A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
INFORMATION SATURATION IN MULTISPECTRAL PIXEL LEVEL IMAGE FUSIONIJCI JOURNAL
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more informative as well as perceptible to human eye. Multispectral image fusion is the
process of combining images from different spectral bands that are optically acquired. In this paper, we
used a pixel-level image fusion based on principal component analysis that combines satellite images of the
same scene from seven different spectral bands. The purpose of using principal component analysis
technique is that it is best method for Grayscale image fusion and gives better results. The main aim of
PCA technique is to reduce a large set of variables into a small set which still contains most of the
information that was present in the large set. The paper compares different parameters namely, entropy,
standard deviation, correlation coefficient etc. for different number of images fused from two to seven.
Finally, the paper shows that the information content in an image gets saturated after fusing four images.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSIONijistjournal
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming to produce quality images. Image Fusion is to integrate complementary and redundant information from multiple images of the same scene to create a single composite image that contains all the important features of the original images. The resulting fused image will thus be more suitable for human and machine perception or for further image processing tasks. The existing fusion techniques based on either direct operation on pixels or segments fail to produce fused images of the required quality and are mostly application based. The existing segmentation algorithms become complicated and time consuming when multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to produce multiple slices of the source and reference images based on various combination of gray scale and can dynamically fused depending on the application. The proposed technique is adopted and analyzed for fusion of multiple images. The technique is robust in the sense that there will be no loss in information due to the property of Self Organizing Feature Maps; noise removal in the source images done during processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities.
INFORMATION SATURATION IN MULTISPECTRAL PIXEL LEVEL IMAGE FUSIONIJCI JOURNAL
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more informative as well as perceptible to human eye. Multispectral image fusion is the
process of combining images from different spectral bands that are optically acquired. In this paper, we
used a pixel-level image fusion based on principal component analysis that combines satellite images of the
same scene from seven different spectral bands. The purpose of using principal component analysis
technique is that it is best method for Grayscale image fusion and gives better results. The main aim of
PCA technique is to reduce a large set of variables into a small set which still contains most of the
information that was present in the large set. The paper compares different parameters namely, entropy,
standard deviation, correlation coefficient etc. for different number of images fused from two to seven.
Finally, the paper shows that the information content in an image gets saturated after fusing four images.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An ensemble classification algorithm for hyperspectral imagessipij
Hyperspectral image analysis has been used for many purposes in environmental monitoring, remote
sensing, vegetation research and also for land cover classification. A hyperspectral image consists of many
layers in which each layer represents a specific wavelength. The layers stack on top of one another making
a cube-like image for entire spectrum. This work aims to classify the hyperspectral images and to produce
a thematic map accurately. Spatial information of hyperspectral images is collected by applying
morphological profile and local binary pattern. Support vector machine is an efficient classification
algorithm for classifying the hyperspectral images. Genetic algorithm is used to obtain the best feature
subjected for classification. Selected features are classified for obtaining the classes and to produce a
thematic map. Experiment is carried out with AVIRIS Indian Pines and ROSIS Pavia University. Proposed
method produces accuracy as 93% for Indian Pines and 92% for Pavia University.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
QUALITY ASSESSMENT OF PIXEL-LEVEL IMAGE FUSION USING FUZZY LOGICijsc
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to
produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the
quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual
information measure ( MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported
methods, wavelet transform based image fusion and weighted average discrete wavelet transform based
image fusion using genetic algorithm.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Issues in Image Registration and Image similarity based on mutual informationDarshana Mistry
This is my 2nd Doctorate progresses committee presentation in image registration which is explained how do you find image similarity based on Entropy and mutual information
Analysis of Multi-focus Image Fusion Method Based on Laplacian PyramidRajyalakshmi Reddy
This paper presented a simple and efficient algorithm
for multi-focus image fusion, which used a
multiresolution signal decomposition scheme called
Laplacian pyramid method. The principle of Laplacian
pyramid transform is introduced, and based on it the
fusion strategy is described in detail. By analyzing the
experimental results, it showed that this method has
good performance, and the quality of the fused image is
better than the results of other methods
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
A novel approach to Image Fusion using combination of Wavelet Transform and C...IJSRD
Panchromatic furthermore multi-spectral image fusion outstands common methods of high-resolution color image amalgamation. In digital image reconstruction, image fusion is standout pre-processing step that aims increasing hotspot image quality to extricate all suitable information from source images ruining inconsistencies or artifacts. Around the different strategies available for image fusion, Wavelet and Curvelet based algorithms are mostly preferred. Wavelet transform is useful for point singularities while Curvelet transform, as the name describes, is more useful for the analysis of images having curved shape edges. This paper reveals a study of development in the field of image fusion.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...sipij
