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 3 Issue 4 October - December 2016

Research Paper

Implementation of Color Image Segmentation Using Watershed Algorithm

M. Aravind Kumar* , Srinivas Bachu**

* Assistant Professor, Department of Electronics and Communication Engineering, Grandhi Varalakshmi Venkata Rao Institute of Technology, Andhra Pradesh, India.

** Associate Professor, Department of Electronics and Communication Engineering, Guru Nanak Institutions Technical Campus (Autonomous), Telangana, India.

Kumar, M. A., and Bachu, S. (2016). Implementation Of Color Image Segmentation Using Watershed Algorithm. i-manager's Journal on Image Processing, 3(4), 1-8.

Abstract

The objective of this paper is to improve the picture by utilizing division procedure for shading pictures. The most essential characteristic of division of a picture is its luminance, plentiful for a monochrome picture and shading parts of a shading picture. This paper proposes a shading based division strategy for utilization of watershed method. The watershed calculation can perform picture division utilizing numerical morphology. The individualization of an article from an advanced picture is a typical issue in the field of picture preparing. The authors introduced a versatile concealing and a thresholding instrument over every shading channel to overcome over division issue, before consolidating the division from every channel into the last one. The proposed strategy guarantees precision and nature of the shading pictures. The exploratory results are acquired utilizing Image Quality Assessment (IQA) measurements, for example, PSNR, MSE, and Color Image Quality Measure (CQM). The watershed change consolidated with a quick calculation based and topological angle approach gives great results. The numerical tests acquired outline of the effectiveness of the methodology for picture division. This methodology along these lines give a practical new answer for picture division, which might be useful in picture recovery. The trial results illuminate the viability of our way to deal with enhanced division quality in parts of accuracy and computational time. The reenactment results exhibit that the proposed calculation is promising.

References

[1]. Muthukannan K, and Merlin Moses. M, (2010). “Color Image Segmentation using K-means Clustering and Optimal Fuzzy C-Means Clustering”. Proceedings of the International Conference on Communication and Computational Intelligence – 2010, Kongu Engineering College, Perundurai, Erode, T.N., India. pp.229-234.

[2]. Chinki Chandhok, Soni Chaturvedi, and A. Akhurshid, (2012). “An Approach to Image Segmentation using K means Clustering Algorithm”. International Journal of Information Technology (IJIT), Vol. 1, No. 1, pp. 11-17.

[3]. Digital image processing 3^rd Edition (DIP/3e), by Gonzalez and Woods.

[4]. F. Destrempes, J.F. Angers, and M. Mignotte, (2006). “Fusion of hidden Markov random field models and its Bayesian estimation”. IEEE Trans. Image Process., Vol. 15, No. 10, pp. 2920–2935.

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[9]. S. Mary Praveena, and Ila Vennila, (2010). “Optimization Fusion Approach for Image Segmentation using K-Means Algorithm”. International Journal of Computer Applications, Vol. 2, No. 7.

[10]. Seber, G.A.F. (1984). Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc., 1984.

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[12]. A.M Uso, F. Pla, and P.G Sevila, (2006). “Unsupervised Image Segmentation using a Hierarchical Clustering Selection Process”. Structural Syntactic and Statistical Pattern Recognition, Vol. 4, pp. 799-807.

[13]. A.Z Arifin, and A. Asano, (2006). “Image Segmentation by histogram thresholding using hierarchical cluster analysis”. Pattern Recognition Letters, Vol. 27, No. 13, pp. 1515-1521.

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Research Paper

Image De-Noising with the Aid of Dual Tree Wavelet Transform And Artificial Bee Colony Optimization Algorithm

Yugandhar Dasari* , S.K. Nayak**

* Associate Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

** Professor, Department of Electronic Science, Berhampur University, Berhampur, Odisha.

Dasari, Y., and Nayak, S. K. (2016). Image De-Noising With The Aid Of Dual Tree Wavelet Transform And Artificial Bee Colony Optimization Algorithm. i-manager's Journal on Image Processing, 3(4), 9-18.

