This document presents a computational model of perceptual image distortion that aims to better predict how humans perceive differences between images. The model consists of retinal, cortical, and detection components based on studies of spatial pattern detection. It processes reference and distorted images to measure distortion visibility rather than raw pixel differences. The model is shown to better predict which of two distorted images appears more degraded compared to using mean squared error alone. It also predicts distortion visibility in JPEG compressed images at different quality settings.
ABSTRACT
The multimedia applications are rapidly increasing. It is essential to ensure the authenticity of multimedia
components. The image is one of the integrated components of the multimedia. In this paper ,we desing a
model based on customized filter mask to ensure the authenticity of image that means the image forgery
detection based on customized filter mask. We have satisfactory results for our dataset.
Visual Image Quality Assessment Technique using FSIMEditor IJCATR
The goal of quality assessment (QA) research is to design algorithms that can automatically
assess the quality of images in a perceptually consistent manner. Image QA algorithms generally
interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual
space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression
and other distorted images, this paper puts forward an image quality assessment method based on phase
congruency and gradient magnitude. The experimental results show that the image quality assessment
method has a higher accuracy than traditional method and it can accurately reflect the image visual
perception of the human eye. In this paper, we propose an image information measure that quantifies the
information that is present in the reference image and how much of this reference information can be
extracted from the distorted image.
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.
Blind Image Quality Assessment with Local Contrast Features ijcisjournal
The aim of this research is to create a tool to evaluate distortion in images without the information about
original image. Work is to extract the statistical information of the edges and boundaries in the image and
to study the correlation between the extracted features. Change in the structural information like shape and
amount of edges of the image derives quality prediction of the image. Local contrast features are effectively
detected from the responses of Gradient Magnitude (G) and Laplacian of Gaussian (L) operations. Using
the joint adaptive normalisation, G and L are normalised. Normalised values are quantized into M and N
levels respectively. For these quantised M levels of G and N levels of L, Probability (P) and conditional
probability(C) are calculated. Four sets of values namely marginal distributions of gradient magnitude Pg,
marginal distributions of Laplacian of Gaussian Pl, conditional probability of gradient magnitude Cg and
probability of Laplacian of Gaussian Cl are formed. These four segments or models are Pg, Pl, Cg and Cl.
The assumption is that the dependencies between features of gradient magnitude and Laplacian of
Gaussian can formulate the level of distortion in the image. To find out them, Spearman and Pearson
correlations between Pg, Pl and Cg, Cl are calculated. Four different correlation values of each image are
the area of interest. Results are also compared with classical tool Structural Similarity Index Measure
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
ABSTRACT
The multimedia applications are rapidly increasing. It is essential to ensure the authenticity of multimedia
components. The image is one of the integrated components of the multimedia. In this paper ,we desing a
model based on customized filter mask to ensure the authenticity of image that means the image forgery
detection based on customized filter mask. We have satisfactory results for our dataset.
Visual Image Quality Assessment Technique using FSIMEditor IJCATR
The goal of quality assessment (QA) research is to design algorithms that can automatically
assess the quality of images in a perceptually consistent manner. Image QA algorithms generally
interpret image quality as fidelity or similarity with a “reference” or “perfect” image in some perceptual
space. In order to improve the assessment accuracy of white noise, Gauss blur, JPEG2000 compression
and other distorted images, this paper puts forward an image quality assessment method based on phase
congruency and gradient magnitude. The experimental results show that the image quality assessment
method has a higher accuracy than traditional method and it can accurately reflect the image visual
perception of the human eye. In this paper, we propose an image information measure that quantifies the
information that is present in the reference image and how much of this reference information can be
extracted from the distorted image.
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.
Blind Image Quality Assessment with Local Contrast Features ijcisjournal
The aim of this research is to create a tool to evaluate distortion in images without the information about
original image. Work is to extract the statistical information of the edges and boundaries in the image and
to study the correlation between the extracted features. Change in the structural information like shape and
amount of edges of the image derives quality prediction of the image. Local contrast features are effectively
detected from the responses of Gradient Magnitude (G) and Laplacian of Gaussian (L) operations. Using
the joint adaptive normalisation, G and L are normalised. Normalised values are quantized into M and N
levels respectively. For these quantised M levels of G and N levels of L, Probability (P) and conditional
probability(C) are calculated. Four sets of values namely marginal distributions of gradient magnitude Pg,
marginal distributions of Laplacian of Gaussian Pl, conditional probability of gradient magnitude Cg and
probability of Laplacian of Gaussian Cl are formed. These four segments or models are Pg, Pl, Cg and Cl.
