i-manager Publications

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

Current Issue Vol. 10 Issue 4

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

Most Cited

Volume 1 Issue 4 October - December 2014

Research Paper

Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images

S. Muni Rathnam* , T. Ramashri**

* H.O.D of Electronics and Communication, Balaji Institute of Engineering And Management Studies, Nellore(D), Andhra Pradesh, India.

** Professor in Electronics and Communication, Sri Venkateswara College of Engineering, SV University, Tirupati, Chittoor (D), Andhra Pradesh, India.

Rathnam, M., and Ramashri, T. (2014). Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images. i-manager’s Journal on Image Processing, 1(4), 1-7. https://doi.org/10.26634/jip.1.4.3034

Abstract

The explosive growth of remote sensing technology, internet and multimedia systems poses great challenge in handling huge amount of data. Advancement in the field of Remote Sensing has gone to an extent of taking the geospatial accuracy to few centimeters. The use of remote sensing of natural hazards and disasters has become common. Remote sensing plays a vital role because of their pressing need in the analysis of natural hazards. Among the various hazards, the Volcanoes are terrific hazards which may harm the nature as well as the living things. Here, the identification of volcanoes and their hotspot identification are important to protect the living things. Hence, the present investigation is utilized to identify the volcanoes and their hotspot from the satellite images. Therefore to overcome the aforesaid problems, we are going to identify the hotspot of volcano using the Artificial Neural Network (ANN) which uses Resilient Back Propagation (RBP) Algorithm. At first, the color space of the satellite image will be converted to another color space to identify the contents of the image clearly. Then image will be segmented to identify the volcano's hotspot. To improve the accuracy, eight Statistical parameters are extracted from satellite image such as mean, variance, contrast, homogeneity, energy, correlation, standard deviation and entropy. The proposed mechanism will be developed with the aid of the platform MATLAB (version 7.11).

References

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[2]. Mohammad Awad. ( April 2010). “An Unsupervised Artificial Neural Network Method for Satellite Image Segmentation”. The International Arab Journal of Information Technology. Vol.7, No.2, pp.199-207,

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

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

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

[6]. Sadykhov, Dorogush, Pushkin, Podenok and Ganchenko. (July 2007). “Multispectral Satellite Images Processing For Forests And Wetland Regions Monitoring Using Parallel MPI Implementation”. In proceedings of ENVISAT Symposium. Monteux, Switzerland.

[7]. Fleury, Self and Downton. (2005). “Multi-spectral Satellite Image Processing on a Platform FPGA Engine”. In Military and Aeronautics Logic Devices (MAPLD'05).

[8]. Ahmed Rekik, Mourad Zribi, Ahmed Ben Hamida and Mohammed Benjelloun. (2009). “An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k-Means Clustering Algorithm Initialization”. International Journal of Signal Processing. Vol.5, No.1, pp.38-45.

[9]. Farnood Ahmadi, Valadan Zoej, Ebadi and Mokhtarzade. (2008). “Road Extraction from High Resolution satellite images using Image Processing Algorithms and CAD based Environments Facilities”. Journal of Applied Sciences. Vol.8, No.17, pp.2975-2982.

[10]. Ahmed Rekik, Mourad Zribi, Ahmed Ben Hamida and Mohammed Benjelloun. (October 2007). “Review of satellite image segmentation for an optimal fusion system based on the edge and region approaches”. IJCSNS. Vol.7,No. 10, pp.242-250.

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

[12]. Mirnalinee Dhinesh, Sukhendu Das, Koshy Varghese. (January 2008). “Automatic Curvilinear Structure detection from Satellite Images using Multi resolution GMM”. International Journal of Imaging Science and Engineering. Vol.2,No.1,pp.154-157.

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[14]. Shwetank, Jain Kamal and Bhatia. (June 2010). “Review of Rice Crop Identification and Classification using Hyper- Spectral Image Processing System”. International Journal of Computer Science & Communication. Vol.1, No.1, pp.253-258.