Abstract

Determination of the threshold value is extremely an important part in wavelet based image de-noising. Finding appropriate threshold value can be done either by using deterministic approaches or soft computing algorithms. Artificial Bee Colony (ABC) is one of the algorithms motivated by the intelligent behaviour of honey bees. In this paper, a nature inspired population based image de-noising technique has been implemented to find the dynamic threshold value using an ABC algorithm by using Dual Tree Complex Wavelet Transform (DT-CWT). The DT-CWT is a relatively recent enhancement to the Discrete Wavelet Transform (DWT) with two additional properties known as shift invariance and directional selectivity. These additional features are in turn used to preserve geometric image features like ridges and edges. The performance of the proposed method has been compared in terms of Peak Signal to Noise Ratio (PSNR) with conventional wavelet thresholding using DT-CWT and Adaptive Median Filter (AMF) techniques.

References

[1]. Gyaourova, A., Kamath, C., and Fodor, I.K. (2002). “Undecimated wavelet transforms for image de-noising”. Report, Lawrence Livermore National Lab., CA, pp. 18.

[2]. McGaffin, M.G., and Fessler, J.A. (2015). “Edge- Preserving Image De-noising via Group Coordinate Descent on the GPU”. IEEE Transactions on Image Processing, Vol. 24, No. 4, pp. 1273-1281.

[3]. Tian, D., Xue, D., and Wang, D. (2015). “A fractionalorder adaptive regularization primal–dual algorithm for image de-noising”. Information Science, Vol. 296, pp. 147–159.

[4]. Chen, D., Chen, Y., and Xue, D. (2015). “Fractionalorder total variation image de-noising based on proximity algorithm”. Applied Mathematics and Computation, Vol. 257, pp. 537-545.

[5]. Wei, Z., Wang, J., Nichol, H., Wiebe, S., and Chapman, D. (2012). “A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image”. Micron, Vol. 43, No. 2–3, pp. 170- 176.

[6]. Nordström, K.N. (1990). “Biased anisotropic diffusion: A unified regularization and diffusion approach to edge detection”. Image and Vision Computing, Vol. 8, No. 4, pp. 318-327.

[7]. Rudin, L.I., Osher, S., and Fatemi, E. (1992). “Nonlinear total variation based noise removal algorithms”. Physica D, Vol. 60, No. 1-4, pp. 259–268.

[8]. Pasti, L., Walczak, B., Massart, D.L., and Reschiglian, P. (1999). “Optimization of signal de-noising in discrete wavelet transform”. Chemometrics and Intelligent Laboratory Systems, Vol. 48, No. 1, pp. 21-34.

[9]. Donoho, D.L. (1995). “De-noising by softthresholding”. IEEE Transactions on Information Theory, Vol. 41, No. 3, pp. 613 – 627.

[10]. Chang, S.G., Yu, Bin, and Vattereli, M. (2000). “Adaptive Wavelet Thresholding for Image De-noising and Compression”. IEEE Trans. Image Processing, Vol. 9, No. 9, pp. 1532-1546.

[11]. El-Shehaby, I.A., and Tran, T.D. (2006). “On Local Computation of Wavelet Coefficients in the Dual-tree CWT”. IEEE Signals, Systems and Computers, pp. 411-415, Pacific Grove, CA.

[12]. Kingsbury, N.G. (2001). “Complex wavelets for shift invariant analysis and filtering of signals”. Applied and Computational Harmonic Analysis, Vol. 10, No. 3, pp. 234-253.

[13]. Karaboga, D. and Basturk, B. (2008). “On the performance of Artificial Bee Colony (ABC) algorithm”. Applied Soft Computing, Vol. 8, pp. 687–697.

[14]. Karaboga, D. and Akay, B. (2009). “A Comparative study of Artificial Bee Colony algorithm”. Applied Mathematics and Computation, Vol. 214, pp. 108-132.

[15]. Hwang, H. and Hadded, R. (1995). “Adaptive median filter: New algorithms and results”. IEEE Trans. Image Process., Vol. 4, No. 4, pp. 499–502.

[16]. Panetta, K., Zhou, Y., Agaian, S. and Jia, H. (2011). “ Nonlinear Unsharp Masking for Mammogram Enhancement ”. IEEE Transactions on Information Technology in Biomedicine, Vol. 15, No. 6, pp. 918-928.

