- K. Sambandan
- N. Raman
- S. Raja Balachandar
- S. G. Venkatesh
- S. K. Ayyaswamy
- R. Dharmarajan
- G. K. Rastogi
- B. Satheesh Raja
- G. Prema
- C. Aravindan
- R. Maheswaran
- S. Kother Mohideen
- S. Arumuga Perumal
- N. Krishnan
- K. Arulmozhi
- N. Sathish Kumar
- S. Sendhil Kumar
- D. Rajalakshmi
- R. Srikanth
- S. Gnanavel
- S. Kavitha
- M. Dhinesh Kumar
- K. Rajesh
- D. Sarala
- V. Venkataraman
- G. Balamurugan
- S. Ramesh
- K. Raja
- D. Dinesh
- K. Rajan
- S. Wesley Moses Samdoss
- Indian Forester
- Indian Journal of Science and Technology
- International Journal of Computational and Applied Mathematics
- The Indian Practitioner
- Programmable Device Circuits and Systems
- Networking and Communication Engineering
- Digital Image Processing
- Artificial Intelligent Systems and Machine Learning
- International Journal of Plant Sciences
- Asian Journal of Pharmaceutical Research and Health Care
- ICTACT Journal on Image and Video Processing
- ICTACT Journal on Management Studies
- Rashtriya Krishi (English)
- International Journal of Advanced Networking and Applications
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kannan, K.
- Vesicular - Arbuscular Mycorridzae of Casuarina Equisetifolia forst. in Four Different Soil Types in Tamil Nadu
Authors
Source
Indian Forester, Vol 120, No 6 (1994), Pagination: 510-514Abstract
Mycorrhizal association in Casuarina equisetifolia grown in four different soil types, namely sandy loam soil, red soil, clay soil and sandy soil was inveastigated. The plants from all four sites were found to Possess hyphae, vesicles and arbuscles in their ischolar_mains. The four types of soil were nitrogen deficient but rich in phosphorus and potassium and soils were acidic to alkaline. The rhizosphere soils harboured Spores of different VAM fungi and diversity of spore types were observed in all four site. A total of 10 species of Glomus, 2 ofGigaspora and 2 of Sclerocystic In the rhizosphere soils were recorded. In four sites, variation in per cent ischolar_main colonization and number of spores per 100g of soil were noted.- Newton's Law of Gravity-based Search Algorithms
Authors
1 Department of Mathematics, SASTRA University, Thanjavur, 613001, IN
Source
Indian Journal of Science and Technology, Vol 6, No 2 (2013), Pagination: 4141-4150Abstract
Many heuristic optimization methods have been developed in recent years that are derived from Nature. These methods take inspiration from physics, biology, social sciences, and use of repeated trials, randomization, and specific operators to solve NP-hard combinatorial optimization problems. In this paper we try to describe the main characteristics of heuristics derived from "Newton's law of gravitation", namely a gravitational emulation local search algorithm and a gravitational search algorithm. We also present the detailed survey of distinguishing properties, parameters and applications of these two algorithms.Keywords
Meta-Heuristic Algorithms, Gravitation, Newton's Law of Gravity, Combinatorial Optimization Problems, NP-HardReferences
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- Wavelet Solution for Class of Nonlinear Integro-differential Equations
Authors
1 Department of Mathematics, School of Humanities and Sciences, SASTRA University, Thanjavur-613401, Tamilnadu, IN
Source
Indian Journal of Science and Technology, Vol 6, No 6 (2013), Pagination: 4670-4677Abstract
The aim of this work is to study the Legendre wavelets for the solution of a class of nonlinear Volterra integro-differential equation. The properties of Legendre wavelets together with the Gaussian integration method are used to reduce the problem to the solution of nonlinear algebraic equations. Also a reliable approach for convergence of the Legendre wavelet method when applied to nonlinear Volterra equations is discussed. Illustrative examples have been discussed to demonstrate the validity and applicability of the technique and the results obtained by Legendre wavelet method is very nearest to the exact solution. The results demonstrate reliability and efficiency of the proposed method.Keywords
Legendre Wavelets, Integro-differential Equations, Gaussian Integration, Legendre Wavelet Method, Convergence AnalysisReferences
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- Abbasbandy S (2006b). Application of He’s homotopy perturbation method for Laplace transform, Chaos Solitons & Fractals, vol 30(5), 1206-1212.
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- Ghasemi M, Kajani C M T et al. (2006). Numerical solution of linear integro-differential equations by using sine-cosine wavelet method, Applied Mathematics and Computation, vol 180(2), 569-574.
- Ghasemi M, Kajani C M T et al. (2007a). Comparison between wavelet Galerkin method and homotopy perturbation method for the nonlinear integro-differential equations, Computers Mathematics with Applications, vol 54, 1162-1168.
- Ghasemi M, Kajani C M T et al. (2007b). Comparison between the homotopy perturbation method and the sine-cosine wavelet method for solving linear integro-differential equations, Computers Mathematics with Applications, vol 54(7-8), 1162-1168.
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- On Minimal Transversals in Simple Hypergraphs
Authors
1 Department of Mathematics, SASTRA University, Thanjavur 613402, IN
Source
International Journal of Computational and Applied Mathematics, Vol 7, No 2 (2012), Pagination: 119-124Abstract
We formulate and prove a variation of Claude Berge's proposition to characterize minimal transversals in simple hypergraphs. Our hypothesis is weaker than Berge's and so our characterization is eventually stronger. We also analyze some properties of simple hypergraphs from the viewpoint of the number of elements in minimal transversals.Keywords
Simple Hypergraph, Hyperedge, Minimal TransversalReferences
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- Puttaraju C., Hamiltonian Distance Generating Sets of Graphs and Groups, Ph.D Thesis (2008), Visvesvaraya Technological University, Belgaum.
