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.
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.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
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.
This document presents a novel edge detection algorithm proposed for mammographic images. It begins with an abstract summarizing the paper's focus on edge detection in mammograms and comparison to other common edge detection methods. It then provides background on edge detection and medical image analysis, describing common gradient and derivative-based edge detection methods. The main body introduces a new two-phase edge detection process called Binary Homogeneity Enhancement Algorithm (BHEA) that homogenizes the mammogram and detects edges by traversing the image horizontally and vertically. Results from the new method are then compared to other common edge detection filters.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
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.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Fpga implementation of image segmentation by using edge detection based on so...eSAT Journals
This document summarizes an article that presents a method for implementing image segmentation using edge detection based on the Sobel edge operator on an FPGA. It describes how the Sobel operator works by calculating horizontal and vertical gradients to detect edges. The document outlines the steps to segment an image using Sobel edge detection, including applying horizontal and vertical masks, calculating the gradient, and thresholding. It also provides the architecture for the FPGA implementation, including modules for pixel generation, Sobel enhancement, edge detection, and binary segmentation. The results show edge detection outputs from MATLAB and simulation waveforms, demonstrating the FPGA-based method can perform edge-based image segmentation.
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.
This document presents a novel edge detection algorithm proposed for mammographic images. It begins with an abstract summarizing the paper's focus on edge detection in mammograms and comparison to other common edge detection methods. It then provides background on edge detection and medical image analysis, describing common gradient and derivative-based edge detection methods. The main body introduces a new two-phase edge detection process called Binary Homogeneity Enhancement Algorithm (BHEA) that homogenizes the mammogram and detects edges by traversing the image horizontally and vertically. Results from the new method are then compared to other common edge detection filters.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
SEMANTIC IMAGE RETRIEVAL USING MULTIPLE FEATUREScscpconf
In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar
images, the need for databases, the semantic gap, and retrieving the desired images from huge
collections are the keys to improve. CBIR system analyzes the image content for indexing,
management, extraction and retrieval via low-level features such as color, texture and shape.
To achieve higher semantic performance, recent system seeks to combine the low-level features
of images with high-level features that conation perceptual information for human beings.
Performance improvements of indexing and retrieval play an important role for providing
advanced CBIR services. To overcome these above problems, a new query-by-image technique
using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
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.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
This document discusses boundary detection techniques for images. It proposes a generalized boundary detection method (Gb) that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation and contour grouping methods are also introduced to further improve boundary detection accuracy with minimal extra computation. The document presents outputs of Gb on sample images and concludes that Gb effectively detects boundaries in a principled manner by jointly resolving constraints from multiple image interpretation layers in closed form.
GABOR WAVELETS AND MORPHOLOGICAL SHARED WEIGHTED NEURAL NETWORK BASED AUTOMAT...sipij
1) The document proposes an automatic face recognition system using Gabor wavelet face detection with neural networks and morphological shared weighted neural networks (MSNN) for face recognition.
2) Face detection is performed using Gabor filters for feature extraction and a neural network for classification. Detected faces are input to the MSNN for face recognition.
3) The MSNN uses hit-miss transforms for feature extraction in each layer, which are independent of grayscale shifts. Feature matching compares output thresholds to identify faces.
This document summarizes and compares four no-reference blur estimation methods: (1) a scale adaptive wavelet transform method, (2) a method using energy of quadrature filters, (3) a kurtosis measurement method using discrete dyadic wavelet transform, and (4) a method measuring average edge width. The methods are evaluated on blurred, noisy, and compressed images from a database. Results show the quadrature filter method performs best except for compressed images, where a perceptual method works best. The scale adaptive and kurtosis methods degrade more with noise while the quadrature filter method is more robust.
A survey on efficient no reference blur estimation methodseSAT Journals
Abstract Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-reference blur estimation methods. The first method applies a scale adaptive technique of blur estimation to get better accuracy in the results. The second blur metric involves finding the energy using second order derivatives of an image using derivative pair of quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the images. The fourth method of blur estimation is obtained by finding the ratio of sum of the edge widths of all the detected edges to the total number of edges. The results provided are useful in comparing the methods based on metrics like Spearman correlation coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database. The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different categories namely Gaussian blur, motion blur and also JPEG2000 compressed images. Blur estimation finds its application in quality assessment, image fusion and auto-focusing in images. The sharpness of an image can also be found from the blur metric as sharpness is inversely proportional to blur. Sharpness metrics can also be combined with other metrics to measure overall quality of the image. Keywords: Blur estimation, blur, no-reference
Texture based feature extraction and object trackingPriyanka Goswami
This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.
Spot Edge Detection in cDNA Microarray Images using Window based Bi-Dimension...idescitation
Ongoing Microarray is an increasingly playing a crucial role applied in the field
of medical and biological operations. The initiator of Microarray technology is M. Schena et
al. [1] and from past few years microarrays have begun to be used in many fields such as
biomedicine, mostly on cancer and Diabetic, and medical diagnoses. A Deoxyribonucleic
Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface,
such as glass, plastic or silicon chip forming an array. Processing of DNA microarray image
analysis includes three tasks: gridding, segmentation and intensity extraction and at the
stage of processing, the irregularities of shape and spot position which leads to generate
significant errors. This article presents a new spot edge detection method using Window
based Bi-dimensional Empirical Mode Decomposition. On separating spots form the
background area and to decreases the probability of errors and gives more accurate
information about the states of spots we are proposing a spot edge detection via WBEMD.