[17]. Kingsbury, N.G. (1999). “Image processing with complex wavelets”. Philos. Trans. R. Soc. London A, Math. Phys. Sci., Vol. 357, No. 1760, pp. 2543–2560.

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Research Paper

Comparative Analysis of Advanced Reversible Watermarking Techniques

V. Belmer Gladson* , Y. Sam Josuva, R. Balasubramanian*

* Research Scholar, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamilnadu, India.

Assistant Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamilnadu, India.

* Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamilnadu, India.

Gladson, V. B., Josuva, Y. S., and Balasubramanian, R. (2016). Comparative Analysis of Advanced Reversible Watermarking Techniques. i-manager's Journal on Image Processing, 3(4), 19-25.

Abstract

The Reversible watermarking is a field of hiding the information, which hides the crucial information in different forms like an image, song, video for protection of illegal duplication and distribution of multimedia data. This research work is to embed data in encrypted images and decrypt the image to rebuild the original image by removing the hidden data without any distortion. There are many researches in this field and various techniques were proposed. So in order to choose which one is the best technique, a definite need arises to compare with the techniques like Least Significant Bit (LSB), Difference Expansion (DE), Reversible Contrast Mapping (RCM), Wavelet-Fuzzy (WF) and these reversible watermarking methods are analyzed with the help of metrics PSNR, MSE, Processing Time and Correlation. From the experimental results and performance evaluation, LSB is better, but based on correlation and after applying a median filter, Wavelet- Fuzzy (WF) provides better results.

References

[1]. Alattar, Adnan M. (2004). “Reversible watermark using the difference expansion of a generalized integer transform”. IEEE Transactions on Image Processing, Vol. 13, No. 8, pp. 1147-1156.

[2]. Thodi, Diljith M., and Jeffrey J. Rodríguez, (2007). “Expansion embedding techniques for reversible watermarking”. IEEE Transactions on Image Processing, Vol. 16, No. 3, pp. 721-730.

[3]. D. Coltuc, and J. M. Chassery, (2007). “Very fast watermarking by reversible contrast mapping”. IEEE Signal Process. Lett., Vol. 14, No. 4, pp. 255-258.

[4]. Patanwar, Abhishek, and Shikha Singh, (2015). “A Comparative Study of Reversible Watermarking Techniques”. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 4.

[5]. Lande, Pankaj U., Sanjay N. Talbar, and G. N. Shinde, (2010). “Robust image adaptive watermarking using fuzzy logic an FPGA approach”. International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 3, No. 4, pp. 43-54.

[6]. Arthi, R., V. Jaganya, and S. Poonkuntran, (2012). “Modified LSB watermarking for image authentication”. International Journal of Communication Technology, Vol. 3, No. 3, pp. 2231-0371.0.

[7]. Oueslati, Sameh, Adnane Cherif, and Bassel Solaiman, (2010). “Maximizing strength of digital watermarks using fuzzy logic”. Signal and Image Processing: An International Journal (SIPIJ), Vol. 1, No. 2, pp. 112-124.

[8]. Abhishek Patanwar, and Shikha Singh, (2015). “A Comparative Study of Reversible Watermarking Techniques”. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 4.

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Research Paper

Quantifying Deformation And Predicting Volcanic Eruption From SAR Satellite Images Using Digital Elevation Model

S. Muni Rathnam* , T. Ramashri**

* Professor & HOD, Department of Electronics and Communication Engineering, Balaji Institute of Engineering and Management Studies, Andhra Pradesh, India.

** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati, India.

Rathnam, S. M., and Ramashri, T. (2016). Quantifying Deformation And Predicting Volcanic Eruption From SAR Satellite Images Using Digital Elevation Model. i-manager's Journal on Image Processing, 3(4), 26-37.