- Puttaraju C and B.Sooryanarayana, Hamilton Distance Sets of a Path, International Journal of Computational and Applied Mathematics, volume5, number5(2010), pp.637-647.
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- Radio Isotopes in Endocrinology
Authors
1 Department of Endocrinology. Poslyruduate Institute of Medical Education and Research, Chandigarh, IN
Source
The Indian Practitioner, Vol 29, No 5 (1976), Pagination: 321-327Abstract
No AbstractKeywords
No Keywords- Brand Preference and Usage of Air Compressor in Coimbatore Textile Industry
Authors
1 RVS Institute of Management Studies, Coimbatore, IN
Source
Programmable Device Circuits and Systems, Vol 3, No 14 (2011), Pagination: 830-833Abstract
The topic of brand preference has drawn substantial attention in recent years as a field of study and it is fair to say that much has been learned. It is a dynamic field and many discoveries are still to be made. The study of brand preference possesses important problems for both marketers and customers. In the current scenario, where the competition is tough, customers choose according to their own will and pleasure and hence there exists a preference of brand. Moreover there are several factors which play a vital role in the decision making process of brand preference. There are numerous manufacturers marketing their Air Compressor under different brand names. Customers are aware and prefer a particular brand when it is available in the market. Moreover they make enquiries about the best brand available and they have realized that quality of the product matters most. Since branding plays an important role in determining the product‟s ultimate success or failure, the researcher has made an attempt to assess the brand preference and usage of Air Compressor in textile industry.- Generic Modeling of Realistic Movement Pattern of Nodes in MANET
Authors
1 Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IN
2 SSN College of Engineering, Chennai, Tamilnadu, IN
3 SASTRA University, Thanjavur, Tamilnadu, IN
4 Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, IN
Source
Networking and Communication Engineering, Vol 3, No 1 (2011), Pagination: 20-25Abstract
A multi-hop mobile ad hoc network (MANET) consist of a group of mobile wireless nodes formed by the wireless devices carried by the people in any environment which are capable of forming a self configured wireless network that operate without infrastructure support and take part in routing packets among themselves in a cooperative manner. Mobility of nodes that represent the actual movement pattern of the mobile nodes plays an important role in MANET routing protocol‟s performance evaluation. In this work, a generic realistic movement pattern of nodes in MANET is simulated with the help of a Personal behavior model that dictates the spatial placement of individual mobile nodes in the environment based on the probability of visit to the attraction points in the environment with the help of the node specific and environment specific parameters is proposed. The developed model is based on the list of activities intended to be performed in the environment in attraction points where nodes fulfill their intended activities rather than the random movement. This model is adaptive since the mobility behavior of mobile nodes at any environment can be modeled by observing the environment and obtaining the node specific and environment specific inputs.Keywords
MANET, Mobility Model, Probability Transition Matrix, and Movement Pattern of Nodes.- A Novel Approach to Image Denoising by Combining Neighshrink and Sureshrink in Wavelet Domain
Authors
1 Department of Computer Science, Sadakathullah Appa College, Tirunelveli-627011, Tamilnadu, IN
2 Department of Computer Science, S. T. Hindu College, Nagarcoil-629002, Tamilnadu, IN
3 Centre for Information Technology and Engineering, Manonmanium Sundaranar University, Tirunelveli, Tamilnadu-627012, IN
4 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626001, Tamilnadu, IN
Source
Digital Image Processing, Vol 2, No 2 (2010), Pagination: 60-67Abstract
Removing noise from the original image is still a challenging problem for researchers. A traditional way to remove noise from image data is to employ spatial filters. With wavelet transform gaining popularity in the last two decades, various algorithms for denoising in wavelet domain were introduced. In this paper, it is proposed to combine Neighshrink and Sureshrink to denoise an image corrupted by additive white Gaussian noise in wavelet domain.Keywords
Image Denoising, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.- Area Level Fusion of Multi-Focused Images Using Dual Tree Complex Wavelet Packet Transform
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 5 (2009), Pagination: 213-220Abstract
The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion,namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at thatlocation. Multi scale transform fusion uses transform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform suffers due to poor directionality and does not provide a geometrically oriented decomposition in multiple directions. One way to generalize the discrete wavelet transform so as to generate a structured dictionary of base is given by the Discrete Wavelet Packet Transform (DWPT). This benefit comes from the ability of the wavelet packets to better represent high frequency content and high frequency oscillating signals in particular. However, it is well known that both DWT and DWPT are shift varying. The Dual Tree Complex Wavelet Transform (DTCWT) introduced by Kingsbury, is approximately shift -invariant and provides directional analysis. And there are three levels for image fusion namely pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Dual Tree Complex Wavele Packet Transform (DTCWPT), extending the DTCWT as the DWPT extends the DWT and the performance is measured in terms of various performance measures like ischolar_main mean square error, peak signal to noise ratio, quality index and normalized weighted performance metric.