[15]. Ashok and Rajan. (Aug 2010). “Multi Spectral Image Enhancement in Satellite Imagery”. ACS-International Journal on Computational Intelligence. Vol.1, No.1, pp.13-20.

[16]. V.V.Joseph Rajapandian and N. Gunaseeli. (Oct 2007). “Modified Standard Back Propagation Algorithm with Optimum Initialization for Feedforword Neural Network”. International Journal of Image Science and Engineering. Vol.1, No.3.

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[18]. Chien Sheng and Szu Lin Su. (2010). “Resilient Back Propagation Neural Network for Approximation 2- DGDOP”, Proceedings of the International Multi Conference of Engineers & Computer Scientists. Vol.II, Hong Kong.

[19]. S. Muni Rathnam and T. Ramashri. (March 2014). “Identification of volcano hotspots by using standard back propagation (SBP) algorithm via satellite images”. Journal of Theoretical and Applied Information Technology. Vol. 61. No.1.

[20]. S. Muni Rathnam and T. Ramashri. (May-Jun. 2014). “Identification of volcano hotspots by using Modified Standard Back propagation (MBP) algorithm via satellite images”. IOSR Journal of VLSI and Signal Processing (IOSRJVSP). Vol. 4, No. 3, Ver. I.

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Research Paper

Unconstrained Handwritten Text Line Segmentation: An Improved Technique

Y.C. Kiran* , Mohammadi, V.N. Manjunath Aradhya*

* Department of Computer science, Jain University, Bengaluru, India.

Behsan Mellat, Iran

* Department of MCA, Sri Jayachamarajendra College of Engineering, Mysuru, India.

Kiran, Y.C., Mohammadi, and Aradhya, V.N. (2014). Unconstrained Handwritten Text Line Segmentation: An Improved Technique. i-manager’s Journal on Image Processing, 1(4), 8-16. https://doi.org/10.26634/jip.1.4.3035

Abstract

Handwritten text line segmentation is still a challenging area in document image analysis. This is due to the variation of handwritten style from individual to individual causing many problems like multi-skewed, overlapping and touching text lines. In this paper, authors have proposed an improved technique for handwritten text line segmentation to the method proposed in [1]. The proposed method consists of four major phases, which includes piece-wise painting algorithm, skew estimation, line drawing and edge detection process. The proposed method is implemented on dataset of Kannada, English and comprising of Persian, Oriya and Bangla documents. The success rate of the technique, as observed experimentally was satisfactory and it may contribute significantly for the development of applications related to handwritten OCR (Optical Character Recognition).

References

[1]. A. Alaei, U. Pal, and P. Nagabhushan (2011). A new scheme for unconstrained handwritten text-line segmentation. Pattern Recognition, Vol.44(4),pp. 917–928.

[2]. U. Pal ans S. Datta (2003). Segmentation of bangla unconstrained handwritten text. In proceedings of 7th Intl conference on Document Analysis and Recognition, Vol. 2, pp.1128-1132

[3]. S. Basu, C. Chaudhuri, M. kundu, M. Nasipuri, and D.K. Basu (2007). Text line extraction from multi-skewed handwritten documents. Pattern Recognition, Vol.40(4), pp. 1825–1839.

[4]. B.B. Chaudhuri and S. Bera (2009). Handwritten text line identification in indian scripts. In the proceedings of 10th International Conference on Document Analysis and Recognition, pp. 636–640.

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[7]. M. Liwicki and H. Bunke (2009). Combining diverse on-line and off-line systems for handwritten text line recognition. Pattern Recognition, Vol.42(12), pp. 3254–3263.

[8]. G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatis (2009). Text line and word segmentation of handwritten documents. Pattern Recognition, Vol.42, pp. 3169–3183.

[9]. G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis (2009). Text line detection in handwritten documents. Pattern Recognition, Vol.41, pp. 3758–3772.