Abstract

Monitoring of volcanoes using satellite images is gaining ground due to multiple advantages it offer. Satellite based radars in the form of Synthetic Aperture Radar (SAR) imaging can be a potent tool in monitoring these volcanoes. Changes in morphologies of volcano due to various deformations have been identified as precursor for volcanic activity. The deformation that can happen prior to the volcanic éruption cane enable better prediction. It has been identified through numerous researches that very strong correlation has occured between volcanic eruption and deformation. This work exploits the correlation to analyse morphological changes in volcano by creation of Digital Elevation Model (DEM). In order to create DEM, SAR images has been employed and interferogram is completed with the help of a processing implemented using MATLAB. This approach which accounts for various phase noises can result in the generation of DEM, which can identify the deformation post and preeruptions giving us on correlation between eruption and deformation.

References

[1]. Yu. B. Slezin, (2003). “The mechanism of volcanic eruptions”. Elsevier Journal of Volcanology and Geothermal Research, Vol. 122, pp. 7-50.

[2]. Donald A. Swanson, (2008). “The mechanism of volcanic eruptions”. Journal of Volcanology and Geothermal Research, Vol. 176, pp. 427–431.

[3]. Gregory A. Zielinski. (2002). “Climatic Impact of Volcanic Eruptions”. The Scientific World Journal, Vol. 2, pp. 869-884.

[4]. Saheb Karim, Farah Riadh Bassel Solaiman, and Ben- Ahmed Mohamed, (2008). “Toward a multi-temporal approach for satellite image interpretation”. The International Arab Journal of Information Technology, Vol. 5, No. 3, pp. 281-287.

[5]. Sathiya Vaidhiyanathan, and Victor Rajamanickam, (2009). “Assessment of Ocean Parameters through the Satellite Images – AOOPSI”. International Journal of Computer Theory and Engineering, Vol. 1, No. 4, pp. 420- 423.

[6]. Nigar Sulthana and Mahesh Chandra, (2010). “Image compression with Adaptive Arithmetic Coding”. International Journal of Computer Applications, Vol. 1, No. 18, pp. 31-34.

[7]. Joyce, Belliss, Samsonov, McNeil, and Glassey, (2009). “A review of the status of satellite remote sensing and image processing techniques for mapping hazards and disasters”. Progress in Physical Geography, Vol. 33, No. 2, pp. 183-207.

[8]. Mirnalinee Dhinesh, Sukhendu Das, and Koshy Varghese, (2008). “Automatic Curvilinear structure detection from satellite images using multi resolution GMM”. International Journal of Imaging Sciences and Engineering, Vol. 2, No. 1, pp. 154-157.

[9]. G. Jones, D.K. Chester, and F. Shooshtarian, (2009). “Statistical analysis of the frequency of eruptions at Furnas Volcano, Sa˜o Miguel, Azores”. Journal of Volcanology and Geothermal Research, Vol. 92, No. 1-2, pp. 31–38.

[10]. K.A. Reath, M.S. Ramsey, J. Dehn, and P.W. Webley, (2016). “Predicting eruptions from precursory activity using remote sensing data hybridization”. Journal of Volcanology and Geothermal Research, Vol. 321, pp. 18–30.

[11]. M. Kervyn, F. Kervyn, R. Goossens, S. K. Rowland and G. G. J. Ernst, (2007). Mapping Volcanic Terrain using High-resolution and 3D Satellite Remote Sensing. Geological Society, London, Special Publications. Vol. 283, pp. 5-30.

[12]. J. Biggs, S.K. Ebmeier, W.P. Aspinall, Z. Lu, M.E. Pritchard, R.S.J. Sparks, and T.A. Mather, (2014). “Global link between deformation and volcanic eruption quantified by satellite imagery”. Nature Communications, DOI: 10.1038/ncomms 4471.

[13]. Zhengxiao Li, and James Bethel, (2008). “Image Coregistration In SAR Interferometry”. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, Vol. Xxxvii. Part B1.

[14]. Zhong Lua, Daniel Dzurisin, Hyung-Sup Jung, Jixian Zhang, and Yonghong Zhang, (2010). “Radar image and data fusion for natural hazards characterization”. International Journal of Image and Data Fusion, Vol. 1, No. 3, pp. 217–242.

[15]. Roland Burgmann, Paul A. Rosen, and Eric J. Fielding, (2000). “Synthetic Aperture Radar Interferometry to Measure Earth's Surface Topography and its Deformation”. Annu. Rev. Earth Planet. Science, Vol. 28, pp. 169–209.