By using this method we can identify the spots with low density, which leads to increasing
the performance of cDNA microarray images.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
This document presents a new approach for fingerprint matching called the Minutia Cylindrical Code (MCC) approach. It involves extracting minutia points from fingerprint images, then generating a code for each fingerprint based on the local structure and spatial relationships of minutia points within a cylindrical neighborhood. MCC codes make the fingerprints invariant to scale and rotation. The approach is tested on a database of 200 fingerprints and achieves false acceptance ratios between 6-13% and false rejection ratios below 0.12% depending on the threshold used. The MCC approach performs fingerprint matching efficiently while maintaining accuracy even when fingerprints are rotated or scaled.
Vegetables detection from the glossary shop for the blind.IOSR Journals
This document describes a system for automatically recognizing vegetables in images to help blind people shop independently in grocery stores. It uses image processing and machine learning techniques to extract features like color, shape, size and texture from images of vegetables. These features are then used to classify and identify the vegetables by comparing to stored data. The system analyzes images, identifies the vegetables within them, and verbally informs the blind user of the names and other information to help them select the vegetables they want.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Design of Gabor Filter for Noise Reduction in Betel Vine leaves Disease Segme...IOSR Journals
This document describes a design of a Gabor filter for noise reduction in images of betel vine leaves to aid in disease segmentation. A Gabor filter is designed using Verilog HDL and implemented on a CADENCE platform. The filter takes pixel inputs from images that have undergone preprocessing like Sobel edge detection and segmentation. It convolves the pixels with stored filter coefficients to reduce noise and segment the diseased areas. The proposed Gabor filter achieves noiseless segmentation with increased speed and reduced delays compared to existing methods. It utilizes fewer resources with minimal warnings. The system could be enhanced further with 2D/3D processing and neural network training.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
The document discusses designing teams and processes to adapt to changing needs. It recommends structuring teams so members can work within their competencies and across projects fluidly with clear roles and expectations. The design process should support the team and their work, and be flexible enough to change with team, organization, and project needs. An effective team culture builds an environment where members feel free to be themselves, voice opinions, and feel supported.
UX, ethnography and possibilities: for Libraries, Museums and ArchivesNed Potter
1) The document discusses how the University of York Library has used various user experience (UX) techniques like ethnographic observation and interviews to better understand user needs and behaviors.
2) Some changes implemented based on UX findings include installing hot water taps, changing hours, and adding blankets - aimed at improving the small details of user experience.
3) The presentation encourages other libraries, archives and museums to try incorporating UX techniques like behavioral mapping and cognitive interviews to inform design changes that enhance services for users.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
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.
This document summarizes and reviews several techniques for image mining, including feature extraction, image clustering, and object recognition algorithms. It discusses color, texture, and edge feature extraction techniques and evaluates their precision and recall. It also describes the block truncation algorithm for image recognition and the cascade feature extraction approach. The key techniques - color moments, block truncation coding, and cascade classifiers - are evaluated based on experimental recall and precision results. Overall, the document provides an overview of different image mining techniques and evaluates their effectiveness.
Performance of Efficient Closed-Form Solution to Comprehensive Frontier Exposureiosrjce
This document discusses boundary detection techniques for images. It proposes a generalized boundary detection method (Gb) that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation and contour grouping methods are also introduced to further improve boundary detection accuracy with minimal extra computation. The document presents outputs of Gb on sample images and concludes that Gb effectively detects boundaries in a principled manner by jointly resolving constraints from multiple image interpretation layers in closed form.
GABOR WAVELETS AND MORPHOLOGICAL SHARED WEIGHTED NEURAL NETWORK BASED AUTOMAT...sipij
1) The document proposes an automatic face recognition system using Gabor wavelet face detection with neural networks and morphological shared weighted neural networks (MSNN) for face recognition.
2) Face detection is performed using Gabor filters for feature extraction and a neural network for classification. Detected faces are input to the MSNN for face recognition.
3) The MSNN uses hit-miss transforms for feature extraction in each layer, which are independent of grayscale shifts. Feature matching compares output thresholds to identify faces.
This document summarizes and compares four no-reference blur estimation methods: (1) a scale adaptive wavelet transform method, (2) a method using energy of quadrature filters, (3) a kurtosis measurement method using discrete dyadic wavelet transform, and (4) a method measuring average edge width. The methods are evaluated on blurred, noisy, and compressed images from a database. Results show the quadrature filter method performs best except for compressed images, where a perceptual method works best. The scale adaptive and kurtosis methods degrade more with noise while the quadrature filter method is more robust.
