Sub-windowed laser speckle image velocimetry by fast fourier transform technique
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame- single-exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed to obtain the velocity information with vector map analysis precisely.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This document provides an overview of digital image processing. It discusses what image processing entails, including enhancing images, extracting information, and pattern recognition. It also describes various image processing techniques such as radiometric and geometric correction, image enhancement, classification, and accuracy assessment. Radiometric correction aims to reduce noise from sources like the atmosphere, sensors, and terrain. Geometric correction geometrically registers images. Image enhancement improves interpretability. Classification categorizes pixels. The document outlines both supervised and unsupervised classification methods.
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.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
This document discusses image fusion techniques at different levels of abstraction: pixel level, feature level, and decision level. It describes various fusion methods including numerical (e.g. multiplicative, Brovey), color related (e.g. IHS), statistical (e.g. PCA, Gram Schmidt), and feature level (e.g. Ehlers) techniques. Both qualitative (visual) and quantitative (statistical measures like RMSE, correlation coefficient, entropy) methods to assess fusion quality are outlined. Image fusion has applications in improving classification and displaying sharper resolution images.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
OBIA on Coastal Landform Based on Structure Tensor csandit
This paper presents the OBIA method based on structure tensor to identify complex coastal
landforms. That is, develop Hessian matrix by Gabor filtering and calculate multiscale structure
tensor. Extract edge information of image from the trace of structure tensor and conduct
watershed segment of the image. Then, develop texons and create texton histogram. Finally,
obtain the final results by means of maximum likelihood classification with KL divergence as
the similarity measurement. The study findings show that structure tensor could obtain
multiscale and all-direction information with small data redundancy. Moreover, the method
described in the current paper has high classification accuracy
This document provides an overview of digital image processing. It discusses what image processing entails, including enhancing images, extracting information, and pattern recognition. It also describes various image processing techniques such as radiometric and geometric correction, image enhancement, classification, and accuracy assessment. Radiometric correction aims to reduce noise from sources like the atmosphere, sensors, and terrain. Geometric correction geometrically registers images. Image enhancement improves interpretability. Classification categorizes pixels. The document outlines both supervised and unsupervised classification methods.
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.
Different Image Fusion Techniques –A Critical ReviewIJMER
This document reviews and compares different image fusion techniques, including spatial domain and transform domain methods. Spatial domain techniques like simple averaging and maximum selection are disadvantageous because they can produce spatial distortions and reduce contrast in the fused image. Transform domain methods like discrete wavelet transform (DWT) and principal component analysis (PCA) perform better by preserving more spatial and spectral information. DWT fusion in particular minimizes spectral distortion and improves the signal-to-noise ratio over pixel-based approaches, though it results in lower spatial resolution. Tables in the document provide quantitative comparisons of different techniques using performance measures like peak signal-to-noise ratio, entropy, and normalized cross-correlation.
This document describes a method for pixel-level image fusion using principal component analysis (PCA). PCA is used to transform correlated image pixels into a set of uncorrelated principal components. The first principal component accounts for the most variance in the pixel values. To fuse images, the pixels of the input images are arranged into vectors and subtracted from their mean. PCA is applied to get the eigenvectors corresponding to the largest eigenvalues. The normalized eigenvectors are used to compute a fused image as a weighted sum of the input images. Performance is evaluated using metrics like standard deviation, entropy, cross-entropy, and fusion mutual information, with higher values of these metrics indicating better quality of the fused image.
Single image super resolution with improved wavelet interpolation and iterati...iosrjce
IOSR journal of VLSI and Signal Processing (IOSRJVSP) is a double blind peer reviewed International Journal that publishes articles which contribute new results in all areas of VLSI Design & Signal Processing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced VLSI Design & Signal Processing concepts and establishing new collaborations in these areas.Design and realization of microelectronic systems using VLSI/ULSI technologies require close collaboration among scientists and engineers in the fields of systems architecture, logic and circuit design, chips and wafer fabrication, packaging, testing and systems applications. Generation of specifications, design and verification must be performed at all abstraction levels, including the system, register-transfer, logic, circuit, transistor and process levels.
This document discusses image fusion techniques at different levels of abstraction: pixel level, feature level, and decision level. It describes various fusion methods including numerical (e.g. multiplicative, Brovey), color related (e.g. IHS), statistical (e.g. PCA, Gram Schmidt), and feature level (e.g. Ehlers) techniques. Both qualitative (visual) and quantitative (statistical measures like RMSE, correlation coefficient, entropy) methods to assess fusion quality are outlined. Image fusion has applications in improving classification and displaying sharper resolution images.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
Detection and Classification in Hyperspectral Images using Rate Distortion an...Pioneer Natural Resources
This document summarizes an experiment that compares two methods of bit allocation for compressing hyperspectral imagery using JPEG2000: 1) the traditional high bit rate quantizer approach and 2) the rate distortion optimal (RDO) approach. The experiment shows that both methods perform well at relatively low bit rates, achieving over 96% classification accuracy. However, at very low bit rates, the RDO approach outperforms the high bit rate quantizer approach, achieving 90% accuracy at 0.0375 bpppb compared to less than 90% for the high bit rate method. The RDO approach also achieves lower mean squared error than the high bit rate quantizer approach.