The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comparison of Segmentation Algorithms and Estimation of Optimal Segmentation ...Pinaki Ranjan Sarkar
Recent advancement in sensor technology allows very high spatial resolution along with multiple spectral bands. There are many studies, which highlight that Object Based Image Analysis(OBIA) is more accurate than pixel-based classification for high resolution(< 2m) imagery. Image segmentation is a crucial step for OBIA and it is a very formidable task to estimate optimal parameters for segmentation as it does not have any unique solution. In this paper, we have studied different segmentation algorithms (both mono-scale and multi-scale) for different terrain categories and showed how the segmented output depends on upon various parameters. Later, we have introduced a novel method to estimate optimal segmentation parameters. The main objectives of this study are to highlight the effectiveness of presently available segmentation techniques on very high-resolution satellite data and to automate segmentation process. Pre-estimation of segmentation parameter is more practical and efficient in OBIA. Assessment of segmentation algorithms and estimation of segmentation parameters are examined based on the very high-resolution multi-spectral WorldView-3(0.3m, PAN sharpened) data.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Issues in Image Registration and Image similarity based on mutual informationDarshana Mistry
This is my 2nd Doctorate progresses committee presentation in image registration which is explained how do you find image similarity based on Entropy and mutual information
Analysis of Multi-focus Image Fusion Method Based on Laplacian PyramidRajyalakshmi Reddy
This paper presented a simple and efficient algorithm
for multi-focus image fusion, which used a
multiresolution signal decomposition scheme called
Laplacian pyramid method. The principle of Laplacian
pyramid transform is introduced, and based on it the
fusion strategy is described in detail. By analyzing the
experimental results, it showed that this method has
good performance, and the quality of the fused image is
better than the results of other methods
An Improved Way of Segmentation and Classification of Remote Sensing Images U...ijsrd.com
The Ultimate significance of Images lies in processing the digital image which stems from two principal application areas: Advances of pictorial information for human interpretation; and dispensation of image data for storage, communication, and illustration for self-sufficient machine perception. The objective of this research work is to define the meaning and possibility of image segmentation based on remote sensing images which are successively classified with statistical measures. In this paper kernel induced Possiblistic C-means clustering algorithm has been implemented for classifying remote sensing image data with image features. As a final point of the proposed work is to point out that this algorithm works well for segmenting and classifying the image with better accuracy with statistical metrices.
A novel approach to Image Fusion using combination of Wavelet Transform and C...IJSRD
Panchromatic furthermore multi-spectral image fusion outstands common methods of high-resolution color image amalgamation. In digital image reconstruction, image fusion is standout pre-processing step that aims increasing hotspot image quality to extricate all suitable information from source images ruining inconsistencies or artifacts. Around the different strategies available for image fusion, Wavelet and Curvelet based algorithms are mostly preferred. Wavelet transform is useful for point singularities while Curvelet transform, as the name describes, is more useful for the analysis of images having curved shape edges. This paper reveals a study of development in the field of image fusion.
RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...sipij
The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image Segmentation Using Pairwise Correlation ClusteringIJERA Editor
A pairwise hypergraph based image segmentation framework is formulated in a supervised manner for various images. The image segmentation is to infer the edge label over the pairwise hypergraph by maximizing the normalized cuts. Correlation clustering which is a graph partitioning algorithm, was shown to be effective in a number of applications such as identification, clustering of documents and image segmentation.The partitioning result is derived from a algorithm to partition a pairwise graph into disjoint groups of coherent nodes. In the pairwise correlation clustering, the pairwise graph which is used in the correlation clustering is generalized to a superpixel graph where a node corresponds to a superpixel and a link between adjacent superpixels corresponds to an edge. This pairwise correlation clustering also considers the feature vector which extracts several visual cues from a superpixel, including brightness, color, texture, and shape. Significant progress in clustering has been achieved by algorithms that are based on pairwise affinities between the datasets. The experimental results are shown by calculating the typical cut and inference in an undirected graphical model and datasets.
Semi-Supervised Method of Multiple Object Segmentation with a Region Labeling...sipij
Efficient and efficient multiple object segmentation is an important task in computer vision and object recognition. In this work; we address a method to effectively discover a user’s concept when multiple objects of interest are involved in content based image retrieval. The proposed method incorporate a framework for multiple object retrieval using semi-supervised method of similar region merging and flood fill which models the spatial and appearance relations among image pixels. To improve the effectiveness of similarity based region merging we propose a new similarity based object retrieval. The users only need to roughly indicate the after which steps desired objects contour is obtained during the automatic merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique and objects detection using region labeling and flood fill. A region R is merged with its adjacent regions Q if Q has highest similarity with Q (using Bhattacharyya descriptor) among all Q’s adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 12 object classes (224 images total) show promising results.