A survey on efficient no reference blur estimation methodseSAT Journals
Abstract Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-reference blur estimation methods. The first method applies a scale adaptive technique of blur estimation to get better accuracy in the results. The second blur metric involves finding the energy using second order derivatives of an image using derivative pair of quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the images. The fourth method of blur estimation is obtained by finding the ratio of sum of the edge widths of all the detected edges to the total number of edges. The results provided are useful in comparing the methods based on metrics like Spearman correlation coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database. The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different categories namely Gaussian blur, motion blur and also JPEG2000 compressed images. Blur estimation finds its application in quality assessment, image fusion and auto-focusing in images. The sharpness of an image can also be found from the blur metric as sharpness is inversely proportional to blur. Sharpness metrics can also be combined with other metrics to measure overall quality of the image. Keywords: Blur estimation, blur, no-reference
Texture based feature extraction and object trackingPriyanka Goswami
This document provides a project report on texture-based feature extraction and object tracking. It discusses using various texture analysis techniques like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), and Local Ternary Pattern (LTP) to extract features from images for tasks like cloud tracking. It implements these techniques in MATLAB and evaluates them on standard datasets to extract features and represent images with histograms for tasks like image recognition and analysis while reducing computational requirements compared to using raw images. The techniques are then applied to track cloud motion in weather satellite images by analyzing differences in texture histograms over time.
Spot Edge Detection in cDNA Microarray Images using Window based Bi-Dimension...idescitation
Ongoing Microarray is an increasingly playing a crucial role applied in the field
of medical and biological operations. The initiator of Microarray technology is M. Schena et
al. [1] and from past few years microarrays have begun to be used in many fields such as
biomedicine, mostly on cancer and Diabetic, and medical diagnoses. A Deoxyribonucleic
Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface,
such as glass, plastic or silicon chip forming an array. Processing of DNA microarray image
analysis includes three tasks: gridding, segmentation and intensity extraction and at the
stage of processing, the irregularities of shape and spot position which leads to generate
significant errors. This article presents a new spot edge detection method using Window
based Bi-dimensional Empirical Mode Decomposition. On separating spots form the
background area and to decreases the probability of errors and gives more accurate
information about the states of spots we are proposing a spot edge detection via WBEMD.
By using this method we can identify the spots with low density, which leads to increasing
the performance of cDNA microarray images.
This document presents a hybrid approach for color image segmentation that integrates color edge information and seeded region growing. It uses color edge detection in CIE L*a*b color space to select initial seed regions and guide region growth. Seeded region growing is performed based on color similarity between pixels. The edge map and region map are fused to produce homogeneous regions with closed boundaries. Small regions are then merged. The approach is tested on images from the Berkeley segmentation dataset and produces reasonably good segmentation results by combining color and edge information.
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
Feature Extraction of an Image by Using Adaptive Filtering and Morpological S...IOSR Journals
Abstract: For enhancing an image various enhancement schemes are used which includes gray scale manipulation, filtering and Histogram Equalization, Where Histogram equalization is one of the well known image enhancement technique. It became a popular technique for contrast enhancement because it is simple and effective. The basic idea of Histogram Equalization method is to remap the gray levels of an image. Here using morphological segmentation we can get the segmented image. Morphological reconstruction is used to segment the image. Comparative analysis of different enhancement and segmentation will be carried out. This comparison will be done on the basis of subjective and objective parameters. Subjective parameter is visual quality and objective parameters are Area, Perimeter, Min and Max intensity, Avg Voxel Intensity, Std Dev of Intensity, Eccentricity, Coefficient of skewness, Coefficient of Kurtosis, Median intensity, Mode intensity. Keywords: Histogram Equalization, Segmentation, Morphological Reconstruction .
This document presents a new approach for fingerprint matching called the Minutia Cylindrical Code (MCC) approach. It involves extracting minutia points from fingerprint images, then generating a code for each fingerprint based on the local structure and spatial relationships of minutia points within a cylindrical neighborhood. MCC codes make the fingerprints invariant to scale and rotation. The approach is tested on a database of 200 fingerprints and achieves false acceptance ratios between 6-13% and false rejection ratios below 0.12% depending on the threshold used. The MCC approach performs fingerprint matching efficiently while maintaining accuracy even when fingerprints are rotated or scaled.
Vegetables detection from the glossary shop for the blind.IOSR Journals
This document describes a system for automatically recognizing vegetables in images to help blind people shop independently in grocery stores. It uses image processing and machine learning techniques to extract features like color, shape, size and texture from images of vegetables. These features are then used to classify and identify the vegetables by comparing to stored data. The system analyzes images, identifies the vegetables within them, and verbally informs the blind user of the names and other information to help them select the vegetables they want.
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Design of Gabor Filter for Noise Reduction in Betel Vine leaves Disease Segme...IOSR Journals
This document describes a design of a Gabor filter for noise reduction in images of betel vine leaves to aid in disease segmentation. A Gabor filter is designed using Verilog HDL and implemented on a CADENCE platform. The filter takes pixel inputs from images that have undergone preprocessing like Sobel edge detection and segmentation. It convolves the pixels with stored filter coefficients to reduce noise and segment the diseased areas. The proposed Gabor filter achieves noiseless segmentation with increased speed and reduced delays compared to existing methods. It utilizes fewer resources with minimal warnings. The system could be enhanced further with 2D/3D processing and neural network training.