Comparative study on image fusion methods in spatial domainIAEME Publication
This document provides a comparative study of various image fusion methods in the spatial domain. It begins by introducing image fusion and its applications. Section 2 then describes several common fusion algorithms in the spatial domain, including average, select maximum/minimum, Brovey transform, intensity hue saturation (IHS), and principal component analysis (PCA). Section 3 defines image fusion quality measures like entropy, mean squared error, and normalized cross correlation. Section 4 provides a comparative analysis of the spatial domain fusion techniques based on parameters like simplicity, type of resources, and disadvantages. It finds that spatial domain methods provide high spatial resolution but have issues like image blurring and producing less informative outputs. The document concludes that while the best algorithm depends on the problem, spatial
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
This document describes an extended fuzzy c-means (EFCM) clustering algorithm for noisy image segmentation. The algorithm first preprocesses noisy pixels in an image by regenerating their values based on neighboring pixel intensities. It then applies the conventional fuzzy c-means clustering algorithm to segment the image. The EFCM approach is presented as being less sensitive to noise than other clustering algorithms and able to efficiently segment noisy images. The document provides background on image segmentation, fuzzy c-means clustering, types of image noise, and density-based clustering challenges. It also outlines the EFCM methodology and its computational advantages over other robust clustering methods for noisy image segmentation.
Application of Image Retrieval Techniques to Understand Evolving Weatherijsrd.com
Multispectral satellite images provide valuable information to understand the evolution of various weather systems such as tropical cyclones, shifting of intra tropical convergence zone, moments of various troughs etc., accurate prediction and estimation will save live and property. This work will deal with the development of an application which will enable users to search an image from database using either gray level, texture and shape features for meteorological satellite image retrieval .Gray level feature is extracted using histogram method. The Texture feature is extracted using gray level co-occurrence method and wavelet approach. The shape feature vector is extracted using morphological operations. The similarity between query image and database images is calculated using Euclidian distance. The performance of the system is evaluated using precision
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
This lecture is about particle image velocimetry technique. It include discussion about the basic element of PIV setup, image capturing, laser lights, synchronize and correlation analysis.
This document describes a method for segmenting gray scale images using iterative triclass thresholding based on Otsu's method. It begins with applying PCA to reduce the image to a single band. Otsu's method is then used to initially segment the image into foreground, background, and a third region to be further processed. Morphological operations like dilation and erosion are applied for smoothing. The threshold is recalculated and triclass partitioning is repeated iteratively until the target regions are extracted with better accuracy. The method provides low complexity segmentation with better noise removal and object detection performance.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
The document discusses image segmentation techniques. It describes image segmentation as partitioning a digital image into multiple regions based on characteristics like color or texture. Common applications of image segmentation include industrial inspection, optical character recognition, and medical imaging. The techniques discussed are fixed thresholding, iterative thresholding, and fuzzy c-means clustering. Fuzzy c-means clustering is identified as the most suitable for pest image segmentation based on its lower entropy and normalized mutual information values. Simulated annealing is also proposed to improve upon the limitations of fuzzy c-means clustering.
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
This document describes a content-based image retrieval system that uses 2-D discrete wavelet transform with texture features. It proposes using DWT to reduce image dimensions before extracting texture features from images using gray level co-occurrence matrix. Texture features and Euclidean distance are then used to retrieve similar images from a database. The system is tested on a dataset of 1000 images from 10 classes and achieves an average retrieval accuracy of 89.8%.
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.
A New Approach of Medical Image Fusion using Discrete Wavelet TransformIDES Editor
MRI-PET medical image fusion has important
clinical significance. Medical image fusion is the important
step after registration, which is an integrative display method
of two images. The PET image shows the brain function with
a low spatial resolution, MRI image shows the brain tissue
anatomy and contains no functional information. Hence, a
perfect fused image should contains both functional
information and more spatial characteristics with no spatial
& color distortion. The DWT coefficients of MRI-PET
intensity values are fused based on the even degree method
and cross correlation method The performance of proposed
image fusion scheme is evaluated with PSNR and RMSE and
its also compared with the existing techniques.
Detection and Classification in Hyperspectral Images using Rate Distortion an...Pioneer Natural Resources
This document summarizes an experiment that compares two methods of bit allocation for compressing hyperspectral imagery using JPEG2000: 1) the traditional high bit rate quantizer approach and 2) the rate distortion optimal (RDO) approach. The experiment shows that both methods perform well at relatively low bit rates, achieving over 96% classification accuracy. However, at very low bit rates, the RDO approach outperforms the high bit rate quantizer approach, achieving 90% accuracy at 0.0375 bpppb compared to less than 90% for the high bit rate method. The RDO approach also achieves lower mean squared error than the high bit rate quantizer approach.
Comparative study on image fusion methods in spatial domainIAEME Publication
This document provides a comparative study of various image fusion methods in the spatial domain. It begins by introducing image fusion and its applications. Section 2 then describes several common fusion algorithms in the spatial domain, including average, select maximum/minimum, Brovey transform, intensity hue saturation (IHS), and principal component analysis (PCA). Section 3 defines image fusion quality measures like entropy, mean squared error, and normalized cross correlation. Section 4 provides a comparative analysis of the spatial domain fusion techniques based on parameters like simplicity, type of resources, and disadvantages. It finds that spatial domain methods provide high spatial resolution but have issues like image blurring and producing less informative outputs. The document concludes that while the best algorithm depends on the problem, spatial
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
This document discusses band ratioing, image differencing, and principal and canonical component analysis techniques in remote sensing. Band ratioing involves dividing pixel values in one band by another band to enhance spectral differences. Image differencing calculates differences between images after alignment. Principal component analysis transforms correlated spectral data into fewer uncorrelated bands retaining most information, while canonical component analysis aims to maximize separability of user-defined features. These techniques can help analyze multispectral and hyperspectral remote sensing data.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScsitconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the
global environment, and in analysing the target detection and recognition .But , segmentation
of (SAR) images is known as a very complex task, due to the existence of speckle noise.