A Pattern Classification Based approach for Blur Classificationijeei-iaes
Blur type identification is one of the most crucial step of image restoration. In case of blind restoration of such images, it is generally assumed that the blur type is known prior to restoration of such images. However, it is not practical in real applications. So, blur type identification is extremely desirable before application of blind restoration technique to restore a blurred image. An approach to categorize blur in three classes namely motion, defocus, and combined blur is presented in this paper. Curvelet transform based energy features are utilized as features of blur patterns and a neural network is designed for classification. The simulation results show preciseness of proposed approach.
There exists a plethora of algorithms to perform image segmentation and there are several issues related to
execution time of these algorithms. Image Segmentation is nothing but label relabeling problem under
probability framework. To estimate the label configuration, an iterative optimization scheme is
implemented to alternately carry out the maximum a posteriori (MAP) estimation and the maximum
likelihood (ML) estimations. In this paper this technique is modified in such a way so that it performs
segmentation within stipulated time period. The extensive experiments shows that the results obtained are
comparable with existing algorithms. This algorithm performs faster execution than the existing algorithm
to give automatic segmentation without any human intervention. Its result match image edges very closer to
human perception.
Multitude Regional Texture Extraction for Efficient Medical Image Segmentationinventionjournals
Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted brain segmentation and disease diagnosis applications.First for ultra sound we present region based segmentation.Homogeneous regions depends on image granularity features. Second a local threshold based multitude texture regional seed segmentation for medical image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less medical metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
A Survey on Image Segmentation and its Applications in Image Processing IJEEE
As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper.
Call for Research Articles - 5th International Conference on Artificial Intel...ijistjournal
5th International Conference on Artificial Intelligence and Machine Learning (CAIML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and Machine Learning. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Machine Learning in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Computer Science, Engineering and Applications.
Online Paper Submission - International Journal of Information Sciences and T...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Call for Research Articles - 4th International Conference on NLP & Data Minin...ijistjournal
4th International Conference on NLP & Data Mining (NLDM 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing and Data Mining.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
Research Article Submission - International Journal of Information Sciences a...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Call for Papers - International Journal of Information Sciences and Technique...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Implementation of Radon Transformation for Electrical Impedance Tomography (EIT)ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Online Paper Submission - 6th International Conference on Machine Learning & ...ijistjournal
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
Submit Your Research Articles - International Journal of Information Sciences...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
BER Performance of MPSK and MQAM in 2x2 Almouti MIMO Systemsijistjournal
Almouti published the error performance of the 2x2 space-time transmit diversity scheme using BPSK. One of the key techniques employed for correcting such errors is the Quadrature amplitude modulation (QAM) because of its efficiency in power and bandwidth.. In this paper we explore the error performance of the 2x2 MIMO system using the Almouti space-time codes for higher order PSK and M-ary QAM. MATLAB was used to simulate the system; assuming slow fading Rayleigh channel and additive white Gaussian noise. The simulated performance curves were compared and evaluated with theoretical curves obtained using BER tool on the MATLAB by setting parameters for random generators. The results shows that the technique used do find a place in correcting error rates of QAM system of higher modulation schemes. The model can equally be used not only for the criteria of adaptive modulation but for a platform to design other modulation systems as well.
Online Paper Submission - International Journal of Information Sciences and T...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Call for Papers - International Journal of Information Sciences and Technique...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
International Journal of Information Sciences and Techniques (IJIST)ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
BRAIN TUMOR MRIIMAGE CLASSIFICATION WITH FEATURE SELECTION AND EXTRACTION USI...ijistjournal
Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.
Research Article Submission - International Journal of Information Sciences a...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
A MEDIAN BASED DIRECTIONAL CASCADED WITH MASK FILTER FOR REMOVAL OF RVINijistjournal
In this paper A Median Based Directional Cascaded with Mask (MBDCM) filter has been proposed, which is based on three different sized cascaded filtering windows. The differences between the current pixel and its neighbors aligned with four main directions are considered for impulse detection. A direction index is used for each edge aligned with a given direction. Minimum of these four direction indexes is used for impulse detection under each masking window. Depending on the minimum direction indexes among these three windows new value to substitute the noisy pixel is calculated. Extensive simulations showed that the MBDCM filter provides good performances of suppressing impulses from both gray level and colored benchmarked images corrupted with low noise level as well as for highly dense impulses. MBDCM filter gives better results than MDWCMM filter in suppressing impulses from highly corrupted digital images.