This document summarizes a paper presented at the 2nd International Conference on Current Trends in Engineering and Management. The paper proposes using discrete wavelet transform techniques for pixel-based fusion of multi-focus images. It discusses registering the images and then applying pixel-level fusion methods like average, minimum and maximum approaches. It also introduces a wavelet-based fusion method that decomposes images into different frequency bands for fusion. The goal is to produce a single fused image that has the maximum information and focus from the input images.
The document discusses designing teams and processes to adapt to changing needs. It recommends structuring teams so members can work within their competencies and across projects fluidly with clear roles and expectations. The design process should support the team and their work, and be flexible enough to change with team, organization, and project needs. An effective team culture builds an environment where members feel free to be themselves, voice opinions, and feel supported.
UX, ethnography and possibilities: for Libraries, Museums and ArchivesNed Potter
1) The document discusses how the University of York Library has used various user experience (UX) techniques like ethnographic observation and interviews to better understand user needs and behaviors.
2) Some changes implemented based on UX findings include installing hot water taps, changing hours, and adding blankets - aimed at improving the small details of user experience.
3) The presentation encourages other libraries, archives and museums to try incorporating UX techniques like behavioral mapping and cognitive interviews to inform design changes that enhance services for users.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
An immersive workshop at General Assembly, SF. I typically teach this workshop at General Assembly, San Francisco. To see a list of my upcoming classes, visit https://generalassemb.ly/instructors/seth-familian/4813
I also teach this workshop as a private lunch-and-learn or half-day immersive session for corporate clients. To learn more about pricing and availability, please contact me at http://familian1.com
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
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.
Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.
Image Blur Detection with 2D Haar Wavelet Transform and Its Effect on Skewed ...Vladimir Kulyukin
This document presents an algorithm for image blur detection using 2D Haar wavelet transforms. The algorithm splits images into tiles and applies the 2D Haar wavelet transform to each tile to detect regions with pronounced changes. Tiles with similar changes are grouped into clusters. Images with large clusters are classified as sharp, while images with small clusters are classified as blurred. The algorithm is integrated with a skewed barcode scanning algorithm to filter out blurred images, improving scanning success rates. Experimental results found it performed comparably or better than two other blur detection algorithms and positively impacted skewed barcode scanning when integrated.
This document reviews techniques for multi-image morphing. It discusses early cross-dissolve morphing methods and their limitations. Mesh warping and field morphing are introduced as improved techniques that use control points and line mappings to better align images during transition. The document also summarizes point distribution, critical point filters, and other common morphing methods. It concludes by noting that effective morphing requires mechanisms for feature specification, warp generation, and transition control.
Performance Evaluation of Illumination Invariant Face Recognition AlgorthimsIRJET Journal
This document presents a performance evaluation of illumination invariant face recognition algorithms. It proposes a hybrid approach using Local Binary Pattern and Local Ternary Pattern fusion with gradient-based illumination normalization. The performance is evaluated using Receiver Operating Characteristics (ROC) curves and the proposed algorithm is shown to outperform traditional algorithms like Gradientfaces and Weberface on benchmark databases by achieving a higher true positive rate and lower false positive rate.
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.
Features for Cross Spectral Image Matching: A SurveyjournalBEEI
This document summarizes several commonly used features for cross-spectral image matching between visible light and thermal images. It discusses how features represent information from different spectrum images for matching. The document reviews features such as local binary pattern (LBP), histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), Gabor wavelets, discrete cosine transform (DCT) and binary statistical image features (BSIF) that have been used in cross-spectral face and iris recognition with good results. It provides an overview of how these features extract unique characteristics from visible and thermal images to effectively represent the images and enable successful cross-spectral matching.
This summarizes an article about a method for image segmentation and intensity non-uniformity correction. It proposes defining a local clustering criterion function based on intensities in a neighborhood around each point. This function evaluates how well the intensities are classified based on the image partition. An energy term is defined as the integral of this local clustering criterion over the image domain. Minimizing this energy allows simultaneous segmentation of the image into regions and estimation of a bias field to correct for intensity non-uniformities. The method is applied to MRI and other medical images to produce accurate segmentations in the presence of intensity inhomogeneities.
EFFICIENT IMAGE RETRIEVAL USING REGION BASED IMAGE RETRIEVALsipij
1) The document describes an efficient region-based image retrieval system that uses discrete wavelet transform and k-means clustering. It segments images into regions, each characterized by features like size, mean, and covariance.
2) The system pre-processes images by resizing, converting to HSV color space, performing DWT, and using k-means clustering on DWT coefficients to generate regions. It extracts features for each region and stores them in a database.
3) For retrieval, it pre-processes the query image similarly and calculates similarities between the query regions and database regions based on their features, returning similar images.