Therefore, in this paper we present a fast SAR images segmentation based on between class
variance. Our choice for used (BCV) method, because it is one of the most effective thresholding
techniques for most real world images with regard to uniformity and shape measures. Our
experiments will be as a test to determine which technique is effective in thresholding
(extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
AUTOMATIC THRESHOLDING TECHNIQUES FOR SAR IMAGEScscpconf
Segmentation of Synthetic Aperture Radar (SAR) images have a great use in observing the global environment, and in analysing the target detection and recognition .But , segmentation of (SAR) images is known as a very complex task, due to the existence of speckle noise. Therefore, in this paper we present a fast SAR images segmentation based on between class variance. Our choice for used (BCV) method, because it is one of the most effective thresholding techniques for most real world images with regard to uniformity and shape measures. Our experiments will be as a test to determine which technique is effective in thresholding (extraction) the oil spill for numerous SAR images, and in the future these thresholding
techniques can be very useful in detection objects in other SAR images
IMPROVEMENT OF BM3D ALGORITHM AND EMPLOYMENT TO SATELLITE AND CFA IMAGES DENO...ijistjournal
This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.
This document describes an extended fuzzy c-means (EFCM) clustering algorithm for noisy image segmentation. The algorithm first preprocesses noisy pixels in an image by regenerating their values based on neighboring pixel intensities. It then applies the conventional fuzzy c-means clustering algorithm to segment the image. The EFCM approach is presented as being less sensitive to noise than other clustering algorithms and able to efficiently segment noisy images. The document provides background on image segmentation, fuzzy c-means clustering, types of image noise, and density-based clustering challenges. It also outlines the EFCM methodology and its computational advantages over other robust clustering methods for noisy image segmentation.
Application of Image Retrieval Techniques to Understand Evolving Weatherijsrd.com
Multispectral satellite images provide valuable information to understand the evolution of various weather systems such as tropical cyclones, shifting of intra tropical convergence zone, moments of various troughs etc., accurate prediction and estimation will save live and property. This work will deal with the development of an application which will enable users to search an image from database using either gray level, texture and shape features for meteorological satellite image retrieval .Gray level feature is extracted using histogram method. The Texture feature is extracted using gray level co-occurrence method and wavelet approach. The shape feature vector is extracted using morphological operations. The similarity between query image and database images is calculated using Euclidian distance. The performance of the system is evaluated using precision
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
IMAGE SEGMENTATION BY USING THRESHOLDING TECHNIQUES FOR MEDICAL IMAGEScseij
Image binarization is the process of separation of pixel values into two groups, black as background and
white as foreground. Thresholding can be categorized into global thresholding and local thresholding. This
paper describes a locally adaptive thresholding technique that removes background by using local mean
and standard deviation. Most common and simplest approach to segment an image is using thresholding.
In this work we present an efficient implementation for threshoding and give a detailed comparison of
Niblack and sauvola local thresholding algorithm. Niblack and sauvola thresholding algorithm is
implemented on medical images. The quality of segmented image is measured by statistical parameters:
Jaccard Similarity Coefficient, Peak Signal to Noise Ratio (PSNR).
This lecture is about particle image velocimetry technique. It include discussion about the basic element of PIV setup, image capturing, laser lights, synchronize and correlation analysis.
This document describes a method for segmenting gray scale images using iterative triclass thresholding based on Otsu's method. It begins with applying PCA to reduce the image to a single band. Otsu's method is then used to initially segment the image into foreground, background, and a third region to be further processed. Morphological operations like dilation and erosion are applied for smoothing. The threshold is recalculated and triclass partitioning is repeated iteratively until the target regions are extracted with better accuracy. The method provides low complexity segmentation with better noise removal and object detection performance.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
The document discusses image segmentation techniques. It describes image segmentation as partitioning a digital image into multiple regions based on characteristics like color or texture. Common applications of image segmentation include industrial inspection, optical character recognition, and medical imaging. The techniques discussed are fixed thresholding, iterative thresholding, and fuzzy c-means clustering. Fuzzy c-means clustering is identified as the most suitable for pest image segmentation based on its lower entropy and normalized mutual information values. Simulated annealing is also proposed to improve upon the limitations of fuzzy c-means clustering.
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
This document describes a content-based image retrieval system that uses 2-D discrete wavelet transform with texture features. It proposes using DWT to reduce image dimensions before extracting texture features from images using gray level co-occurrence matrix. Texture features and Euclidean distance are then used to retrieve similar images from a database. The system is tested on a dataset of 1000 images from 10 classes and achieves an average retrieval accuracy of 89.8%.
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.
An Unsupervised Change Detection in Satellite IMAGES Using MRFFCM ClusteringEditor IJCATR
This document summarizes an unsupervised change detection method for satellite images using Markov random field fuzzy c-means (MRFFCM) clustering. The method first generates a difference image from multitemporal satellite images using image fusion techniques. It then applies MRFFCM clustering to the difference image to segment it into changed and unchanged regions. Experimental results on real synthetic aperture radar images show that MRFFCM clustering produces more accurate change detection results with less error than previous approaches, while also having lower time complexity. The method is evaluated on datasets from Bern, Ottawa, and the Yellow River region, demonstrating its effectiveness.