DECEPTION AND RACISM IN THE TUSKEGEE SYPHILIS STUDYijistjournal
During the twentieth century (1932-1972), white physicians representing the United States government
conducted a human experiment known as the Tuskegee Syphilis Study on black syphilis patients in Macon
County, Alabama. The creators of the study, who supported the idea of black inferiority and the concept
that black people’s bodies functioned differently from white people’s, observed the effects of a disease
called syphilis on untreated black patients in order to collect data for further research on syphilis. Black
individuals involved with the study believed that they were receiving treatment, although in truth,
treatments for syphilis were purposely held back from them. Not only this, but fluids from their bodies, such
as blood and spinal fluid, were extracted to serve as research material without their awareness of the
purpose of the collection. The physicians justified their approach by positioning it as mere observation,
asserting that they were not actively intervening with the patients participating in the experiment. However,
despite their claims of passivity, these white physicians engaged in various morally improper actions,
including deceit, which ultimately resulted in the deaths of numerous black patients who might have had a
chance at survival.
Online Paper Submission - International Journal of Information Sciences and T...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
Call for Papers - International Journal of Information Sciences and Technique...ijistjournal
The International Journal of Information Science & Techniques (IJIST) focuses on information systems science and technology coercing multitude applications of information systems in business administration, social science, biosciences, and humanities education, library sciences management, depiction of data and structural illustration, big data analytics, information economics in real engineering and scientific problems.
This journal provides a forum that impacts the development of engineering, education, technology management, information theories and application validation. It also acts as a path to exchange novel and innovative ideas about Information systems science and technology.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
ADOPTING AND IMPLEMENTATION OF SELF ORGANIZING FEATURE MAP FOR IMAGE FUSION
1. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
DOI : 10.5121/ijist.2013.3104 43
ADOPTING AND IMPLEMENTATION OF
SELF ORGANIZING FEATURE MAP FOR
IMAGE FUSION
Dr.AnnaSaroVijendran1
and G.Paramasivam2
1
Director, SNR Institute of Computer Applications, SNR Sons College, Coimbatore,
Tamilnadu, INDIA.
saroviji@rediffmail.com
2
Asst.Professor, Department of Computer Applications, SNRSonsCollege,
CoimbatoreTamilnadu,INDIA.
pvgparam@yahoo.co.in
ABSTRACT
A different image fusion algorithm based on self organizing feature map is proposed in this paper, aiming
to produce quality images. Image Fusion is to integrate complementary and redundant information from
multiple images of the same scene to create a single composite image that contains all the important
features of the original images. The resulting fused image will thus be more suitable for human and
machine perception or for further image processing tasks. The existing fusion techniques based on either
direct operation on pixels or segments fail to produce fused images of the required quality and are mostly
application based. The existing segmentation algorithms become complicated and time consuming when
multiple images are to be fused. A new method of segmenting and fusion of gray scale images adopting Self
organizing Feature Maps(SOM) is proposed in this paper. The Self Organizing Feature Maps is adopted to
produce multiple slices of the source and reference images based on various combination of gray scale and
can dynamically fused depending on the application. The proposed technique is adopted and analyzed for
fusion of multiple images. The technique is robust in the sense that there will be no loss in information due
to the property of Self Organizing Feature Maps; noise removal in the source images done during
processing stage and fusion of multiple images is dynamically done to get the desired results. Experimental
results demonstrate that, for the quality multifocus image fusion, the proposed method performs better than
some popular image fusion methods in both subjective and objective qualities.
KEYWORDS
Image Fusion, Image Segmentation,Self Organizing Feature Maps,Code Book Generation,Multifocus
Images, Gray Scale Images
1.INTRODUCTION
Nowadays, image fusion has become an important subarea of image processing. For one object or
scene, multiple images can be taken from one or multiple sensors.These images usually contain
complementary information. Image fusion is the process of combining information from two or
more images of a scene into a single composite image that is more informative and is more
suitable for visual perception or computer processing. The objectivein image fusion is to reduce
uncertainty and minimize redundancy in the output while maximizing relevant information
particular to an application or task. Image fusion has become a common term used within medical
diagnostics and treatment. Given the same set of input images, different fused images may be
2. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
44
created depending on the specific application and what is considered relevant information. There
are several benefits in using image fusion like wider spatial and temporal coverage, decreased
uncertainty, improved reliability and increased robustness of system performance.Often a single
sensor cannot produce a complete representation of a scene. Successful image fusion significantly
reduces the amount of data to be viewed or processed without significantly reducing the amount
of relevant information.
Image fusion algorithms can be categorized into pixel, feature and symbolic levels. Pixel-level
algorithms work either in the spatial domain [1, 2] or in the transform domain [3, 4 and 5].