This document proposes a method for improving medical image registration using mutual information. It aims to address limitations in standard mutual information-based registration when there are local intensity variations. The method incorporates spatial and geometric information by computing mutual information in regions identified by the Harris corner detection operator. These regions have large spatial variations that provide geometric information. The method is tested on synthetic and clinical data, showing improved registration accuracy. It is implemented on a GPU for increased parallel processing efficiency, providing a 4-46% speed improvement over standard registration methods.
V. Karthikeyan proposes a novel histogram-based image registration technique. The method segments images using multiple histogram thresholds to extract objects. Extracted objects are characterized by attributes like area, axis ratio, and fractal dimension. Objects between images are matched to estimate rotation and translation. The technique was tested on pairs of images with different rotations and translations and achieved sub-pixel accuracy in registration. The method outperformed other techniques like SIFT for remote sensing images. Future work could optimize the segmentation and apply the technique to multispectral images.
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
ABSTRACT : Image registration is an important and fundamental task in image processing used to match two different images. Image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A Feature-Based Approach to automated image-to-image registration is presented. In this paper, various methods are used in different Phases of Image registration. The characteristics of this approach is it combines scale interaction of Discrete wavelets for feature extraction, Scale Invariant Feature Transform (SIFT) for feature matching. Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes.
A Novel Feature Extraction Scheme for Medical X-Ray ImagesIJERA Editor
X-ray images are gray scale images with almost the same textural characteristic. Conventional texture or color
features cannot be used for appropriate categorization in medical x-ray image archives. This paper presents a
novel combination of methods like GLCM, LBP and HOG for extracting distinctive invariant features from Xray
images belonging to IRMA (Image Retrieval in Medical applications) database that can be used to perform
reliable matching between different views of an object or scene. GLCM represents the distributions of the
intensities and the information about relative positions of neighboring pixels of an image. The LBP features are
invariant to image scale and rotation, change in 3D viewpoint, addition of noise, and change in illumination A
HOG feature vector represents local shape of an object, having edge information at plural cells. These features
have been exploited in different algorithms for automatic classification of medical X-ray images. Excellent
experimental results obtained in true problems of rotation invariance, particular rotation angle, demonstrate that
good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary
patterns.
This document summarizes a research paper that proposes a two-stage method for enhancing low quality fingerprint images. The first stage uses a ridge-compensation filter in the spatial domain to enhance ridge pixels and reduce non-ridge pixels. The second stage uses polar coordinates and separates filters into radial and angular domains. Local orientation and frequency are estimated based on learning from images. Frequency band pass filtering is then applied using the estimated local orientation and frequency maps. The two-stage approach enhances fingerprint images in both the spatial and frequency domains to improve low quality images.
This document discusses techniques for image segmentation and edge detection. It proposes a generalized boundary detection method called Gb that combines low-level and mid-level image representations in a single eigenvalue problem to detect boundaries. Gb achieves state-of-the-art results at low computational cost. Soft segmentation is also introduced to improve boundary detection accuracy with minimal extra computation. Common methods for edge detection are described, including gradient-based, texture-based, and projection profile-based approaches. Improved Harris and corner detection algorithms are presented to more accurately detect edges and corners. The output of Gb using soft segmentations as input is shown to correlate well with occlusions and whole object boundaries while capturing general boundaries.
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.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
Comparison of various Image Registration Techniques with the Proposed Hybrid ...idescitation
Image Registration is termed as the method to
transform different forms of image data into one coordinate
system. Registration is a important part in image processing
which is used for matching the pictures which are obtained at
different time intervals or from various sensors. A broad range
of registration techniques have been developed for the various
types of image data. These techniques are independently
studied for many applications resulting in the large body of
result. Vision is the most advanced of human sensors, so
naturally images play one of the most important roles in
human perception. Image registration is one of the branches
encompassed by the diverse field of digital image processing.
Due to its importance in many application areas as well as
since its nature is complicated; image registration is now the
topic of much recent research. Registration algorithms tend
to compute transformations to set correspondence betweenthe two images. In this paper the survey is done on various
image registration techniques. Also the different techniques
are compared with the proposed system of the projec
Automatic rectification of perspective distortion from a single image using p...ijcsa
Perspective distortion occurs due to the perspective projection of 3D scene on a 2D surface. Correcting the distortion of a single image without losing any desired information is one of the challenging task in the field of Computer Vision. We consider the problem of estimating perspective distortion from a single still image of an unstructured environment and to make perspective correction which is both quantitatively accurate as well as visually pleasing. Corners are detected based on the orientation of the image. A method based on plane homography and transformation is used to make perspective correction. The algorithm infers frontier information directly from the images, without any reference objects or prior knowledge of the camera parameters. The frontiers are detected using geometric context based segmentation. The goal of this paper is to present a framework providing fully automatic and fast perspective correction.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
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Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Climate Impact of Software Testing at Nordic Testing Days
Ar4201293298
1. R. Sobilaljini et al. Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.293-298
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
Feature Based Registration of Brain Mr Image
R.Sobilal Jini*, S.Robinson Sobitharaj**
*(Department of Electronics and Communication Engineering (Applied Electronics), Loyola Institute of
Technology and Science, Nagercoil.)