[10]. P. Nagabhushan and A. Alaei (2010). Tracing and straightening the baseline in handwritten persian/arabic text-line: A new approach based on painting -technique. Intl Journal on Computer Science and Engineering, Vol. 2(4), pp.907-916.

[11]. A. Nicolaou and B. Gatos (2009). “Handwritten text line segmentation by shredding text into its lines”. In proceedings of 10th Intl conference on Document Analysis and Recognition, pp. 626–630.

[12]. N.K.Garg, L. Kaur, and M.K. Jindal (2010). A new method for line segmentation of handwritten hindi text. In proceeding of 7th Intl conference on Information technology, pp. 392–397.

[13]. N. Ouwayed, A. Belaid, and F. Aanger (2010). General text line extraction approach based on locally orientation estimation. In proceedings of 17th Intl conference on Document Recognition and Retrieval.

[14]. V. Papavassiliou, T.S. Lakis, V. Kastsourous, and G. Carayannis (2010). Handwritten document image segmentation into text lines and words. Pattern Recognition, Vol.43, pp. 369–377.

[15]. P.P. Roy, U. pal, and J. Liados (2010). Morphology based handwritten line segmentation using foreground and background information. In proceeding of Intl conference on Frontiers in Handwriting Recognition.

[16]. A. Sanchez, P.D. Saurez, C.A.B. Mello, A.L.I. Oliveria, and V.M.O (2009). Alves. Text line segmentation in images of handwritten historical documents. In proceedings of IEEE Trans on Image Processing Theory, Tool and Applications, pp. 1–6.

[17]. Z. Shi, S. Setlur, and V. Govindaraju (2009). A steerable directional local profile technique for extraction of handwritten arabic text lines. In proceedings of 10th Intl conference on Document Analysis and Recognition, pp. 176–180.

[18]. X.Du, W. Pan, and T.D. Bui. Text line segmentation in handwritten documents using mumford shah model Pattern Recognition, Vol.42(12), 3136–3145.

[19]. F. Yin and C. L. Liu (2009). A variational bayes method for handwritten text line segmentation. In proceeding of 10th Intl conference on Document Analysis and Recognition, pp. 436–440.

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Research Paper

Performance Analysis of Coiflets Wavelets forSegmentation of Heart Sound

Lekram Premlal Bahekar* , Sharadkumar Ghadale, Kiran Kawale*, Mahendra Bisen****

*-**** Assistant Professor and Research Scholar, Department of Electronics & Telecommunication, Madhukarrao Pandav College of Engineering, Bhandara, India.

Bahekar, L.P., Ghadale, S., Kawale, K., and Bisen, M. (2014). Performance analysis of Coiflets wavelets for segmentation of heart sound. i-manager’s Journal on Image Processing, 1(4), 17-25. https://doi.org/10.26634/jip.1.4.3036

Abstract

This paper discusses the usage of digital signal processing techniques on phonocardiography (PCG) waveforms and presents all the cardiac signals and their dates on the PC. This makes it easy for medical professionals to interpret disorders and make a better diagnosis. A segmentation which detects a single cardiac cycle (S1-Systole-S2-Diastole) of Phonocardiogram (PCG) signals using coiflets wavelet family and heart sound is classify into three types Normal (N), Systolic murmur (S) and Diastolic murmur (D). This paper proposed an adaptive sub-level tracking algorithm based on wavelet transform to separate the S1 and S2 from other components such as murmurs and noises Citerai of time interval, energy and phonocardiogram (PCG) collecting position which are used to identify S1 with respect to the beginning of each cardiac cycle. The pre-processing before calculating energy of PCG signal by wavelet, segments the PCG signal, and finds different parameters for segmentation. This paper presents an analysis of coiflets wavelet segmentation of heart sound segmentation techniques and suggested performance measures.