Although pixel-level fusion is a local operation, transform domain algorithms create the fused
image globally. By changing a single coefficient in the transformed fused image, all imagevalues
in the spatial domain will change. As a result, in the process of enhancing properties in some
image areas, undesirable artifacts may be created in other image areas. Algorithms that work in
the spatial domain have the ability to focus on desired image areas, limiting change in other
areas.Multiresolution analysis is a popular method in pixel-level fusion. Burt [6] and Kolczynski
[7] used filters with increasing spatial extent to generate a sequence of images from each image,
separating information observed at different resolutions. Then at each position in the transform
image, the value in the pyramid showing the highest saliency was taken. An inverse transform of
the composite image was used to create the fused image. In a similar manner, various wavelet
transforms can be used to fuse images. The discrete wavelet transform (DWT) has been used in
many applications to fuse images [4]. The dual-tree complexwavelet transforms (DT-CWT), first
proposed by Kingsbury[8], was improved by Nikolov [9] and Lewis [10] to outperform most
other gray-scale image fusion methods.
Feature-based algorithms typically segment the images into regions and fuse the regions using
their various properties [10–12]. Feature-based algorithms are usually less sensitive to signal-
level noise [13]. Toet [3] first decomposed each input image into aset of perceptually
relevant patterns. The patterns were then combined to create a composite image containing all
relevant patterns. A mid-level fusion algorithm was developed by Piella [12, 15] where the
images are first segmented and the obtained regions are then used to guide the multiresolution
analysis.
Recently methodshave been proposed to fuse multifocus source images using the divided blocks
or segmented regions instead of single pixels [16, 17, and 18]. All the segmented region-based
methods are strongly dependent on the segmentation algorithm. Unfortunately, the segmentation
algorithms, which are of vital importance to fusion quality, are complicated and time-consuming.
The common transform approaches for fusion of mutifocus images include the discrete wavelet
transform (DWT) [19],curvelet transform [20] and nonsubsamplingcontourlet transform (NSCT)
[21]. Recently, a new multifocus image fusion and restoration algorithm based on the sparse
representation hasbeen proposed byYang and Li [22]. A new multifocus image fusion method
based on homogeneity similarity and focused regions detection has been proposed during the
year 2011 by Huafeng Li , Yi Chai , Hongpeng Yin and Guoquan Liu [23] .
Most of the traditional image fusion methods are based on the assumption that the source images
are noise free, and they can produce good performance when the assumption is satisfied. For the
traditional noisy image fusion methods, they usually denoise the source images, and then the
denoised images are fused. The multifocus image fusion and restoration algorithm proposed by
Yang and Li [22] performs well with both noisy and noise free images, and outperforms
traditional fusion methods in terms of fusion quality and noise reductionin the fused output.
However, this scheme is complicated and time-consuming especially when the source images are
noise free. The image fusion algorithm based on homogeneity similarity proposed by HuafengLi ,
Yi Chai , Hongpeng Yin and Guoquan Liu [23] aims at solving the fusion problem of clean and
3. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
45
noisy multifocus images. Further in any region based fusion algorithm, the fusion results are
affected by the performance of segmentation algorithm. The various segmentation algorithms are
based on thresholding and clustering but the partition criteria used by these algorithms often
generates undesired segmented regions.
In order to overcome the above said problems a new method for segmentation using Self-
organizing Feature Maps which consequently helps in fusion of images dynamically to the
desired degree of information retrieval depending on the application has been proposed in this
paper. The proposed algorithm is compatible for any type of image either noisy or clean. The
method is simple and since mapping of image is carried out by Self-organizing Feature Maps all
the information in the images will be preserved.The images used in image fusion should already
be registered. A novel image fusion algorithm based on self organizing feature map is proposed in
this paper.
The outline of this paper is as follows: In Section 2, the Self-organizing Feature Maps is briefly
introduced. Section 3 describes the algorithm for Code Book generation using Self Organizing
Feature Maps. Section 4 describes the proposed method of Fusion. Section 5 details the
Experimental Analysis and Section 6 gives the conclusion of this paper.
2. SELF-ORGANIZING FEATURE MAP
Self-organizing Feature Map (SOM) is a special class of Artificial Neural Networkbased on
competitive learning. It is an ingenious Artificial Neural Network built around a one or two-
dimensional lattice of neurons for capturing the important features contained in the input. The
Kohonen technique creates a network that stores information in such a way that any topological
relationships within the training set are maintained. In addition to clustering the data into distinct
regions, regions of similar properties are put into good use by the Kohonen maps.