** (Department of Electronics and Communication Engineering, Loyola Institute of Technology and Science,
Nagercoil.)
ABSTRACT
Medical image processing is a difficult problem. Not only a registration algorithm needs to capture both large
and small scale image deformations, it also has to deal with global and local intensity variations. Two main
problems occurs during the registration process of non rigid image. First, the correspondence problem occurs
between the template and the subject image due to variation in the voxel intensity level. Second, in the presence
of bias field the occurrence of interference and noise will make the image sensitive to rotation variation. To
avoid these problems and to calculate efficiently a new feature based registration of non rigid brain MR image
using Uniform Pattern of Spherical Region Descriptor is proposed in this paper. The proposed method is based
on a new image feature called Uniform Pattern of Spherical Region Descriptor. This uses two features namely
Uniform pattern of spherical descriptor and Uniform pattern of gradient descriptor to extract geometric features
from input images and to identify first order and second order voxel wise anatomical information. The MRF
labeling frame work and the α- expansion algorithm are used to maximize the energy function. The defected
region in the image is accurately identified by Normalized correlation method. The input image for evaluation is
taken from the database Brain web and internet Brain Segmentation Repository respectively. The performance
can be evaluated using Back propagation networks.
Key Words: Non rigid image registration, Rotation invariance, Normalized correlation.
Most of the elastic deformation based non
rigid image registrations depend on the assumption
I. INTRODUCTION
that the image intensity remains constant between the
Image registration is important in many
images [2]. But it is not always true and it affects the
imaging applications especially in medical image
analysis. In diagnostic imaging there exist always a
registration accuracy. This method will detect the
need for comparing two images of different imaging
defected region by comparing the intensity of the
modalities such as MR, and PET images for disease
image in an fully automatic mode , so the variation in
diagnosis.
the anatomical voxel wise intensity will affect the
Registration of non rigid brain MR image
registration accuracy [1].
plays a vital role in Medical image analysis. Its
In some cases the registration process may
medical applications includes brain disease diagnosis
be trapped at local minima, because of the possibility
and statistical parametric mapping based on the
of occurrence of variation between the intensity and
nature of transformations and the details extracted
the anatomical similarity, this will reduce the goal of
from the image, the Non rigid image registration is
optimizing intensity similarity between the template
broadly classified into three categories, Land mark
and the subject images [3]. In feature based
based registration , Intensity based registration and
approaches [4] different signatures are identified for
Feature based registration. In Land mark based
each voxel and then the registration is carried out as a
registration, several land marks are obtained from the
feature matching process. To remove the effect of
input image using manual land mark fixing
intensity
gray
level
differences,
intensity
techniques. Using this manually located landmarks
standardization procedures are employed [5]. The sub
the anatomical features of the images are extracted
volume deformation model used in Hierarchical
[1].This method is computationally efficient because
Attribute Matching Mechanism for Elastic
it requires prior knowledge about the manually
Registration (HAMMER) algorithm uses the
placed landmark points. The accuracy of registration
Geometric Moment Invariant (GMI) features as the
and complexity of this method increases as required
adopted feature to drive the registration process. But
number if dependable landmarks from the image
this approach requires a Pre- segmented image [6].
increase.
The defected regions in the images are accurately
www.ijera.com
293 | P a g e
2. R. Sobilaljini et al. Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.293-298
determined by comparing the extracted features
between the template and the subject images. The
registration process is formulated as a feature
matching and optimization problem the selected
features determine the accuracy of registration
process [7]. Many factors such as extracted features,
transformation models and the similarity measures
may affect the registration accuracy.
Over the passed decades in non rigid image
registration there exist two main challenging effects
that tend to affect the registration process. First , the
intensity similarity and the anatomical similarity will
not be always remain same because MR image
intensities do not have fixed tissue meaning in image
scale even within the same pattern for the same body
region and also the intensity information alone are
not sufficient to fully characterize the anatomical
difference between different tissue classes [8].
Second, during image acquisition the non
uniformities such as rotation variation and variation
in the gray level bias fields adversely affect the
registration process and this will also produce
interference and noise abnormalities while processing
the image for registration. To overcome these two
effects a new registration algorithm is formulated and
it is based on a novel feature called Uniform Pattern
of Spherical Region Descriptor. This algorithm is
robust and it will maintain the rotation invariance
property and reduce the effect of gray level bias field.
The effect of noise in the image can be removed by
suitable filtering techniques. Therefore a new feature
based non rigid image registration method based on
Uniform Pattern of Spherical Region Descriptor is
proposed. In this method this Uniform Pattern of
Spherical Region Descriptor will act as the signature
for each voxel and it is in variant to rotation and
monotonic gray level field. The Uniform Pattern of
Spherical Region Descriptor feature is combined with
Markov Random Field and the energy function is
optimized with alpha expansion algorithm. The
sample images are obtained from the database Brain
Web and Internet Brain Segmentation Repository
respectively.
II. UNIFORM PATTERN OF
SPHERICAL REGION
DESCRIPTOR
Uniform Pattern of Spherical Region
Descriptor is a new region based descriptor that
combines the distribution function of two 2D
descriptors namely Uniform Pattern of Spherical
Structure and Uniform Pattern of Spherical Gradient.