[16]. Carlos Lopez-Martínez, and Eric Pottier, (2007). “Coherence estimation in synthetic aperture radar data based on speckle noise modeling”. Applied Optics, Vol. 46, No. 4/1.

[17]. S. Li, H. P. Xu, and L. Q. Zhang, (2011). “An Advanced DSS-SAR InSAR Terrain Height Estimation Approach Based on Baseline De-Coupling”. Progress in Electromagnetics Research, Vol. 119, pp. 207-224.

[18]. E. Chaussard, F. Amelung, and Y. Aoki, (2013). “Characterization of open and closed volcanic systems in Indonesia and Mexico using InSAR time series”. Journal of Geophysical Research: Solid Earth, Vol. 118, pp. 3957–3969.

[19]. Zhong Lu, (2007). “InSAR Imaging of Volcanic Deformation over Cloud-prone Areas – Aleutian Islands”. Photogrammetric Engineering & Remote Sensing, Vol. 73, No. 3, pp. 245–257.

[20]. Ashar Muda Lubis, (2014). “Uplift of Kelud Volcano prior to the November 2007 Eruption as observed by LBand InSAR”. J. Eng. Technol. Sci., Vol. 46, No. 3, pp. 245- 257.

[21]. Anieri M. Morales Rivera, Falk Amelung, and Patricia Mothes, (2016). “Volcano deformation survey over the Northern and Central Andes with ALOS InSAR time series”. Journal on Geochemistry, Geophyscics, Geosystems, Vol. 17, No. 7, pp. 2869–2883.

[22]. Dr. Pratibha, P. Shingare, and Sumit S. Kale, (2013). “Review on Digital Elevation Model”. International Journal of Modern Engineering Research (IJMER), Vol. 3, No. 4, pp. 2412-2418.

[23]. Vivek Kumar Singh, Prashant Kumar Champati Ray, Ayyeum Perumal, and Thillai Jeyaseelan, (2010). “Orthorectification and Digital Elevation Model (DEM) Generation using Cartosat-1 Satellite Stereo Pair in Himalayan Terrain”. Journal of Geographic Information System, Vol. 2, pp. 85-92.

[24]. Samadrita Mukherjee, Anirban Mukhopadhyay, Ashutosh Brardwaj, Arun Mondal, Sananda Kundu, and Sugata Hazra, (2012). “Digital Elevation Model Generation and Retrieval of Terrain Attributes using CARTOSAT-1 Stereo Data”. International Journal of Science and Technology, Vol. 2, No. 5.

[25]. D. Raucouples, and Howard A. Zabker, (2008). “Ground deformation detection using radar interferogram technique”. Natural Hazards Earth Systems and Sciences, Vol. 8, pp. 779-788.

[26]. Andrew Hooper, and Howard A. Zabkar, (2007). “Phase unwrapping in three dimensions with application of InSAR time series”. Journal on Optics Survey on America, Vol. 24, No. 9.

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Survey Paper

A Review of Robust Hashing Methods for Content Based Image Authentication

Lokanadham Naidu Vadlamudi* , V. V. Rama Prasad, D. Vasumathi*

* Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Andhra Pradesh, India.

Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Andhra Pradesh, India.

* Professor, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Telangana, India.

Vadlamudi, L. N., Vaddella, R. P. V., and Devara, V. (2016). A Review Of Robust Hashing Methods For Content Based Image Authentication. i-manager's Journal on Image Processing, 3(4), 38-45.

Abstract

At present, the multimedia data will be transmitted rapidly using an internet. But, whether the data reaches the recipient without tampering is questionable. The current multimedia editing software allows adversaries to alter multimedia data without changing its visual perception. Different categories of approaches exist for verifying the integrity as well as authenticity of multimedia data. The Content Based Authentication (CBA) is one among them. This review paper describes various Content Based Image Authentication (CBIA) methods, which have been developed for integrity verification of images. And also, this paper summarizes various techniques used in existing image authentication methods to extract robust features. In addition, this paper discusses the important characteristics of a CBIA approach, limitations of existing methods and future research directions. The development of robust and secure image authentication method for content authentication is still an open challenge for current researchers.

