intrinsic Mode Function. i-manager's Journal on Image Processing, 7(3), 14-24. https://doi.org/10.26634/jip.7.3.17674

Abstract

References

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Brain Tumor Detection using MRI and CT Image

Abid Khan*

Deparment of Electronics and Telecommunication Engineering, Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh, India.

Khan, A. (2020). Brain Tumor Detection using MRI and CT Image. i-manager's Journal on Image Processing, 7(3), 25-28. https://doi.org/10.26634/jip.7.3.17141

Abstract

References

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A Review of Document Image Noise Removal Techniques

Lalit Prakash Saxena*

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

Saxena, L. P. (2020). A Review of Document Image Noise Removal Techniques. i-manager's Journal on Image Processing, 7(3), 29-35. https://doi.org/10.26634/jip.7.3.17324

Abstract

References

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* Department of Electronics and Communication Engineering, Srinivasa Institute of Engineering and Technology, Amalapuram, Andhra Pradesh, India.

** Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India.