A Review on Deformation Measurement from Speckle Patterns using Digital Image...IRJET Journal
This document reviews digital image correlation (DIC) for deformation measurement using speckle patterns. DIC is a non-contact optical method that uses digital images of a speckle pattern on a surface before and after deformation. By comparing the speckle patterns in the images, DIC can determine displacement and strain fields with high accuracy. The document discusses speckle pattern types, the DIC process, related works that have improved DIC methods, and applications of DIC such as for high-temperature testing. DIC provides full-field measurements and greater accuracy compared to conventional contact methods.
IRJET- Lunar Image Fusion based on DT-CWT, Curvelet Transform and NSCTIRJET Journal
This document compares three image fusion techniques - Dual Tree Complex Wavelet Transform (DT-CWT), Curvelet transform, and Nonsubsampled Contourlet Transform (NSCT) - for fusing high spectral resolution lunar images from the HySI instrument and high spatial resolution images from the TMC instrument on the Chandrayaan-1 satellite. Statistical analysis of the fused images shows that the NSCT technique best preserves both the spectral information of the HySI image and the spatial information of the TMC image, with correlation coefficients over 0.99, higher entropy and average gradients, and lower root mean square errors than the other techniques. Therefore, NSCT produces the highest quality fused lunar images according to these evaluation metrics.
Novel Approach for Image Restoration and TransmissionCSCJournals
This paper develops a new technique in image restoration and transmission process, where the image size is halved after transforming it to the frequency domain by applying discrete Fourier transform. The conjugate symmetry and mirror property of transformed image spectrums could be utilized by deleting the redundant spectrums from second half image after tracking and keeping the conjugated locations. Those redundant locations are kept using one- to- one relationship. Depending on the halving procedure, the new image size will be divided by two. A reconstructed procedure is created to redistribute the deleted spectrum with their associated locations. The reconstructed image is ready now for restoring again by applying the inverse discrete Fourier transform back to the spatial domain. The restored image is qualified using Peak Signal to Noise Ratio measurement and the result was very satisfied. The advantages of this technique appear in the storage cost, where the memory locations will be reduced to the half. Also, from communication side, this work approved that the image transmission time needs to transmit the halved image is half of the original one.
Local Distance and Dempster-Dhafer for Multi-Focus Image Fusionsipij
The document describes a new method for fusing multi-focus images using Dempster-Shafer theory and local variability (DST-LV). The method calculates the quadratic distance between each pixel value and its neighboring pixels to determine local variability, which is then used to define mass functions in Dempster-Shafer theory. The proposed method was tested on 150 images and found to produce higher quality fused images compared to other popular fusion methods like PCA, DWT, and gradient bilateral, with lower RMSE values. Potential applications of the method include image fusion for drones, medical imaging, and food quality inspection.
Local Distance and Dempster-Dhafer for Multi-Focus Image Fusionsipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability
(DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in
calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all
the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part.
The results of the proposed method are significantly better when comparing them to results of other
methods.
Geometric wavelet transform for optical flow estimation algorithmijcga
This paper described an algorithm for computing the optical flow (OF) vector of a moving objet in a video sequence based on geometric wavelet transform (GWT). This method tries to calculate the motion between two successive frames by using a GWT. It consists to project the OF vectors on a basis of geometric wavelet. Using GWT for OF estimation has been attracting much attention. This approach takes advantage of the geometric wavelet filter property and requires only two frames. This algorithm is fast and able to estimate the OF with a low-complexity. The technique is suitable for video compression, and can be used for stereo vision and image registration.
Density Driven Image Coding for Tumor Detection in mri ImageIOSRjournaljce
The significant of multi spectral band resolution is explored towards selection of feature coefficients based on its energy density. Toward the feature representiaon in transformed domain, multi wavelet transformations were used for finer spectral representation. However, due to a large feature count these features are not optimal under low resource computing system. In the recognition units, running with low resources a new coding approach of feature selection, considering the band spectral density is developed. The effective selection of feature element, based on its spectral density achieve two objective of pattern recognition, the feature coefficient representiaon is minimized, hence leading to lower resource requirement, and dominant feature representation, resulting in higher retrieval performance.
Dynamic texture based traffic vehicle monitoring systemeSAT Journals
Abstract Dynamic Textures are function of both space and time which exhibit spatio-temporal stationary property. They have spatially repetitive patterns which vary with time. In this paper, importance of phase spectrum in the signals is utilized and a novel method for vehicle monitoring is proposed with the help of Fourier analysis, synthesis and image processing. Methods like Doppler radar or GPS navigation are used commonly for tracking. The proposed image based approach has an added advantage that the clear image of the object (vehicle) can be used for future reference like proof of incidence, identification of owner and registration number. Keywords-Fourier Transform, dynamic texture, phase spectrum
This document presents an image fusion algorithm based on wavelet transform and second generation curvelet transform. It begins with an introduction to image fusion and discusses limitations of wavelet transforms. It then introduces the second generation curvelet transform, which represents edges and singularities better than wavelets. The proposed algorithm uses both wavelet and curvelet transforms to decompose and fuse images. It applies the algorithm to experiments fusing multi-focus images and complementary CT and MRI images. Results show the curvelet-based fusion produces clearer images that better preserve useful information from the source images.