The primary benefit is that the network learns autonomously without the requirement that the
system be well defined. System does not stop learning but instead continues to adapt to changing
inputs. This plasticity allows it to adapt as the environment changes.A particular advantage over
other artificial neural networks is that the system appears well suited to parallel computation.
Indeed the only global knowledge required by each neuron is the current input to the network
and the position within the array of the neuron which produced the maximum output
Kohonen networks are grid of computing elements, which allows identifying the immediate
neighbours of a unit. This is very important, since during learning, the weights of computing units
and their neighbours are updated. The objective of such a learning approach is that neighbouring
units learn to react to closely related signals.
A Self-organizing Feature Mapdoes not need a target output to be specified unlike many other
types of network. Instead, where the node weights match the input vector, that area of the lattice
is selectively optimized to more closely resemble the data for the class, the input vector is a
member. From an initial distribution of random weights, and over many iterations, the Self-
organizing Feature Map eventually settles into a map of stable zones. Each zone is effectively a
feature classifier. The output is a type of feature map of the input space. In the trained network,
the blocks of similar values represent the individual zones. Any new, previously unseen input
vectors presented to the network will stimulate nodes in the zone with similar weight vectors.
Training occurs in several steps and over many iterations.
Each node's weights are initialized. A vector is chosen at random from the set of training data and
presented to the lattice. Every node is examined to calculate which one's weights are most like the
input vector. The winning node is commonly known as the Best Matching Unit (BMU). The
4. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
46
radius of the neighbourhood of the Best Matching Unit is now calculated. This is a value that
starts large, typically set to the 'radius' of the lattice, but diminishes each time-step. Any nodes
found within this radius are deemed to be inside the Best Matching Unit‘s neighbourhood. Each
neighbouring node's weights are adjusted to make them more like the input vector. The closer a
node is to the Best Matching Unit; the more its weights get altered. The procedure is repeated for
all input vectors for number of iterations. Prior to training, each node's weights must be
initialized. Typically these will be set to small-standardized random values. To determine the
Best Matching Unit, one method is to iterate through all the nodes and calculate the Euclidean
distance between each node's weight vector and the current input vector. The node with a weight
vector closest to the input vector is tagged as the Best Matching Unit. After the Best Matching
Unit has been determined, the next step is to calculate which of the other nodes are within the
Best Matching Unit's neighbourhood. All these nodes will have their weight vectors altered in the
next step. A unique feature of the Kohonen learning algorithm is that the area of the
neighbourhoodshrinks over time to the size of just one node.
After knowing the radius, iterations are carried out through all the nodes in the lattice to
determine if they lay within the radius or not. If a node is found to be within the neighbourhood
then its weight vector is adjusted. Every node within the Best Matching Unit‘s neighbourhood
(including the Best Matching Unit) has its weight vector adjusted.
In Self-organizing Feature Map, the neurons are placed at the lattice nodes; the lattice may take
different shapes: rectangular grid, hexagonal, even random topology .
Figure 1.Self Organizing Feature Map Architecture
The neurons become selectivity tuned on various input patterns in the course of competitive
learning process. The locations of the neurons (i.e. the winning neurons) so tuned, tend to become
ordered with respect to each other in such a way that a meaningful coordinate system for different
input features to be created over the lattice.
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47
SOM Neural Network Training GUI:
3. CODE BOOK GENERATION USING SELF-ORGANIZG
FEATURE MAP
Given a two-dimensional input image pattern to be mapped onto a two dimensional spatial
organization of neurons located at different positions (i, j)’s on a rectangular lattice of size nxn.
Thus, for a set of nxn points on the two-dimensional plane, there would be n2
neurons Nij:1 ≤ i,j ≤
n, and for each neuron Nij there is an associated weight vector denoted as Wij. In Self-organizing
Feature Map, the neuron with minimum distance between its weight vector Wij and the input
vector X is the winner neuron (k,l), and it is identified using the following equation.
||X - W kl|| = min [min ||X - W ij ||]
1≤i≤ n i≤j≤ n (1)
After the position of the (i,j)th winner neuron is located in the two-dimensional plane, the winner
neuron and its neighbourhood neurons are adjusted using Self- organizing Feature Map learning
rule as:
Wij(t+1) = Wij(t) + α || X -Wij(t) || (2)
Where, α is the Kohonen’s learning rate to control the stability and the rate of convergence. The
winner weight vector reaches equilibrium when Wij(t+1)=Wij(t). The neighbourhood of neuron
Nij is chosen arbitrary. It can be a square or a circular zone around Nij of arbitrary chosen radius.
Algorithm
1: The image A (i,j) of size 2 N
x 2 N
isdivided into blocks, each of them of size
2 n
x 2n
pixels, n < N.