Uniform Pattern of Spherical Region Descriptor is
the 2-D joint gray level distribution of these two
region descriptors. Uniform Pattern of Spherical
Region Descriptor is used to extract the rotation and
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monotonic gray level transformation
function from the subject image.
invariant
A. Uniform Pattern of Spherical Structure
It is an region descriptor that is used to
extract the anatomical feature from the subject
images. The texture classification in 2-D can be
obtained by proper design of this 3-D descriptor [4].
The obtained 2-D features are converted into 3-D by
using Uniform Pattern of Spherical Structure. We
define a sphere Sv with center voxel V with radius R
and Nv samples are uniformly taken on the surface of
Sv using the sampling methods proposed in [4],
where Sv is a sphere taken from an input image G, in
which each voxel V є G. Tri linear interpolation
method is used to interpolate the samples which do
not fall exactly on the image sampling grid. Then the
image is converted into proper binary numbers by
Otsu’s global thresholding technique. Intensity of
voxel are taken as the major components for the
thresholding operation. The intensity of voxel I on
the surface of Sv is compared with the voxel intensity
of the center voxel V. Hence each voxel i are
thresholded to a binary number “0” or “1” by using
the equation
Bi = 0, if Ii < Iv
(1)
Bi =1, if Ii > Iv
(2)
Where Ii is the intensity of the neighboring
voxel I and Iv is the intensity of the central voxel. Bi
is the binary value. This binary value gives the voxel
wise interaction between the neighboring voxel and
central voxel. The thresholded surface which
resembles the geometric features surrounding the
voxel V is called the Binary Pattern of Spherical
Structure. Since the relative intensity variation does
not alter in this process, the Binary Pattern of
Spherical Structure is said to be monotonic gray level
transformation invariant. The Uniform Pattern of
Spherical Structure and Binary Pattern of Spherical
Structure has two continuous regions of 0’s and 1’s.
B. Uniform Pattern of Spherical Gradient
Uniform Pattern of Spherical Gradient is an
another region descriptor which is delivered based on
the local binary patterns. It gives the second order
voxel wise information. Binary Pattern of Spherical
Gradient is the binary pattern obtained from Uniform
Pattern of Spherical Gradient and it is monotonic
gray level transformation invariant. Each voxel in the
Binary Pattern of Spherical Gradient have the same
labels. The discriminant power of the Gradient
Spherical Pattern is depend on the angle space
between the patterns. The angle space between the
patterns can be divided into four , inorder to prevent
the image from noise and histogram sparsing, these
four angle spaces are given as,
I(Vi) = 1, if Ө Vi є [0, π/4]
(3)
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ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.293-298
I(Vi)= 2, if Ө V є [π/4, π/2]
(4)
i
I(Vi)= 3, if Ө V є [π/2, 3π/4]
(5)
i
I(Vi)= 2, if Ө V є [3π/4, π]
(6)
i
The Binary Pattern of Spherical Gradient
and Uniform Pattern of Spherical Gradient are
converted into two uniform areas. When number of
possible labels to represent the angle space is
increased, it is very difficult for a Binary Pattern of
Spherical Gradient to be Uniform Pattern of
Spherical Gradient. The time complexity analyzed
and the largest connected component is obtained by
using the BFS algorithm which needs O(N) time. It
takes constant computation time.
III. FEATURE EXTACTION USING
UNIFORM PATTERN OF
SPHERICAL DESCRIPTOR
Feature based approaches attempt to find the
correspondence and transformation using distinct
anatomical features that are extracted from images.
These features include solidity, area and perimeter of
anatomical structures. Feature based methods are
typically applied when the local structure information
such as mutual information and correlation are more
significant than the information carried by the image
intensity. They can handle complex between-image
distortions and can be faster. In this approach
normalized correlation and Mutual information are
taken as the parameters of registration. Mutual
information-based registration begins with the
estimation of the joint probability of the intensities of
corresponding voxels in the two images. This joint
probability of the intensities of corresponding voxel
is used as the signature for every voxel.. The mutual
information can be used to parameterize and solve
the correspondence problem in feature-based
registration. Correlation is used to compare several
images of the same object, e.g. to analyze
development of the disease. The processing steps to
extract feature using Uniform pattern of Spherical
Structure is given below.
1. Sample images are taken from the data base
Brainweb and Internet Brain Segmentation
Repository.
2. Sample images are taken as the input images.
3. Input images are converted into Binary number
by comparing its intensity to the intensity of the
Central voxel.
4. Check whether the binary value of the
neighboring voxel is equal to the binary value of
the ith voxel, if it equal, state that the image is
Uniform Pattern of Spherical Structure , or
repeat the step till it becomes true.
5. A rotation-invariant orientation measure
is
defined for each neighboring voxel of the
thresholded input image , which is the angle
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between the orientation and the direction of can
be calculated by,
(7)
As the monotonic gray-level transformation
affects the absolute gradient magnitude of each
neighboring voxel, the gradient orientation
remains the same.