Keywords
Image fusion, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.- Area level fusion of Multi-focused Images using Double Density DWT and DTCWT
Authors
1 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
2 Department of Computer Science, St. Hindu Colleg, Nagarcoil–629 002, Tamilnadu, IN
3 Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar–626 001, Tamilnadu, IN
Source
Digital Image Processing, Vol 1, No 6 (2009), Pagination: 231-242Abstract
Image fusion is a process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely spatial fusion and multi scale transform fusion. In spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Multi scale transform fusion usestransform for representing the source image at multi scale. The most common widely used transform for image fusion at multi scale is Discrete Wavelet Transform (DWT) since it minimizes structural distortions. But, wavelet transform cannot provide efficient approximation for directional features of images which in turn affects the performance of DWT-based image fusion schemes. Many multi scale tools have been invented to boost image fusion performance by incorporating directional representation. These tools can be classified into two categories according to the domain where they are designed: Spatial-domain Multiscale Directional Transform (SMDT) and Frequency domain Multiscale Directional Transform (FMDT). In FMDT, the basis functions of each subband orient at a certain direction, overcoming the poor directionality of 2-D DWT. Representative work includes curvelets, contourlets, bandelets, directionlets, multiscale directional filter banks, and complex wavelets. The critically sampled DWT is not a shift-invariant discrete transform, but the Dual Tree Complex Wavelet Transform (DT-CWT) introduced by Kingsbury is approximately shift -invariant and provides directional analysis whereas the undecimated DWT (UDWT) is an exactly shift-invariant transform. When J scales are implemented, the UDWT is expansive by the factor J + 1. The Double-density Discrete Wavelet Transform (DDWT) proposed by Ivan W. Selesnick provides a compromise between the UDWT and the critically-sampled DWT. A Double-density DTCWT (DDT-CWT), also proposed by Ivan W. Selesnick is an over-complete DWT designed to simultaneously possess the good properties of the DDWT and the DTCWT. And there are three levels for image fusion amel pixel level, area level and region level. In this paper, it is proposed to implement area level fusion of multi focused images using Double Density DWT and DTCWPT and the performance is measured in terms of various performance measures like ischolar_main mean square error and peak signal to noise ratio.
Keywords
Image fusion, DDWT, DDT-CWT, Root Mean Square Error, Peak Signal to Noise Ratio.- Experimental and Computational Investigation of Drag Co-Efficient of a Commercial Car Model
Authors
1 PSG College of Technology, Coimbatore-641004, IN
2 Dept. of Production Engineering, PSG College of Technology, Coimbatore-641004, IN
3 Aeronautical Engineering, Karpagam Institute of Technology, Coimbatore-641105, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 8 (2011), Pagination: 558-563Abstract
It is essential that aerodynamics can be taken in to account during the design of cars as an improved aerodynamics in car would attain higher speeds and more fuel efficiency which leads to less fuel consumption and vehicle stability and reduces shortage of fuel. In this paper the external aerodynamics of a commercial car model was investigated numerically and validated the results experimentally with wind tunnel. The objective of the current work is to check the drag and lift co-efficient of model, as well as pressure distribution of the commercial car model in wind tunnel, second to develop virtual wind tunnel environment and conduct trials. In this paper 1:14 scale down car model is used and by applying reverse engineering concept CAD (Computer Aided Design) data is extracted from the scale down model for Computational Fluid Dynamics (CFD) analysis. ANSA and T-grid is used as pre-processor and Fluent as solver for CFD analysis.Keywords
CFD, Reverse Engineering, Wind Tunnel Test.- Soft Strongly g-Closed Sets
Authors
1 Department of Mathematics, Srinivasa Ramanujan Centre, SASTRA University, Kumbakonam - 612 001, Tamil Nadu, IN
2 Department of Mathematics, SASTRA University, Thanjavur - 613 401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 18 (2015), Pagination:Abstract
Background/Objectives: The objective of the present paper is to define soft strongly g-closed sets and soft strongly g-open sets in soft topological spaces and study their basic properties. Methods: Here we used the concept of soft closure of soft interior and soft open sets to define soft strongly g-closed sets. Findings: The relationship between soft strongly g-closed sets and other existing sets has been investigated. Further the union, intersection of two soft strongly g-closed sets have been obtained. The authors discussed the complements of soft strongly g-closed sets in the last section. Conclusion/Improvements: In future, the varieties of new continuous mappings and separation axioms based on these sets may be introduced and the future research may be undertaken in this direction.Keywords
Soft Strongly g-closed Sets, Soft Strongly g-open Sets, Soft g-closed Sets, Soft g-open Sets, Soft rg-closed Sets, Soft rg-open Sets- Laboratory Testing of Plant Originated Oils and Oil Cakes against the Fungal Pathogen Alternaria alternata - Causative Agent of Leaf Spot Disease in Aloe vera
Authors
1 Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
Source
International Journal of Plant Sciences, Vol 11, No 2 (2016), Pagination: 240-243Abstract
In Aloe vera (L) Burn F. Syn. Aloe barbadensis (Miller), leaf spot disease caused by Alternaria alternata is a serious fungal disease. Management of the disease through fungicides alone lead to cause soil residual problems and heatlth hazards, besides involving higher input cost. Hence, attempts were made to manage the disease using environment safer components like plant oils and oil cakes. In this present study, plant oils and oil cakes extract were screened against the fungus, among the oils and oil cakes Eucalyptus oil (2%) and Mahua cake extract (10%) were found superior in reducing the fungal growth.Keywords
Laboratory, Plant, Oil cakes, Alternaria alternata, Aloe vera.References
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- Karthikeyan, M. (1999). Studies on onion (Allium cepa var aggregatum L.) leaf blight caused by Alternaria palandui Ayyangar. M.Sc., (Ag.) Thesis, Tamil Nadu Agricultural University, Coimbatore, India. 120 pp.
- Komathi, K. (2002). Studies on biological management of ischolar_main rot of groundnut (Arachis hypogaea L.) caused by Sclerotium rolfsii Sacc. M.Sc. (Ag.) Thesis, Tamil Nadu Agricultural University, Coimbatore, India. 96 pp.