Detection of Bridges using Different Types of High Resolution Satellite Imagesidescitation
Automatic detection of geographical objects such as roads, buildings and bridges
from remote sensing imagery is a very meaningful but difficult work. Bridges over water is
a typical geographical object and its automatic detection is of great significance for many
applications. Finding Region Of Interest (ROI) having water areas alone is the most crucial
task in bridge detection. This can be done with image processing / soft computing methods
using images in spatial domain or with Normalized Differential Water Index (NDWI) using
images in spectral domain. We have developed an efficient algorithm for bridge detection
where the ROI segmentation is done using both methods. Exact locations of bridges are
obtained by knowledge models and spatial resolution of the image. These knowledge models
are applied in the algorithm in such a way that the thresholds are automatically fixed
depending on the quality of the image. Using the algorithm any type of bridges are extracted
irrespective of their inclination and shape.
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.
IRJET-A Review of Underwater Image Enhancement By Wavelet Decomposition using...IRJET Journal
This document reviews underwater image enhancement techniques using wavelet decomposition implemented on a field programmable gate array (FPGA). It begins with an introduction to the poor quality of underwater images due to light scattering and color distortion. It then discusses prior work on underwater image enhancement using techniques like wavelet fusion and contrast adjustment. The proposed approach involves color correction, contrast enhancement, and multi-scale fusion via wavelet decomposition of the color-corrected and contrast-enhanced images. Low frequency components are fused with weighted averaging while high frequency components use local variance. Experimental results demonstrate this wavelet fusion approach improves underwater image visibility.
Abstract: Primarily due to the progresses in super resolution imagery, the methods of segment-based image analysis for generating and updating geographical information are becoming more and more important. This work presents a image segmentation based on colour features with K-means clustering. The entire work is divided into two stages. First enhancement of color separation of satellite image using de correlation stretching is carried out and then the regions are grouped into a set of five classes using K-means clustering algorithm. At first, the spatial data is concentrated focused around every pixel, and at that point two separating procedures are added to smother the impact of pseudoedges. What's more, the spatial data weight is built and grouped with k-means bunching, and the regularization quality in every district is controlled by the bunching focus esteem. The exploratory results, on both reenacted and genuine datasets, demonstrate that the proposed methodology can adequately lessen the pseudoedges of the aggregate variety regularization in the level.
A comparative study for the assessment of Ikonos satellite image-fusion techn...IJEECSIAES
Image-fusion provide users with detailed information about the urban and rural environment, which is useful for applications such as urban planning and management when higher spatial resolution images are not available. There are different image fusion methods. This paper implements, evaluates, and compares six satellite image-fusion methods, namely wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter (HPF) transform, simple mean value, and PCA. An Ikonos image (PanchromaticPAN and multispectral-MULTI) showing the northwest of Bogotá (Colombia) is used to generate six fused images: MULTIWavelet 2D-M, MULTIG-S, MULTIMHF, MULTIHPF, MULTISMV, and MULTIPCA. In order to assess the efficiency of the six image-fusion methods, the resulting images were evaluated in terms of both spatial quality and spectral quality. To this end, four metrics were applied, namely the correlation index, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE) and the Q index. The best results were obtained for the MULTISMV image, which exhibited spectral correlation higher than 0.85, a Q index of 0.84, and the highest scores in spectral assessment according to ERGAS and RASE, 4.36% and 17.39% respectively.
A comparative study for the assessment of Ikonos satellite image-fusion techn...nooriasukmaningtyas
This document compares six satellite image fusion techniques: wavelet 2D-M transform, gram schmidt, high-frequency modulation, high pass filter transform, simple mean value, and principal component analysis. It applies these techniques to fuse a high-resolution panchromatic Ikonos image with a lower-resolution multispectral Ikonos image of an area near Bogota, Colombia. It then evaluates the spatial and spectral quality of the resulting fused images using four metrics: correlation coefficient, erreur relative globale adimensionnelle de synthese (ERGAS), relative average spectral error (RASE), and the Q index. The technique producing the best results was found to be the simple mean value fusion,
Orientation Spectral Resolution Coding for Pattern RecognitionIOSRjournaljce
In the approach of pattern recognition, feature descriptions are of greater importance. Features are represented in spatial domain and transformed domain. Wherein, spatial domain features are of lower representation, transformed domains are finer and more informative. In the transformed domain representation, features are represented using spectral coding using advanced transformation technique such as wavelet transformation. However, the feature extraction approach considers the band coefficients; the orientation variation is not considered. In this paper towards inherent orientation variation among each spectral band is derived, and the approach of orientation filtration is made for effective feature representation. The obtained result illustrates an improvement in the recognition accuracy, in comparison to conventional retrieval system.
Change Detection from Remotely Sensed Images Based on Stationary Wavelet Tran...IJECEIAES
The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.
Similar to Sub-windowed laser speckle image velocimetry by fast fourier transform technique (20)
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
This study Examines the Effectiveness of Talent Procurement through the Imple...