6. The original binary spherical gradient region
centered at the reference voxel can be obtained
by computing the gradient information of the
voxel.
7. Combine the result of step 4 and step 6. Made
the result as the signature for each voxel in the
image.
8. Edge detection is done using Canny’s approach
and proper segmentation is carried out.
9. The features such as solidity, area and perimeter
are extracted.
10. Then the image is registered and classified.
IV. IMAGE SEGMENTATION
Image segmentation is useful in many
applications. It can identify the regions of interest in
a scene or annotate the data. We categorize the
existing segmentation algorithm into region-based
segmentation, data clustering, and edge-base
segmentation. Region-based segmentation includes
the seeded and unseeded region growing algorithms.
Region-based methods mainly rely on the assumption
that the neighboring pixels within one region have
similar value. The selection of the similarity criterion
is significant and the results are influenced by noise
in all instances. The threshold is made by user and it
usually based on intensity, gray level, or color values.
The regions are chosen to be as uniform as possible.
In this proposed method this segmentation assigns
label to every pixel in the image. The defected
regions are detected by comparing the template
image with the different set of segmented image with
several iterations. In the segmentation process 2x2
median filtering is used to filter out the noise in the
image and to preserve the edges. Gamma correction
is done to adjust the intensity values of every voxel in
the image. The accuracy of this technique is higher
compared to intensity based approach since the
proposed method is monotonic gray level and
rotation invariant.
UNIFORM
FEATURES INTENSITY
DESCRIPTOR
GRAY &
WHITE
3.27
15.17
MATTER
GRAY &
3.44
16.03
CSF
WHITE &
3.08
16.84
CSF
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Table 1: Features of image volume obtained from
brain web
V. IMAGE REGISTRATION
The goal of image registration process is to
remove artificial differences in the images introduced
by the patient movement, differences in imaging
devices etc. Both multi and mono-modality
registration plays an important role in medical
applications. In neurosurgery tumors are typically
identified and diagnosed using MRI. Registration of
these modalities allows the transfer of coordinates of
tumors from the MR images. On the process of
registration some distortions such as monotonic gray
level fields and unnecessary rotations may spoil the
registration quality. Using the Uniform Spherical
patterns approach the interferences occurring during
registration process can be eliminated. Depending on
the defected region in the image the features such as
area, perimeter and solidity varies and this defect can
be exactly determined using Normalized correlation
method.
NC (g) = Cf (g) / (no. of iterations)
(8)
Where Cf (g) denotes the correlation. The segmented
image is taken for registration and it is registered to
classify to find whether it is normal or abnormal.
Minimum gradient will be reached as the normal
image is registered and there is no segmented area for
normal image exists as it is similar to the template
image. Best training performance is identified by
performance graph by using the feed forward neural
networks.
VI. RESULT AND DISSCUSSIONS
The entire simulation is done in MATLAB
and executed. To detect the defected region
accurately the proposed method is executed in the
non rigid brain image obtained from the Brain web
and IBSR database. Hence the registered image is
free from rotation and gray level invariant. Fig 1
shows the registered image with extracted feature and
its classification. The image is segmented and its
area, perimeter and solidity are extracted and it is
classified accurately as normal or abnormal image
using the normalized correlation.
Fig 1. Registered Image with extracted feature and
Classification
Several iterations are carried out to find the
defected region accurately it is shown in Fig2.
Fig 2. Segmentation a) with 40 iteration
Fig 2. Segmentation b) with 80 iteration
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5. R. Sobilaljini et al. Int. Journal of Engineering Research and Applications
ISSN : 2248-9622, Vol. 4, Issue 2( Version 1), February 2014, pp.293-298
Fig 8. Output Ws
Fig 2. Segmentation c) with 100 iteration
The registration process consists of various
pre-processing steps to make the image immune to
noise and interference they are shown. In Fig 3 it is
shown that the image is filtered with median filter to
remove noise and preserve edges. Fig 4 gives the
intensity adjusted image and Fig 5 represents the
converted binary image which form the basis for the
proposed feature extraction technique.
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The mutual information and the correlation
coefficient are the two parameters used to register the
image accurately and to determine the defected
region with maximum value. Neural networks are
implanted to measure the performance of the process
and are showed in Fig 9 and Fig 10.
The spotted image can be clearly viewed,
from that features such as area, perimeter, solidity,
maximum intensity, minimum intensity can be
measured.
Fig 3. Filtered image
Fig 9. performance graph
Fig 4. Histogram equalized image
Fig 5. Obtained Binary image
Fig 10. Training Plot
VII.
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CONCLUSION
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Non rigid registration of Brain MR Image
using a novel feature based method is proposed. This
method is invariant to rotation and monotonic gray
level bias fields. Two complementary features of
Uniform Spherical Descriptor are used to encode the
first and second order voxel wise information. This
feature is combined with the MRF labeling frame
work and alpha expansion algorithm to drive the
registration process. The proposed method is having
high power discrimination and is evaluated on the
data base obtained from Brain web and IBSR. Higher
accuracy, better diagnosis and less computation time
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