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48
2: A Self-organizing Feature Map Network is created with a codebook consisting of M neurons
(mi: i=1,2,.....M).The total M neurons are arranged in a hexagonal lattice, and for each
neuron there is an associated weight vector Wi = [wi1 wi2.......wi2
2n
].
3: The weights vectors are initiated for all the neurons in the lattice with small random values.
4: The learning input patterns (image blocks) are applied to the network. The Kohonen’s
competitive learning process identifies the winning neurons that best match the input blocks.
The best matching criterion is the minimum Euclidean distance between the vectors. Hence,
the mapping process Q that identifies the neuron that best matches the input block X is
determined by applying the following condition.
Q(X) = argi min ||X - Wi|| i = 1,2,……..M (3)
5: At equilibrium, there are m winner neurons per block or m codewords per block. Hence, the
whole image is represented using m number of codewords.
6: The indices of the obtained codewords are stored. The set of indices of all winner neurons
along with the codebook are stored.
7: The reconstructed image blocks of same size as the original ones, will be restored from the
indices of the codewords.
4. PROPOSED METHOD OF FUSION
Let us consider two pre-registered grayscale 8 bitimages A and B formed of the same scene or
object.
The first imageA is decomposed into sub-images and given as input to the Self-organizing
Feature Map Neural Network. In order to preserve all the gray values of the image, the codebook
size for compressing an 8 bit image is chosen to be the maximum possible number of gray levels,
say 256. Since the weight values after training has to represent the input level gray levels, random
values ranging from 0 to 255 are assigned as initial weights.When sub-images of size say 4 x 4 is
considered as the input vector, then there will be 16 nodes in the input layer and the Kohonen
layer consists of 256 nodes arranged in a 16 x 16 array. The input layer takes as input the gray-
level values from all the 16 pixels of the gray-level block. The weights assigned between node j
of the Kohonen layer and the input layer represents the weight matrix. For all the 256 nodes we
have Wji for j = 0,1,…,255 and i = 0,1…15. Once the weights are initialized randomly
network is ready for training.
The image block vectors are mapped with the weight vectors. The neighborhood is initially
chosen; say 5 x 5and then reduced gradually to find the best matching node. The Self-organizing
Feature Map generates the codebook according to the weight updation. The set of indices of all
the winner neurons for the blocks along with the code book are stored for retrieval.
The image A is retrieved by generating the weight vectors of each neuron from the index values,
which gives the pixel value of the image. For each index value the connected neuron is found.
The weight vector of that neuron to the input layer neuron is generated. The values of the neuron
weights are the gray level for the block. The gray level value thus obtained is displayed as pixels.
Thus we get the image A back in its original form.
Now the Image B is given as input to the Neural Network. Since the features in Images A and B
are the same, when B is given as input to the trained Network the code book for the Image B will
be generated in minimum simulation time without loss in information. Also since the images are
registeredthe index values which represent the position of pixels will be the same in the two
7. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
49
images. For the same index value the value of the neuron weights of both the images are
compared and the higher value is selected. The procedure is repeated for all the indices until a
weight vector of optimal strength is generated. The image retrieved from this optimal weight
vector is the fused image which will represent all the gray values in its optimal values.The
procedure can be repeated in various combinations with multiple images until the desired result is
achieved.
5. EXPERIMENTAL ANALYSIS AND RESULTS
The Experimental analysis of the proposed algorithm has been performed using large numbers of
images having different content for simulations and different objective parameters discussed in
the paper. In order to evaluate the fusion performance, the first experiment is performed on one
set of perfectly registered multifocus source images. The dataset consists of multifocus images.
The use of different pairs of multifocus images of different scenes including all categories of text,
text+object and only objects allows evaluating the proposed algorithm in true sense. The
proposed algorithm is simulated in Matlab7. The proposed algorithm is evaluated based on the
quality of the fused image obtained. The robustness of the proposed algorithm that is to obtain
consistent good quality fused image with different categories of images like standard images,
medical images, satellite images has beenevaluated. The average computational time to generate
final fused image from the source image size of 128 x 128 using the proposed algorithm has been
calculated and the average computational time is 96 seconds. The quality of the fused image with
the source images has been compared in terms of RMSE and PSNR values.The experimental
results obtained for fusion of grayscaleimages adopting the proposed algorithm are shown in
Table1. The source multifocus images and the fused images of different types are shown in
Figures 1 to 4.The histogram and image difference for lena and bacteria images are shown in
Figure 5 and 6 respectively.