Sub-windowed laser speckle image velocimetry by fast fourier transform technique
1. 1
Sub-windowed laser speckle image velocimetry by fast
fourier transform technique
R. Balamurugan1*, G. Rajarajan2* and S. Jayanthi3
1 Department of Physics, Kumaraguru College of Technology, Coimbatore 641 049
Tamilnadu, India
2 Department of Physics, Hindustan Institute of Technology & Science,
Chennai 603 103 Tamilnadu, India
3 Department of Electronics and Communication Sciences, Hindustan College of Arts and Science,
Chennai 603 103 Tamilnadu, India
*Email: balamurugan.r.sci@kct.ac.in & grajarajan@yahoo.com
Abstract
In this work, laser speckle velocimetry, a unique optical method for velocity measurement
of fluid flow has been described. A laser sheet is developed and is illuminated on microscopic
seeded particles to produce the speckle pattern at the recording plane. Double frame-
single-exposure speckle images are captured in such a way that the second speckle image is shifted
exactly in a known direction. The auto-correlation method has the ambiguity of direction of flow. To
rectify this, spatial shift of the second image has been premeditated. Cross-correlation of sub
interrogation areas is obtained by Fast Fourier Transform technique. Four sub-windows processed
to obtain the velocity information with vector map analysis precisely.
Keywords: Laser speckle; seeded particles; sub-windows; fast fourier transform; velocity measurement; vector
maps
2. 1. Introduction
It is indispensable in fluid mechanics that the measurements of flow properties like
velocity, vortices, and pressure field in many applications. Seeded particle is also known as the
tracer particle photography method has become a suitable method for the determination of the
velocity of fluid flow. The light scattering tracer particles put off in the flow to provide the velocity
information to track the instantaneous fluid flow is the principle of Particle Image Velocimetry
method. Particle Tracking Velocimetry perfect for low image density [1], the intermediate-range
image density is called Particle Image Velocimetry and Laser Speckle Velocimetry suitable for high
image density of seeded particles. Melling [2] reported that variety of seeded particles is used in
liquid and gas image velocimetry experiments and for spraying in the fluid flow. Particle Image
Velocimetry method is preferred for both liquid and airflow referred as correlation image
velocimetry [3]. It consists of test area encompassing seeded particles, laser sheet to illuminate the
area of interest, charge-coupled device to capture images with software for velocity information by
the position of seeded particles [4-5]. Laser Speckle Velocimetry (LSV), first demonstrated by Barker
and Fourney [Barker D B and Fourney M E. ‘Measuring flow velocities with speckle patterns’. Opt
Lett, 1:135–137 (1977)], with novelty of measuring the flow velocity in a less intrusive way than that
offered by Laser Doppler Velocimetry (LDV) which is based on the measurement of the velocity of
the visualised fluid-markers, with the additional advantage of being whole-field.
To extract the displacement of a group of particles over a short time period, two successive
images are compared. Two- in- one approach of fluid temperature and flow measurements by
thermo-sensitive polymer particles is reported [6]. A high resolution spatial and temporal turbulent
flow measurement by instantaneous time snapshots is reported in [7]. A cost-effective,
single-camera 3D velocimetry method called as endoscopic tomographic velocimetry has been
developed [8]. The central-wall-end design on the fluid characteristics is experimentally verified in
a laboratory-scale pond [9].
In this work, Fast Fourier Transform technique [Cooley J W and Tukey O W. ‘An algorithm for
the machine calculation of complex fourier series’. Math Comput, 297-301:19 (1965)] for correlation
has been used to evaluate the double frame-single exposure recordings of Laser Speckle
Velocimetry. A small domain of first speckle image, named as sub-window is compared with a
sub-window of the same coordinates in the second image by cross-correlation. Interrogation areas
3. are the same size and hence the velocity map acquired from PIV presents vectors arranged on an
identical grid. Depends on the window size, the strength of the correlation peak increases and
constructing a valid measurement.
2. Materials and Methods
The experimental setup is shown in figure.1.
Fig. 1 Schematic experimental setup of Laser Speckle Velocimetry
The Nd-YAG laser of energy 100 mJ, the wavelength of 532nm and pulse duration of 10ns is
illuminated in the plane of investigation. It develops a bright sheet with a constant thickness
without aberration. The pulsed laser is transformed as a sheet with the help of a cylindrical lens. The
fluid flow direction and the light sheet has been aligned satisfactorily. The speckle patterns are
recorded by a 10 Megapixel resolution and 30 fps CCD camera which is positioned perpendicular to
the laser plane sheet.High concentration seeded particles are used to scatter the laser light and is
captured by two separate frames with a short inter-frame period of 0.01s. The two major images of
speckle patterns LSI-A and LSI-B are split into small interrogation areas a1, a2, a3, a4 and b1, b2, b3,
b4 respectively as shown in figures 2&3.
4. Fig. 2 LSI-A and sub-windows a1, a2, a3 and a4
Figure.3. LSI-B and its sub-windows b1, b2, b3 and b4.
2.1. Image evaluation by double frame single exposure
There are many ways to extract the mean displacement of the particles in sub-interrogation
spots from the speckle images. The autocorrelation technique is used for a multi-exposed single
image. The cross-correlation method is appropriate for single exposed but multiple images. Laser
speckle cross-correlation of two sequential images using FFT technique because it is more clear-cut
as well as accurate method [10]. Seeded polyurethane granule images are compared successively at
short interval time to find the displacements. A sub-interrogation area of the first image is
compared with the second image at the same location of sub-interrogation area through double
frame by single exposure method. The principle of double frame single exposure is illustrated in
Figure.4.
5. Fig.4 Double frame/single exposure cross-correlation
This interrogation was performed manually by divided into many parts of images. This
furnishes the possible displacement vector map for a particular -sub-window pattern. The little
image density is preferred for 3D great speed flows, but it is not suitable generally for the
high-density tracer particles or in two-phase flows is investigated.