- Mohan, K. (1996). Management of onion (Allium cepa L.) leaf blight disease incited by Alternaria palandui Ayyangar with special reference to biological control. M.Sc. (Ag.) Thesis, Tamil Nadu Agricultural University, Coimbatore, India. 174 p.
- Pandey, K.K., Pandey, P.K. and Padhyay, J.P.V. (2000). Selection of potential isolate of biocontrol agents based on biomass production, growth rate and antagonistic capability. Veg. Sci., 27 :194-196.
- Schmitz, H. (1930). Poisoned food technique. Industrial & Engg. Chem. Analyst, 2 : 361.
- Sujatha Bai, E. (1992). Studies on fruit rot of chillies (Capsicum annum L. ) caused by Alternaria tennuisNees. M.Sc., (Agri) Thesis, Tamil Nadu Agricultural University, Coimbatore, India. 173 pp.
- In Vitro Antioxidant and Anticancer Activities of Seed Extract of Solanum virginianum
Authors
Source
Asian Journal of Pharmaceutical Research and Health Care, Vol 7, No 1 (2015), Pagination: 1-5Abstract
The aim of the present study is to evaluate the antioxidant and anticancer activities of seed extract of Solanum Virginianum. The antioxidant activity was assessed using DPPH scavenging assay while the anticancer property of the Solanum virginanum in swiss albino mice against Dalton Ascites Lymphoma (DAL).Tumor was induced in mice by intraperitoneal inoculation of Dalton Ascites Lymphoma cells (1 x 105 cells / mouse).The seed extract was found to possess significant antioxidant and anticancer activities.Keywords
Antioxidant, Antitumor, Dalton Ascites Lymphoma , Solanum virgininum- Hypergraph-based Algorithm for Segmentation of Weather Satellite Imagery
Authors
1 Department of Computer Science, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
2 Department of Mathematics, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 36 (2016), Pagination:Abstract
Objective: Classification of cloud images through segmentation of automated satellite images to improvise the level of accuracy. Method Analysis: To classify cloud images the hyper graph model uses the idea of maximally bonded subsets that is endowed with integer valued metric are applied to receive the classifications. The widely used hyper graph model is Intensity Neighborhood Hyper graph (INHG) and representation model in this article is Intensity Interval Hyper graph (IIHG). Findings: The results obtained through this process is proved to be more accurate and the time complexity is O(n) in weather prediction. Similarly, the results received through IIHG, which also provides the same computational complexity where all the pixels to be processed with less time. Enhancement: The proposed methodology increases the accuracy level of prediction with less computation time and this work can be enhanced by including pattern recognition in automated processing.Keywords
Hyper Graph, INHG, IIHG, Satellite Imagery, Segmentation.- Multifocus Image Fusion Using Cloud Model
Authors
1 Department of Mechanical Engineering, Kamaraj College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 2 (2014), Pagination: 944-947Abstract
This paper proposes a multifocus image fusion algorithm based on cloud model. First, each source images are divided into overlapping image blocks of size (2N+1) × (2N+1) and then the mean and entropy of every image pixels over this neighborhood window was calculated and compared in Cloud domain. The pixel with higher magnitude of the calculated image features was selected to form the fused image. The results of multifocus image fusion using this algorithm hold favorable consistency in terms of ischolar_main mean square error, peak signal to noise ratio and quality index for three pairs of test images and confirm the effectiveness of the proposed algorithm.Keywords
Multi Focus Image Fusion, Cloud Model.- Upshot of Derivatives on Spot Market Volatility - An Industry Specific Analysis on Indian Stock Market
Authors
1 Department of Management Studies, The American College, IN
2 Department of Management Studies, Mepco Schlenk Engineering College, IN
Source
ICTACT Journal on Management Studies, Vol 1, No 4 (2015), Pagination: 205-215Abstract
This paper attempts to check whether the spot market volatility variation is an act of derivatives or merely industry specific factors only. This study is based on 23 stocks of six different industries of the Indian stock market. Among the 23 stocks, 10 stocks are derivatives stocks and the remaining 13 are non-derivative stocks of National Stock Exchange of India. Volatility in the selected stocks was modelled with GJR GARCH model for both pre-introduction and post introduction period of derivatives as it measures asymmetric effect also in addition to volatility changes. Changes in volatility, asymmetric effect, and volatility pattern of the selected stocks were examined separately. It was found that all the derivative stocks except HUL and CIPLA had a reduction in volatility after the introduction of derivatives. Most of the Non-Derivatives stocks had also experienced reduced volatility. Further, an industry wise analysis was done to find the effect of industry specific factors influencing volatility. Among the six select industries, five industries' stocks prove the effect of derivatives while one industry, Finance-Housing confirms the effect of industry specific factors.Keywords
Derivatives, Volatility, Volatility Pattern, GJR GARCH, Asymmetry.- A Statistical Sharpness Measure Based Multi Focus Image Fusion Using Double Density Discrete Wavelet Transform
Authors
1 Department of Mechanical Engineering, Kamaraj College of Engineering and Technology, IN
2 Department of Computer Science, S.T. Hindu College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 3, No 3 (2013), Pagination: 577-582Abstract
Image fusion is the process of combining two or more images of the same scene to form the fused image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. Transform fusion uses transform for representing the source image at multi scale. Due to the compactness, orthogonality and directional information, the Discrete Wavelet Transforms and its undecimated version are used for image fusion. These transforms can be implemented using perfect reconstruction Finite Impulse Response filter banks which are either symmetric or orthogonal. To design filters to have both symmetric and orthogonal properties, the number of filters is increased to generate M-band transform. Double density Discrete Wavelet Transform is an example of M-band DWT and consists of one scaling and two wavelet filters. In this paper, an approach for DDWT based image fusion is designed using statistical property of wavelet filters in representing the sharpness and its performance is measured in terms of Root Mean Square Error, Peak to Signal Noise Ratio, Quality Index.Keywords