Table 1.a
Images
RMSE
Image A Image B
Fused Image
Lena 6.2856 6.0162 3.3205
Bacteria 6.8548 6.421 6.227
Satellite map 4.0043 5.325 3.8171
MRI -Head 4.3765 4.9824 1.5163
Table 1.b
Images
PSNR
Image A Image B
Fused Image
Lena 30.381 31.701 38.0481
Bacteria 28.194 27.989 32.5841
Satellite map 32.077 31.582 33.2772
MRI -Head 30.787 33.901 45.3924
8. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
50
Figure 1.a Figure 1.b Figure 1.c
Figure 1.Lena
Figure 2.a Figure 2.b Figure 2.c
Figure 2.Bacteria
Figure 2.a Figure 2.b Figure 2.c
Figure 2.Bacteria
Figure 4.a Figure 4.b Figure 4.c
Figure 4.MRI –Head
Images (a) and (b ) are the multi focused images Image (c) is the fused version of (a)and(b)
9. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
Figure 5. Histogram f
Figure 6. The Difference images between fused image and the source image :(g) Difference
between (a)and (c), (h) Difference between (b) and (c), (i) Difference between (d) and (f), (j)
Difference between (e)and (f).
6. CONCLUSIONS
In this Paper a simple method of fusion ofImages has been proposed. The advantage of Self
organizing Feature Map is that after training, the weight vectors not only represents the image
block cluster centroids but also preserves two main f
the input vectors are mapped to that of topologically neighbouring neurons in the code book.
Also the distribution of weight vectors of the neurons reflects the distribution of the weight
vectors in the input space. Hence their will not be any loss of information in the process
proposed method is dynamic in the sense that depending on the application the optimal weight
vectors are generated and also redundant values as well as noise can be ignored in this
resulting in lesser simulation time.The method can be extended to colour images also.
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SPOT panchromatic images, Information Fusion 3
International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
Figure 5. Histogram for lena and bacteria images
Figure 6. The Difference images between fused image and the source image :(g) Difference
between (a)and (c), (h) Difference between (b) and (c), (i) Difference between (d) and (f), (j)
In this Paper a simple method of fusion ofImages has been proposed. The advantage of Self
organizing Feature Map is that after training, the weight vectors not only represents the image
block cluster centroids but also preserves two main features. Topologically neighbour blocks in
the input vectors are mapped to that of topologically neighbouring neurons in the code book.
Also the distribution of weight vectors of the neurons reflects the distribution of the weight
Hence their will not be any loss of information in the process
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vectors are generated and also redundant values as well as noise can be ignored in this
resulting in lesser simulation time.The method can be extended to colour images also.
S. Li, J.T.Kwok, Y.Wang, Using the discrete wavelet frame transform to mergeLandsat TM and
SPOT panchromatic images, Information Fusion 3 (2002) 17–23.
International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
51
Figure 6. The Difference images between fused image and the source image :(g) Difference
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eatures. Topologically neighbour blocks in
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proposed method is dynamic in the sense that depending on the application the optimal weight
vectors are generated and also redundant values as well as noise can be ignored in this process
resulting in lesser simulation time.The method can be extended to colour images also.
S. Li, J.T.Kwok, Y.Wang, Using the discrete wavelet frame transform to mergeLandsat TM and
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ACKNOWLEDGMENTS
The authors thanks to our Managementof SNR Sons Institutions for allowing us to utilize their
resources for doing our research work. Our sincere thanks to our Principal&Secretary
Dr.H.BalakrishnanM.Com., M.Phil.,Ph.D., for his support and encouragement in our research
work. And my grateful thanks to my guide Dr. Anna SaroVijendran MCA.,M.Phil.,Ph.D.,
Director of MCA., SNR Sons College, Coimbatore-6 for her valuable guidance and giving me a
lot of suggestions & proper solutions for critical situations in our research work.The authors
thank the Associate Editor and reviewers for their encouragement and valued comments, which
helped in improving the quality of the paper.
11. International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.1, January 2013
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AUTHOR’S BIOGRAPHY
Dr. Anna SaroVijendran receivedthe Ph.D. degree in Computer Science from Mother
Teresa Womens University,Tamilnadu, India, in 2009. She has 20 years of experience
in teaching. She is currently working as the Director, MCA in SNR Sons College,
Coimbatore, Tamilnadu, India. She has presented and published many papers in
International and National conferences. She has authored and co-authored more than 30
refereed papers. Herprofessional interests are Image Processing, Image fusion, Data
mining and Artificial Neural Networks.
G.ParamasivamPh.D (PT)Research scholar.He is currently theAsst Professor, SNR
Sons College,Coimbatore, Tamilnadu, India.He has 10 years of teaching experience.His
technical interests include Image Fusion and ArtificialNeural networks.