2.2. Fast Fourier Transform
Cross-correlation is not computed for the two main images, but for each sub interrogation
windows. The value of the displacement is embodied by the peak value of the output image. Thus,
we can calculate the velocity by applying the above equation (1). The position vector (Xi) and the
image position vector (xi) of a particle are related to the first exposure is given by:
Xi = xi * M. … (1)
Here, M is magnification factor. First exposure image intensity field expressed by:
I(x) = ∑ Vo(Xi)τ(x − xi)
𝑁
𝑖=1
… (2)
Where, Vo(Xi) is the transfer function of a particle i in the interrogation windows and τ(x) is the
point spread function. ΔX is the displacement vector between two exposures. Second exposure
image intensity field is:
I′(x) = ∑ V′o(Xj + ΔX )τ(x − xj − δx)
𝑁
𝑗=1
… (3)
Where, δx is the displacement of the particle is obtained by:
X = δx/M. …. (4)
R = IFT • I’FT* … (5)
6. Where IFT and I’FT are the Fourier transforms of the functions I and I’ respectively. The
de-convolution of the image pair helps to find the local displacement function. In the speckle
pattern image processing, each sub-image is transformed from the real to the complex domain by
Fast Fourier Transforms (FFT). Then, complex conjugate multiplication between the transformed
results of both speckle images is computed. Finally, the product is reversed to the real domain by
applying inverse FFT as shown in Figure 6.
Fig. 6 Principle of Fast Fourier Transform
3. Result and discussions
Two images are recorded with a time interval, in which the second image was captured a
known time Δt, after the first image. In Fourier Transform analysis of the image pairs, each
sub-image is transformed from the real to the complex domain using fast Fourier transforms. In the
complex domain a conjugate multiplication between the transform results from both images takes
place, and the product is transformed back to the real domain using an inverse FFT. This yields after
normalization about the same image representation of the 2D probability–density function of the
level of matching between the two sub-images. Increasing the computational speed by optimizing
FFT practices with the help of lookup tables, data re-ordering, weighting coefficients and
fine-tuning the machine level code. LSI-A and LSI-B images are shown in Figure 7&8. The FFT
correlation of these speckle image is shown in Figure.9. with peak value.
7. Fig. 7 First speckle image (LSI-A)
Fig. 8 Second speckle image (LSI-B)
Fig. 9 Correlation of speckle images by FFT (LSI-A&LSI-B)
8. The suitable method for cross-correlation analysis is the division of square
sub-interrogation window with side length N = 2n here, n is an integer. N is occupied as a power of
2 to take the improvement in cross-correlation analysis of the frequency domain by Fast Fourier
transform and is explained [11]. N = 32 and an image size of mR x nC pixels, where mR and nC are
the number of rows and columns respectively. Each image has divided into four images without
non-overlapping m x n sub-windows. After that, we perform the 2D cross-correlation of each
sub-window pair in the image pair. The cross-correlation function is defined as:
R(s, t) =
1
N2
∑ ∑ FI,J
′
(i, j)FI,J
′′
(i + s, j + t)
N−1
j=0
N−1
i=0 … (6)
Where R is the recurrent cross-correlation among sub-windows I, J in the first image of the image
pair (F') and the next image of the image pair (F"), i,j is the pixel location within sub-window I, J, and
s,t are the 2-D cyclic lag for that cross-correlation computing is mentioned in the equation(1). R is
habitually calculated in the spectral domain can be written as:
R(s, t) = ℱ−1
[ℱ∗
{FI,J
′
)} ℱ{FI,J
′′
(i + s, j + t)} … (7)
Where ℱ and ℱ−1
are the Fourier and Inverse Fourier Transform operators. The star denotes
complex conjugate. The cross-correlation of two tasks is the same to a complex conjugate
multiplication of their Fourier transforms. Fourier transform is efficiently implemented for discrete
data by using the Fast Fourier transform, which reduces the computation from O[N2] operations to
O[N log2 N] operations. Match up to O [N4] for the direct computation of the 2D correlation the
process is abridged to O [N2 log2 N] operations. The computational efficiency of this
implementation can be increased by observing the symmetry properties between real value
function and their Fourier transform, named as real part of the transform (which is symmetric) and
imaginary part (anti-symmetric). The vector maps for different windows are shown in Figure.10.
and the colour scale graph is shown in Figure.11.
9. Fig. 10 Vector map-Interrogation (64 pixels) of the LSI-A & LSI-B (including all 4 windows)
11. Table 2 Comparison of parameters of speckle images
Correlation of
Speckle images
Peak differences
The difference of correlation of
speckle images with LSI-A&LSI-B
LSI-A&LSI-B 0.96-0.60 0.36
a1&b1 0.98-0.82 0.16
a2&b2 0.90-0.40 0.50
a3&b3 0.94-0.74 0.20
a4&b4 0.90-0.20 0.70
Various values of speckle images are obtained as a database from the major images LSI-A,
LSI-B and sub interrogation images. X,Y axes values, minimum, maximum and mean values are
given in the table-1. It is explicit that a3&b3 is the best matching because short gap between the
LSI-A&LSI-B value of 0.36 with 0.20 difference as shown in table-2. It requires only 0.16 value to
matching the major image. The pair a1&b1 is the better matching because medium gap between the
LSI-A&LSI-B value of 0.36 with 0.16 difference. It requires 0.20 to match the major image. The pair
a2&b2 is good matching because the gap between LSI-A&LSI-B value of 0.36 with 0.50 difference. It
means -0.14 is needed to coincide with the major image. The negative sign indicates the direction of
vector map deviated with error. The pair a4&b4 is not appropriately matching because large gap
between the LSI-A&LSI-B value of 0.36 with 0.70 difference. It means -0.34 is needed to coincide
with the major image. The negative sign indicates that the large mismatching of the direction of
vector map with more error as shown in arrow marks in Figure.10.Displacement range limitation,
periodicity of data and bias error, all these points are handled properly, FFT algorithm provides the
necessary correlation data from which the displacement data can be retrieved. Inside the
cross-correlation domain, the peak’s location resultant to the normal shift of particles inside the
sub-window area is identified. The pixel shift is converted into a velocity through calibration
parameters.