Image Fusion, Discrete Wavelet Transform (DWT), Finite Impulse Response Filter, M-Band Transform and Double Density Discrete Wavelet Transform (DDWT).- Removal of Impulsive Noise Using Weighted Fuzzy Mean Filter Based on Cloud Model
Authors
1 Department of Mechanical Engineering, Kamaraj College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Anna University, Regional Centre, Madurai, IN
Source
ICTACT Journal on Image and Video Processing, Vol 4, No 1 (2013), Pagination: 661-666Abstract
This paper proposes a weighted fuzzy mean filter based on cloud model and reports its performance in removing the impulsive noise from the digital image. In addition, the performance of the proposed weighted fuzzy mean filter is compared with already existing variants of median and switching filters using ischolar_main mean square error, peak signal to noise ratio and quality index. Even though the image is corrupted by 90%, this weighted fuzzy mean filter is capable of recovering the original image with good detail preservation.Keywords
Weighted Fuzzy Mean Filter, Cloud Model, Median Filters, Switching Filters.- Optimal Level of Decomposition of Stationary Wavelet Transform for Region Level Fusion of Multi-Focused Images
Authors
1 Kamaraj College of Engineering and Technology, Tamil Nadu, IN
2 S.T. Hindu College, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 1, No 2 (2010), Pagination: 76-79Abstract
In machine vision, due to the limited depth-of-focus of optical lenses in CCD devices, it is not possible to have a single image that contains all the information of objects in the image. To achieve this, image fusion is required which is usually refers to the process of combining two or more different images, each containing different features into a new single image retaining important features from each and every image with extended information content. The approaches to image fusion can be classified into two namely Spatial Fusion and Transform fusion. The most commonly used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and this disadvantage is overcome by Stationary Wavelet Transform. This paper describes the optimum level of decomposition of Stationary Wavelet Transform for region based fusion of multi focused images in terms of various performance measures.Keywords
Image Fusion, Region Level Fusion, Discrete Wavelet Transform and Stationary Wavelet Transform.- Decision Making Process for B2C Model Using Behavior Analysis with Big Data Technologies
Authors
1 Department of Computer Science and Engineering, Sathyabama University, Chennai - 600119, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Alpha College of Engineering, Chennai - 600035, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 24 (2016), Pagination:Abstract
Objectives: Business to Consumer (B2C) E-Commerce activities are developed with a large number through agent-based systems. Case Based Reasoning (CBR) has been applied in these systems by analyzing the consumer buying behavior to provide consumers, a support to the decision making process. Analysis: Current applications of CBR to E-Commerce are limited to fixed, unchangeable products. To make the environment support for configurable products, an interactive operator based customization approach from CBR can be applied. Findings: In this work, to make the process more reliable and efficient, real time data from provisional stores has been taken and the system is trained to predict the consumer buying behavior along with CBR to pave way for a consumer to make a better decision making process. Applications/ Improvements: This work also applies big data concepts in predicting the behavior of the consumers. It thereby also led the customers to mine about their preferences in purchasing necessary products.Keywords
Big Data, Business to Consumer E-Commerce Activities, Case based Reasoning, Multi Agent Systems, Semantic Web.- Buckwheat (Fagopyrum esculentum)-A Multipurpose Cover Crop for Hilly Regions
Authors
1 ICAR- Indian Institute of Soil and Water Conservation, Research Centre, Vasad, Anand (Gujarat), IN
2 ICAR - Indian Institute of Soil and Water Conservation, Research Centre, Udhagamandalam (T.N.), IN
Source
Rashtriya Krishi (English), Vol 13, No 2 (2018), Pagination: 99-101Abstract
Buckwheat is a fast growing short-duration cover crop. It establishes, blooms and ready for incorporation within 35 to 40 days after sowing and its residue decomposes quickly. As a grain, it reaches maturity in just 70 to 90 days. Buckwheat suppresses weeds and attracts beneficial insects and pollinators with its abundant blossoms. It is easy to kill and reportedly mobilizes soil phosphorus from soil better than other cover crops. Buckwheat thrives well in cool and moist conditions but it is not frost tolerant. Buckwheat is not a drought tolerant crop and readily wilts under hot and dry conditions.- Analysis on the Performance of Bilateral Filters in Multi Focused Image Fusion
Authors
1 Department of Mechatronics Engineering, Kamaraj College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2182-2185Abstract
Multi focused image fusion combines two or more images focusing different objects in the same scene to produce all-in-one focus image without artifacts and noises. Among two scale edge preserving filters used in multi focused image fusion, Bilateral Filters plays a vital role since it preserves edge information and avoids staircase effect. This paper analyses the performance of Standard Bilateral Filter (SBF) and its variant Robust Bilateral Filter (RBF) and Weighted Bilateral Filters (WBF) in fusing multi focused images in terms of Quality Index and Mutual Information.Keywords
Image Fusion, Multi focused Images, Bilateral Filters, Quality Index and Mutual Information.References
- K.N. Chaudhury, “Fast and Accurate Bilateral filtering using Gauss-Polynomial Decomposition”, Proceedings of IEEE International Conference on Image Processing, pp. 2005-2009, 2015.
- K.N. Chaudhury and K. Rithwik, “Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach”, Proceedings of IEEE International Conference on Image Processing, pp. 108-112, 2015.