References

[1]. Yong Soo Choi and Jong Hyuk Park, (2012). “Image Hash Generation Method using Hierarchical Histogram”. Journal of Multimedia Tools and Applications, Vol. 61, No. 1, pp. 181-194.

[2]. Rui Sun and Wenjun Zeng, (2012). “Secure and Robust Image Hashing via Compressive Sensing”. Journal of Multimedia Tools and Applications, Vol. 70, No. 3, pp. 1- 15.

[3]. Guangjie Liu, et al. (2011). “A Passive Image Authentication Scheme for Detecting Region Duplication Forgery with Rotation”. Journal of Network and Computer Applications, Vol. 34, No. 5, pp. 1557-1565.

[4]. Shijun Xiang and Hyoung Joong Kim, (2011). “Histogram Based Image Hashing for Searching Content- Preserving Copies”. Transactions on Data Hiding Multimedia Security VI, Vol. 6730, pp. 83-108.

[5]. Li Weng and Bart Preneel, (2011). “A Secure Perceptual Hash Algorithm for Image Content Authentication”. Communications and Multimedia Security, Vol. 7025, pp. 108-121.

[6]. Yanqiang Lei, Yuangen Wang, and Jiwu Huang, (2011). “Robust Image Hash in Radon Transform domain for Authentication”. Journal of Signal Processing: Image Communication, Vol. 26, No. 6, pp. 280-288.

[7]. Zhihua Xu, et al. (2011). “A Novel Image Copy Detection Scheme Based on the Local Multi-resolution Histogram Descriptor”. Journal of Multimedia Tools and Applications, Vol. 52, No. 2-3, pp. 445-463.

[8]. Zhenjun Tang, et al. (2011). “Lexicographical Framework for Image Hashing with Implementation based on DCT and NMF”. Journal of Multimedia Tools and Applications, Vol. 52, No. 2-3, pp. 325-345.

[9]. Lina Wang, et al. (2011). “Image Authentication based on Perceptual Hash using Gabor Filters”. Journal of Soft Computing, Vol. 15, No. 3, pp. 493-504.

[10]. Nighat Jamil and Arshad Aziz, (2010). “A Unified Approach to Secure and Robust Hashing Scheme for Image and Video Authentication”. In Proc. of 3^rd Int. Congress on Image and Signal Processing (CISP-2010), pp. 274-278.

[11]. Shui-Hua Han and Chao-Hsien Chu, (2010). “Content-based Image Authentication: Current Status, Issues and Challenges”. International Journal of Information Security, Vol. 9, No. 1, pp. 19-32.

[12]. Fawad Ahmed, M.Y. Siyal, and Vali Uddin Abbas, (2010). “A Secure and Robust Hash-Based Scheme for Image Authentication”. Journal of Signal Processing, Vol.90, No. 5, pp. 1456-1470.

[13]. Yang Ou, Chul Sur, and Kyung Hyune Rhee, (2010). “Discriminative Image Hashing Based on Region of Interest”. In Proc. of 16 Int. Conf. on Advances in Multimedia Modeling, Vol. 5916, pp 701-706.

[14]. Ammar M. Hassan, et al. (2009). “Semi Fragile Image Authentication using Robust Image Hashing with Localization”. In Proc. of Second International Conf. on Machine Vision (ICMV-09), pp. 133-137.

[15]. Marco Tagliasacchi, et al. (2009). “Hash-Based Identification of Spare Image Tampering”. IEEE Trans. on Image Processing, Vol. 18, No. 11, pp. 2491-2504.

[16]. Han-Ling Zhang, et al. (2009). “Content Based Image Hashing Robust to Geometric Transformation”. In Proc. of Second Int. Symposium on Electronic Commerce and Security (ISECS-09), pp. 105-108.

[17]. S.M. Saad, (2009). “Design of a Robust and Secure Digital Signature Scheme for Image Authentication over Wireless Channels”. IET Information Security, Vol. 3, No. 1, pp. 1-8.