A high density seeded particles are required for the PIV vector maps, predominantly for
experimental data analysis and in numerical calculations. In airflow method, it is uneasy to achieve
a high image density because beyond some level, the seeded particle density cannot be increased
further in the flow. This yields the cross-correlation output of the 2D probability–density function of
12. the level of matching between the 2 sub-images. This method is suitable when the noise in the
signals is negligible or insignificant.
But, the noise due to different capturing conditions makes the degradation of the output
yield data. Sharp signal peak gives the displacement in sub-pixels level. The displacement function
d is measured by using the best match between the images in a statistical way of speckle pattern
[12]. All seeded particles confined by the interrogation small areas having no uniformities in
velocity due to various factors like turbulence, velocity gradients, particle size etc., The common
FFT implementation needs the input data to have a base-2 Dimensions (i.e. 32 × 32 pixel or 64 × 64
pixel or 128x128 pixels samples). This process carried out for the pairs of a1 & b1, a2 & b2, a3 & b3
and a4 & b4. Advantage of this method is to identify the offset error in both samples of images
according to the mean displacement of the seeded particles between the two illuminations. This
reduces loss of correlation in-plane and therefore increases the correlation peak strength. The light
scattering effect of seeded tracer particles and their photographic images are studied [13].
The individual particles in the 1-10 micron range would produce more detectable images at
lower mass loadings than the large numbers. But the fine particles would be needed to produce
speckle. Multiple scattering causes for broadened light-sheet makes the poor resolution; this effect is
negligible because majority of the Mie scattering is in forward direction together with the direction
of transmission of the laser beam reported by Christopher Abram and et al [14]. The irregular
pattern of this portion may due to the variation of seeding particle concentration. If the density of
the seeded particles is low, then the pattern is resolved, but here the concentration is increased, so
the images overlap and interfere to develop a random cigar like bright and dark image pattern.
Certain errors cause for the poor result in some part of the analysis are mentioned below and their
impact involved in this experimental study:
1. Random error due to noise in the recorded images.
2. Bias error arising from the process of computing the signal peak location to sub-pixel
accuracy [15].
3. Gradient error resulting from rotation and deformation of the flow within an
interrogation spot leading to loss of correlation. These are minimized by careful
selection of experimental conditions.
Laser Speckle Velocimetry gives a suitable tool for studying the fluid flow in two dimensional,
but it is still limited as they are confined to measurements within a plane. These errors can be
13. rectified by a cautious selection of experimental components and make the suitable ambient
conditions. Though the experimental conditions are perfect, Laser speckle velocimetry producing
vector map consists of ‘bad’ vectors called as false or spurious vectors. These are readily identifiable
when the vector field is re-plotted after subtracting the mean. Magnitudes and directions of bad
vectors are significantly different from their neighbours. Bad vectors are due to the -sub-window
areas, in which Signal to Noise Ratio (SNR) is less than unity because noise peak is higher than the
signal peak. Usually, less than 2% of vectors considered as bad. This is due to lack of particle pairs in
the interrogation area by inadequate seeding density or excess of tracer particles are out-of-plane
motion such that the particles exoduses the light sheet between laser pulses. At irregular intervals, a
piece of fragments or flare from physical boundaries or objects, drawn-out into the measured area.
Hence it causes for overwhelming response of the interrogation area leading to a bad vector.
In this case, a blurry imaged particle just below the focused plane makes the adverse effect of
result. The sequence of the colour red and green wavelength enhancing the particle streak
velocimetry (CSPSV) is reported [16]. The speed of the process may be increased by downsampling
the images during the interrogation passes. The use of smaller interrogation samples is evaluated
much faster. A constant interrogation window size can be used regardless of the image resolution. It
means a 4X down-sampled image interrogated by a 32x32 pixel sampling window corresponds to a
128 x128 pixel sample at the initial image resolution.
4. Conclusions
Double frame by single exposure laser speckle velocimetry method for the measurement of
fluid flow has been described with the help of sub-divided interrogation areas. Determination of the
displacement of seeded particle within the interrogation areas gives the velocity of fluid flow and
various pixel size vector maps have been reported. This non-intrusive method uses four equal
sub-windows from the consecutive speckle images and computed by FFT algorithm.
Acknowledgements
The author Dr. RB would like to thank the management of Kumaraguru College of Technology,
who continuously encouraging for this area of research work. The authors are highly grateful to the
support of Indian Institute of Technology, Madras for the development of LSV technology.
Conflicts of Interest: The author declares no conflict of interest.
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