- K.N. Chaudhury, D. Sage and M. Unser, “Fast O(1) Bilateral Filtering using Trigonometric Range Kernels”, IEEE Transactions on Image Processing, Vol. 20, No. 12, pp. 3376-3382, 2011.
- F. Durand and J. Dorsey, “Fast Bilateral Filtering for the Display of High Dynamic-Range Images”, ACM Transactions on Graphics, Vol. 21, No. 3, pp. 257-266, 2002.
- W. Gan, X. Wu, W. Wu, X. Yang, C. Ren, X. He and K. Liu, “Infrared and Visible Image Fusion with the Use of Multi-Scale Edge-Preserving Decomposition and Guided Image Filter”, Infrared Physics and Technology, Vol. 72, pp. 37-51, 2015.
- M. Haghighat, A. Aghagolzadeh and H. Seyedarabi, “A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features”, Computers and Electrical Engineering, Vol. 37, No. 5, pp. 744-756, 2011.
- M. Haghighat and M.A. Razian, “Fast-FMI: Non-Reference Image Fusion Metric”, Proceedings of 8th International Conference on Application of Information and Communication Technologies, pp. 1-3, 2014.
- K. He, J. Sun and X. Tang, “Guided Image Filtering”, Proceedings of 11th European Conference on Computer Vision, pp. 1-14, 2010.
- K. He, J. Sun and X. Tang, “Guided Image Filtering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 6, pp. 1397-1409, 2013.
- S. Li, X. Kang and J. Hu, “Image Fusion with Guided Filtering”, IEEE Transactions on Image Processing, Vol. 22, No. 7, pp. 2864-2875, 2013.
- S. Li, X. Kang and J. Hu, “Performance Comparison of Different Multi-Resolution Transforms for Image Fusion”, Information Fusion, Vol. 12, No. 2, pp. 74-84, 2011.
- Y. Liu, S. Liu and Z. Wang, “A General Framework for Image Fusion based on Multi-Scale Transform and Sparse Representation”, Information Fusion, Vol. 24, No. 2, pp. 147-164, 2015.
- A. Mittal, R. Soundararajan and A.C. Bovik, “Making a Completely Blind Image Quality Analyzer”, IEEE Signal Processing Letters, Vol. 22, No. 3, pp. 209-212, 2013.
- A. Mittal, A.K. Moorthy, and A.C. Bovik, “No-Reference Image Quality Assessment in the Spatial Domain”, IEEE Transactions on Image Processing, Vol. 21, No. 12, pp. 4695-4708, 2012.
- S. Paris, P. Kornprobst, J. Tumblin and F. Durand, “Bilateral Filtering: Theory and Applications”, Now Publishers, 2009.
- S. Paris and F. Durand, “A Fast Approximation of the Bilateral Filter using a Signal Processing Approach”, Proceedings of European Conference on Computer Vision, pp. 568-580, 2006.
- Peter J. Burt and Raymond J. Kolczynski, “Enhanced Image Capture through Fusion”, Proceedings of 4th IEEE International Conference on Computer Vision, pp. 173-182, 1993.
- P. Perona and J. Malik, “Scale-Space and Edge Detection using Anisotropic Diffusion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 7, pp. 629-639, 1990.
- F. Porikli, “Constant Time O(1) Bilateral Filtering”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
- K. Sugimoto and S.I. Kamata, “Compressive Bilateral Filtering”, IEEE Transactions on Image Processing, Vol. 24, No. 11, pp. 3357-3369, 2015.
- C. Tomasi and R. Manduchi, “Bilateral Filtering for Gray and Color Images”, Proceedings of IEEE International Conference on Computer Vision, pp. 839-846, 1998.
- G. Qu, D. Zhang and P. Yan, “Information Measure for Performance of Image Fusion”, Electronics Letters, Vol. 38, No. 7, pp. 313-315, 2002.
- Q. Yang, K.H. Tan and N. Ahuja, “Real-Time O(1) Bilateral Filtering”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 557-564, 2009.
- Z. Zhou, B. Wang, S. Li and M. Dong, “Perceptual Fusion of Infrared and Visible Images through A Hybrid Multi-Scale Decomposition with Gaussian and Bilateral Filters”, Information Fusion, Vol. 30, pp. 15-26, 2016.
- Z. Wang, A. Bovik, H. Sheikh and E. Simoncelli, “Image Quality Assessment: from Error Visibility to Structural Similarity”, IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612, 2004.
- Application of Partial Differential Equations in Multi Focused Image Fusion
Authors
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai – 625 701, IN
Source
International Journal of Advanced Networking and Applications, Vol 14, No 1 (2022), Pagination: 5266-5270Abstract
Image Fusion is a process used to combine two or more images to form more informative image. More often, machine vision cameras are affected by limited depth of field and capture the clear view of the objects which are in focus. Other objects in the scene will be blurred. So, it is necessary to combine set of images to have the clear view of all objects in the scene. This is called Multi focused image fusion. This paper compares and presents the performance of second order and fourth order partial differential equation in multi focused image fusion.Keywords
Depth of Field, Image Fusion, Multi Focused Image Fusion, Partial Differential Equations.References
- Yong Yang, Yue Que, Shu-Ying Huang, Pan Lin, "Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network”, Signal, Image and Video Processing, 11(3), 2016, 439-446
- Durga Prasad Bavirisetti and Ravindra Dhuli, "Fusion of Infrared and Visible sensor images based on Anisotropic diffusion and Karhunen Loeve Transform”, IEEE Sensors Journal, 16(1), 2015, 203-209.
- Perona P, and Malik J, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., 12(7), 1990, 629–639.