[18]. Adil Haouzia and Rita Noumeir, (2008). “Methods for image authentication: A Survey”. Journal of Multimedia Tools Applications, Vol. 39, No. 1, pp. 1-46.

[19]. Swinathan Ashwin, et at. (2006). “Robust and Secure Image Hashing”. IEEE Trans on Inf. Forensics and Security, Vol.1, No. 2, pp. 215-230.

[20]. Shijun Xiang, et al. (2007). “Histogram based Image Hashing Scheme Robust against Geometric Deformations”. In Proc. of 9^th International Workshop on Multimedia and Security (MMS-07), pp. 121-128.

[21]. Vishal Monga and M. Kivac Mihcak, (2007). “Robust and Secure Image Hashing via Non–Negative Matrix Factorizations”. IEEE Transaction on Information Forensics and Security, Vol. 2, No. 3, pp. 376-390.

[22]. Kai Chen, Xingleizhu, and Zhishou Zhang, (2007). “A Hybrid Content-Based Image Authentication Scheme”. In Proc. of Advances in Multimedia Information Processing (PCM-2007), Vol. 4810, pp. 226-235.

[23]. Young-Dong Zhang, et al. (2007). “Secure and Incidental Distortion Tolerant Digital Signature for Image Authentication”. Journal of Computer Science and Technology, Vol. 22, No. 4, pp. 523-526.

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

Current Issue Vol. 11 Issue 2

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Volume 3 Issue 4 October - December 2016

Implementation of Color Image Segmentation Using Watershed Algorithm

M. Aravind Kumar* , Srinivas Bachu**

Kumar, M. A., and Bachu, S. (2016). Implementation Of Color Image Segmentation Using Watershed Algorithm. i-manager's Journal on Image Processing, 3(4), 1-8.

Abstract

References

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Image De-Noising with the Aid of Dual Tree Wavelet Transform And Artificial Bee Colony Optimization Algorithm

Yugandhar Dasari* , S.K. Nayak**

* Associate Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India ** Professor, Department of Electronic Science, Berhampur University, Berhampur, Odisha.

Dasari, Y., and Nayak, S. K. (2016). Image De-Noising With The Aid Of Dual Tree Wavelet Transform And Artificial Bee Colony Optimization Algorithm. i-manager's Journal on Image Processing, 3(4), 9-18.

Abstract

References

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Comparative Analysis of Advanced Reversible Watermarking Techniques

V. Belmer Gladson* , Y. Sam Josuva**, R. Balasubramanian***

Gladson, V. B., Josuva, Y. S., and Balasubramanian, R. (2016). Comparative Analysis of Advanced Reversible Watermarking Techniques. i-manager's Journal on Image Processing, 3(4), 19-25.

Abstract

References

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Quantifying Deformation And Predicting Volcanic Eruption From SAR Satellite Images Using Digital Elevation Model

S. Muni Rathnam* , T. Ramashri**

* Professor & HOD, Department of Electronics and Communication Engineering, Balaji Institute of Engineering and Management Studies, Andhra Pradesh, India. ** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati, India.

Rathnam, S. M., and Ramashri, T. (2016). Quantifying Deformation And Predicting Volcanic Eruption From SAR Satellite Images Using Digital Elevation Model. i-manager's Journal on Image Processing, 3(4), 26-37.

Abstract

References

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A Review of Robust Hashing Methods for Content Based Image Authentication

Lokanadham Naidu Vadlamudi* , V. V. Rama Prasad**, D. Vasumathi***

Vadlamudi, L. N., Vaddella, R. P. V., and Devara, V. (2016). A Review Of Robust Hashing Methods For Content Based Image Authentication. i-manager's Journal on Image Processing, 3(4), 38-45.

Abstract

References

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* Associate Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

** Professor, Department of Electronic Science, Berhampur University, Berhampur, Odisha.

V. Belmer Gladson* , Y. Sam Josuva, R. Balasubramanian*

* Professor & HOD, Department of Electronics and Communication Engineering, Balaji Institute of Engineering and Management Studies, Andhra Pradesh, India.

** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University, Tirupati, India.

Lokanadham Naidu Vadlamudi* , V. V. Rama Prasad, D. Vasumathi*