References

[1]. Lekram Bahekar1, Abhishek Misal2, G. R. Sinha3 (2014). “Heart Sound Segmentation Techniques: A Survey” IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), e-ISSN: 2278-1676, p-ISSN: 2320- 3331 pp. 46-49

[2]. Chen Tian-hua Guo Pei-yuan Xing Su-xia Zheng Yu (2010). “A New Method of Sorting of Heart Sound Signal Based on Wavelet Transform and Parameter Model Method” IEEE Xplore Digital library, pp 1-4.

[3]. Zhihai Tu1, Guitao Cao1 , Qiao Li2, Xianxia Zhang3 (2010). Jun Shi4 “Improved Methods for Detecting Main Components of Heart Sounds”, Sixth International Conference on Natural Computation. IEEE Xplore Digital library, pp 3585-3588.

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[9]. S. Rajan, E. Budd, M. Stevenson, R. Doraiswami (2006). “Unsupervised and Uncued Segmentation of the Fundamental Heart Sounds in Phonocardiograms Using a Time-Scale Representation”, Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3,pp 3732-3735.

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[12]. Zamri Mohd Zin, Sheikh Hussain Salleh, Shaparas Daliman, M. Daud Sulaiman (2003). “Analysis of Heart Sounds Based on Continuous WaveletTransform” Student Conference on Research and Development (SCOReD) Proceedings, Putrajaya, Malaysia,pp 19-22.

[13]. Myint W,W,Di llard B (2001). “An electronic stethoscope with diagnosis capability”, Southeastern Symposium on System Theory, pp. 133-137,2001

[14]. P.S.Vikhe, S.T.Hamde, N.S.Nehe (2009). Wavelet Transform Based Abnormality Analysis of Heart Sound, International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp 367- 371

[15]. Thomos, N., Boulgouris, N. V., & Strintzis, M. G. (2006). Optimized Transmission of JPEG2000 Streams Over Wireless Channels. IEEE Transactions on Image Processing, Vol.15 (1), pp.54-67.

[16]. D. Della Giustina, M. Fanfulla, M. Malcangi, F. Belloni, M. Riva (2009). ” A general end-point detection algorithm for cardiac sounds” Advances in mathematical and computational methods, pp 181-186.

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Research Paper

Performance analysis of Anisotropic Diffusion filtering with Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

* Professor,Department of Electronics and Communication, Lendi Institute of Engineering & Technology, Vizianagaram, India.

Professor, Department of Electronics and Communication, Andhra University, Visakhapatnam, India.

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

Sridhar, B., Reddy, K.V.V.S., and Prasad, A.M. (2014). Performance Analysis Of Anisotropic Diffusion Filtering With Mathematical Morphology. i-manager’s Journal on Image Processing, 1(4), 26-35. https://doi.org/10.26634/jip.1.4.3037

Abstract

Mathematical Morphology is an efficient tool to extract the features from robust medical images. It provides a broad set of operations, which highlight the edges and improve the quality of the image. Anisotropic diffusion filtering has been widely applied as a mechanism for intra region smoothing of images. This paper aims to extend existing work in the development quality of medical image by using mathematical morphology and anisotropic vector gradient operators. Such types of operators may be anisotropic with respect to their shape and capacity to smooth the image locally as part of the feature extraction process. The proposed algorithm is applied on mammogram images. The performance of method is evaluated through peak signal to noise ratio, mean square error and structure similarity index measurement are calculated and plotted against the number of iterations of filter.

References

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Review Paper

An investigation of Various Image Denoising Filters for Gray Scale and color Images

V. Murugan* , T. Avudaiappan, R. Balasubramanian*

* Research Scholar, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu.

Research Scholar, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu

* Professor, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu.