- You Y L, and Mostafa Kaveh. "Fourth-order partial differential equations for noise removal." IEEE Transactions on Image Processing, 9(10), 2000, 1723-1730.
- Durga Prasad Bavirisetti, Gang Xiao, Gang Liu. "Multi-sensor image fusion based on fourth order partial differential equations", 20th International Conference on Information Fusion (Fusion), 2017.
- S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency”, Image and Vision Computing, 26 (7), 2008, 971-979.
- Xiaoye Zhang, Yong Ma, Fan Fan, Ying Zhang, and Jun Huang, "Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition," J. Opt. Soc. Am. A, 34, 2017, 1400-1410.
- Wang Z., Bovik A.C., Sheik H. R. and Simoncelli E. P., “ Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Trans. Image Processing, 13, 2004.
- Haghighat, M., Aghagolzadeh, A., Seyedarabi, H., "A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features," Computers and Electrical Engineering, 37(5), 2011, 744-756.
- Haghighat, M., Razian, M.A., "Fast-FMI: non-reference image fusion metric," Proc. 8th International Conference on Application of Information and Communication Technologies (AICT), 2014, 1-3.
- Dhirendra Pal Singh, “Role of Statistical Parameter in Digital Image Enhancement”, Int. J. Advanced Networking and Applications, 11(4), 2020, 4350-4353.
- Gan W., Wu X., Wu, W., Yang, X., Ren, C., He, X., and Liu, K., “Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,” Infrared Physics & Technology, 72, 2015, 37–51.
- He K., Sun J., and Tang, X. , “Guided image filtering,” in Proc. Eur. Conf. Comput. Vis., Heraklion, Greece, 2010, 1–14.
- He K., Sun J., and Tang, X. , “Guided image filtering,” TPAMI, 35(6), 2013,1397–1409.
- Li, S., Kang, X. and Hu, J., “Image fusion with guided filtering,” Image Processing, IEEE Transactions on 22, 2013, 2864–2875.
- Zhou, Z., Wang, B., Li, S. and Dong, M., “Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters,” Information Fusion, 30, 2016, 15–26.
- http://www.imagenet.org/index
- http://www.metapix.de/toolbox.htm
- Multi focused Image Fusion using Fast Adaptive Bilateral Filter.
Authors
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, Madurai –625701., IN
Source
International Journal of Advanced Networking and Applications, Vol 14, No 3 (2022), Pagination: 5477-5481Abstract
This paper presents Fast Adaptive Bilateral Filter (FABF) for fusion of Multi Focuses images. Multi Focused image fusion is used to combine one or more input image into single composite image, focusing all objects in the given scene. FABF filter sharpens the image without producing under and over shoot by increasing the edge slope. This paper uses this property to decompose the input image into high and low frequency images so that different fusion rules can be used for high and low frequency images to produce good quality composite image. The performance this FABF filter in Multi focused image fusion is compared with Adaptive Bilateral Filter (ABF) using Root Mean Square Error (RMSE), Spatial Frequency (SF) and Mutual Information (MI).Keywords
Adaptive Bilateral Filter, Fast Adaptive Bilateral Filter, Multi Focused image fusion, Root Mean Square Error, Spatial Frequency and Mutual Information.References
- Buyue Zhang & Jan P. Allebach, “Adaptive bilateral filter for sharpness enhancement and noise removal,” IEEE Transactions on Image Processing,vol. 17, no. 5, pp. 664–678, 2008
- Chaudhury, K. N., “Fast and accurate bilateral filtering using Gauss-polynomial decomposition,”Proc. IEEE International Conference on Image Processing, pp. 2005 - 2009, 2015.
- Chaudhury K. N. and Rithwik, K., “Image denoising using optimally weighted bilateral filters: A SURE and fast approach,”Proc. IEEE International Conference on Image Processing, pp. 108-112, 2015.
- Chaudhury, K. N. , Sage, D. and Unser, M. , “Fast O(1) bilateral filtering using trigonometric range kernels,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3376-3382, 2011.
- Durand F., and Dorsey. J., “Fast bilateral filtering for the display of high dynamic-range images,”ACM Transactions on Graphics, vol. 21, no. 3, pp. 257-266, 2002.
- Gan W., Wu X., Wu, W., Yang, X., Ren, C., He, X., and Liu, K., “Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter,”Infrared Physics & Technology 72, 37–51 (2015).
- Ruturaj G. Gavaskar and Kunal N. Chaudhury, “Fast Adaptive Bilateral Filtering”, submitted to IEEE Transaction on Image Processing.
- Haghighat, M., Aghagolzadeh, A., Seyedarabi, H., "A Non-Reference Image Fusion Metric Based on Mutual Information of Image Features," Computers and Electrical Engineering, vol. 37, no. 5, pp. 744-756, Sept. 2011.
- Haghighat, M., Razian, M.A., "Fast-FMI: non-reference image fusion metric," 8th International Conference on Application of Information and Communication Technologies (AICT), pp. 1-3, 2014.
- He K., Sun J., and Tang, X. , “Guided image filtering,”in Proc. Eur. Conf. Comput. Vis., Heraklion, Greece, Sep. 2010, pp. 1–14.
- He K., Sun J., and Tang, X. , “Guided image filtering,”TPAMI, 35(6):1397–1409, 2013.
- Li, S., Kang, X. and Hu, J., “Image fusion with guided filtering,”Image Processing, IEEE Transactions on 22, 2864–2875 (2013).
- Li, S., Yang, B. , Hu, J. , “Performance comparison of different multi-resolution transforms for image Fusion”, Information Fusion, 12 (2), (2011), pp.74–84.
- Liu, Y., Liu, S. , Wang, Z. , “A general framework for image fusion based on multi-scale transform and sparse representation”, Information Fusion, 24 (2015), pp. 147–164.
- Mittal, A., R. Soundararajan, and A. C. Bovik. "Making a Completely Blind Image Quality Analyzer." IEEE Signal Processing Letters. Vol. 22, Number 3, March 2013, pp. 209–212.
- Mittal, A., A. K. Moorthy, and A. C. Bovik. "No-Reference Image Quality Assessment in the Spatial Domain." IEEE Transactions on Image Processing. Vol. 21, Number 12, December 2012, pp. 4695–4708.
- Paris, S., Kornprobst, P., Tumblin, J. and Durand, F. , “Bilateral Filtering: Theory and Applications”, Now Publishers Inc., 2009.
- Paris S. and Durand, F. , “A fast approximation of the bilateral filter using a signal processing approach,” Proc. European Conference on Computer Vision, pp. 568-580, 2006.
- Peter J. Burt and Raymond J. Kolczynski, “Enhanced Image Capture through Fusion”, Proc. IEEE International Conference, pp. 173-182,1993.
- Perona P.,and Malik, J., “Scale-space and edge detection using anisotropic diffusion,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, 1990.
- Porikli, F., “Constant time O(1) bilateral filtering,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
- Sugimoto K. and Kamata, S. I. , “Compressive bilateral filtering,”IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3357-3369, 2015.
- Tomasi C., and Manduchi, R., “Bilateral filtering for gray and color images,”Proc. IEEE International Conference on Computer Vision, pp. 839-846, 1998.
- Qu G., Zhang D., and Yan P., “Information measure for performance of image fusion,”Electron. Lett., vol. 38, no. 7, pp. 313–315,Mar. 2002.
- Yang, Q. , Tan, K. H. and Ahuja, N. , “Real-time O(1) bilateral filtering,”Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 557-564, 2009.
- Zhou, Z., Wang, B., Li, S. and Dong, M., “Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters,”Information Fusion 30, 15–26 (2016).
- Wang ,Z., Bovik, A., Sheikh, H. and Simoncelli, E.,“Image quality assessment: From error visibility to structural similarity,”IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612,Apr. 2004.
- Kesari Guru Vishnu, Kesari Eswar Bhageerath and Asrith Vatsal Pallanti, “A Comparative Analysis of Edge Detection Techniques for Processing of a Video Signal”, Int. J. Advanced Networking and Applications, 13(04), pp. 5029-5036,2022.
- K. Prakash, P. Saravanamoorthi, R. Sathishkumar, M. Parimala, “A Study of Image Processing in Agriculture”, Int. J. Advanced Networking and Applications, 09(01), pp. 3311-3315, 2017.
- http://www.image-net.org/index
- On the Performance of Histogram Equalization Techniques in Enhancement of Proton Density Weighted Magnetic Resonance Images
Authors
1 Department of Mechatronics Engineering, KAMARAJ College of Engineering and Technology, K. Vellakulam, IN
Source
International Journal of Advanced Networking and Applications, Vol 15, No 3 (2023), Pagination: 5940– 5945Abstract
Magnetic Resonance Images (MRI) are used by Physician to analyse the body structures to find the diseases & to monitor the treatments. For effective analysis, they should consist of all relevant information in a better visualization format. However, MRI images suffer from poor dynamic range which affects the visible quality due to low contrast. Medical Image enhancement is a powerful tool to increase the perception of information to provide better diagnosis. In this study, different histogram equalization techniques like Global Histogram Equalization (GHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), Recursive Mean Separate Histogram Equalization (RMSHE), Brightness Preserving Dynamic Histogram Equalization (BPDHE), Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE)and Contrast-limited Adaptive Histogram Equalization (CLAHE) are applied to proton density weighted magnetic resonance image to enhance the contrast and their performance is compared in terms Discrete Entropy (DE), Measure of Enhancement (EME), Average Brightness (AB) and Pixel Distance (PD). Based on the performance metrics, the best histogram equalization technique in enhancing the contrast of PD weighted MRI images is determined.Keywords
Average Brightness, Contrast Enhancement, Discrete Entropy, Histogram Equalization, Magnetic Resonance Images, Measure of Enhancement and Pixel Distance.References
- David Pickens, “Handbook of Medical Imaging”, Volume 1. Physics and Psychophysics, https://doi.org/10.1117/3.832716.ch6.
- https://www.imaios.com/en/e-Courses/e-MRI/MRIsignal- contrast/Signal-weighting
- https://radiopaedia.org/articles/mri-sequences- overview
- Gonzalez R.C., Woods R.E., “Digital Image processing”, second ed. Prentice Hall 2002.
- Y. T. Kim, “Contrast Enhancement Using Brightness Preserving Bi-Histogram Equation”, IEEE Transactions on Consumer Electronics, vol. 43, no. 1, (1997) February, pp. 1-8.
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- S.D. Chen and A. R. Ramli, “Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation”, IEEE Transactions on Consumer Electronics, vol. 49, no. 4, (2003) November, pp. 1301-1309.
- H. Ibrahim and N. S. Pik Kong, “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement”, IEEE Transactions on Consumer Electronics, vol. 53, no. 4, (2007) November, pp. 1752- 1758.
- D. Sheet, H. Garud, A. Suveer, M. Mahadevappa and J. Chatterjee, “Brightness Preserving Dynamic Fuzzy Histogram Equalization”, IEEE Transactions on Consumer Electronics, vol. 56, no. 4, (2010) November, pp. 2475-2480.
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