Murugan, V., Avudaiappan, T., and Balasubramanian, R. (2014). An investigation of Various Image Denoising Filters for Gray Scale and color Images. i-manager’s Journal on Image Processing, 1(4), 36-42. https://doi.org/10.26634/jip.1.4.3038

Abstract

A variety of medical and satelite images are essential as sources of in sequence for study and understanding. When an image is transformed from one form to another such as scanning, transmitting, digitizing, storing etc., degradation occurs to the output image. For this reason, the output image wants to be better in order to be recovered . This paper presents a detailed survey on various noise detection and reduction algorithms. Different noises will affect the image in different ways. A detailed survey of the research is needed in order to design the filter which will execute the needed aspects along with handling most of the image filtering issues

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

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Volume 1 Issue 4 October - December 2014

Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images

S. Muni Rathnam* , T. Ramashri**

* H.O.D of Electronics and Communication, Balaji Institute of Engineering And Management Studies, Nellore(D), Andhra Pradesh, India. ** Professor in Electronics and Communication, Sri Venkateswara College of Engineering, SV University, Tirupati, Chittoor (D), Andhra Pradesh, India.

Rathnam, M., and Ramashri, T. (2014). Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images. i-manager’s Journal on Image Processing, 1(4), 1-7. https://doi.org/10.26634/jip.1.4.3034

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Unconstrained Handwritten Text Line Segmentation: An Improved Technique

Y.C. Kiran* , Mohammadi**, V.N. Manjunath Aradhya***

* Department of Computer science, Jain University, Bengaluru, India. ** Behsan Mellat, Iran *** Department of MCA, Sri Jayachamarajendra College of Engineering, Mysuru, India.

Kiran, Y.C., Mohammadi, and Aradhya, V.N. (2014). Unconstrained Handwritten Text Line Segmentation: An Improved Technique. i-manager’s Journal on Image Processing, 1(4), 8-16. https://doi.org/10.26634/jip.1.4.3035

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Performance Analysis of Coiflets Wavelets forSegmentation of Heart Sound

Lekram Premlal Bahekar* , Sharadkumar Ghadale**, Kiran Kawale***, Mahendra Bisen****

*-**** Assistant Professor and Research Scholar, Department of Electronics & Telecommunication, Madhukarrao Pandav College of Engineering, Bhandara, India.

Bahekar, L.P., Ghadale, S., Kawale, K., and Bisen, M. (2014). Performance analysis of Coiflets wavelets for segmentation of heart sound. i-manager’s Journal on Image Processing, 1(4), 17-25. https://doi.org/10.26634/jip.1.4.3036

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Performance analysis of Anisotropic Diffusion filtering with Mathematical Morphology

B. Sridhar* , K.V.V.S. Reddy**, A.M. Prasad***

Sridhar, B., Reddy, K.V.V.S., and Prasad, A.M. (2014). Performance Analysis Of Anisotropic Diffusion Filtering With Mathematical Morphology. i-manager’s Journal on Image Processing, 1(4), 26-35. https://doi.org/10.26634/jip.1.4.3037

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An investigation of Various Image Denoising Filters for Gray Scale and color Images

V. Murugan* , T. Avudaiappan**, R. Balasubramanian***

Murugan, V., Avudaiappan, T., and Balasubramanian, R. (2014). An investigation of Various Image Denoising Filters for Gray Scale and color Images. i-manager’s Journal on Image Processing, 1(4), 36-42. https://doi.org/10.26634/jip.1.4.3038

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* H.O.D of Electronics and Communication, Balaji Institute of Engineering And Management Studies, Nellore(D), Andhra Pradesh, India.

** Professor in Electronics and Communication, Sri Venkateswara College of Engineering, SV University, Tirupati, Chittoor (D), Andhra Pradesh, India.

Y.C. Kiran* , Mohammadi, V.N. Manjunath Aradhya*

* Department of Computer science, Jain University, Bengaluru, India.

Behsan Mellat, Iran

* Department of MCA, Sri Jayachamarajendra College of Engineering, Mysuru, India.

Lekram Premlal Bahekar* , Sharadkumar Ghadale, Kiran Kawale*, Mahendra Bisen****

B. Sridhar* , K.V.V.S. Reddy, A.M. Prasad*

V. Murugan* , T. Avudaiappan, R. Balasubramanian*