The assumption is that the dependencies between features of gradient magnitude and Laplacian of
Gaussian can formulate the level of distortion in the image. To find out them, Spearman and Pearson
correlations between Pg, Pl and Cg, Cl are calculated. Four different correlation values of each image are
the area of interest. Results are also compared with classical tool Structural Similarity Index Measure
Literature Survey on Image Deblurring TechniquesEditor IJCATR
Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes
to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many
methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The
merits and demerits of different methods are discussed in this concern
A Novel Model of Influence Function: Calibration of a Continuous Membrane Def...IDES Editor
Measurement and modeling of the influence
function plays a vital role in assessing the performance of the
continuous MEMS deformable mirror (DM) for adaptive optics
applications. The influence function is represented in terms
of Zernike polynomials and shown that the dominant modes
for representation of central actuators of the DM are different
from those for the edge actuators. In this paper, a novel and
effective method of modeling the influence function for all
the DM actuators is proposed using the 2D sinc-squared
function.
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
Humans are able to process a face in a variety of
ways to categorize it by its identity, along with a number of
other demographic characteristics, including race, gender ,
and age. Experimental results are based on a face database
containing subjects. Race and gender also play an important
role in face-related applications. Experimental results are
indicated that participants categorized the race of the face
and this categorization drives the perceptual process. A face
image data set is collected from Internet, and divided into a
training dataset and a test dataset. Experimental results based
on a face database containing 250 subjects. The proposed
system can also be applied to other image-based classification
tasks.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
A Novel Model of Influence Function: Calibration of a Continuous Membrane Def...IDES Editor
Measurement and modeling of the influence
function plays a vital role in assessing the performance of the
continuous MEMS deformable mirror (DM) for adaptive optics
applications. The influence function is represented in terms
of Zernike polynomials and shown that the dominant modes
for representation of central actuators of the DM are different
from those for the edge actuators. In this paper, a novel and
effective method of modeling the influence function for all
the DM actuators is proposed using the 2D sinc-squared
function.
An Efficient Approach of Segmentation and Blind Deconvolution in Image Restor...iosrjce
This paper introduces the concept of Blind Deconvolution for restoration of a digital image and
small segments of a single image that has been degraded due to some noise. Concept of Image Restoration is
used in various areas like in Robotics to take decision, Biomedical research for analysis of tissues, cells and
cellular constituents etc. Segmentation is used to divide an image into multiple meaningful regions. Concept of
segmentation is helpful for restoration of only selected portion of the image hence reduces the complexity of the
system by focusing only on those parts of the image that need to be restored. There exist so many techniques for
the restoration of a degraded image like Wiener filter, Regularized filter, Lucy Richardson algorithm etc. All
these techniques use prior knowledge of blur kernel for restoration process. In Blind Deconvolution technique
Blur kernel initially remains unknown. This paper uses Gaussian low pass filter to convolve an image. Gaussian
low pass filter minimize the problem of ringing effect. Ringing effect occurs in image when transition between
one point to another is not clearly defined. After removing these ringing effects from the restored image,
resultant image will be clear in visibility. The aim of this paper is to provide better algorithm that can be helpful
in removing unwanted features from the image and the quality of the image can be measured in terms of
PSNR(Peak Signal-to-Noise Ratio) and MSE(Mean Square error). Proposed Technique also works well with
Motion Blur.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image deblurring based on spectral measures of whitenessijma
Image Deblurring is an ill-posed inverse problem used to reconstruct the sharp image from the unknown
blurred image. This process involves restoration of high frequency information from the blurred image. It
includes a learning technique which initially focuses on the main edges of the image and then gradually
takes details into account. As blind image deblurring is ill-posed, it has infinite number of solutions leading
to an ill-conditioned blur operator. So regularization or prior knowledge on both the unknown image and
the blur operator is needed to address this problem. The performance of this optimization problem depends
on the regularization parameter and the iteration number. In already existing methods the iterations have
to be manually stopped. In this paper, a new idea is proposed to regulate the number of iterations and the
regularization parameter automatically. The proposed criteria yields, on average, an ISNR only 0.38dB
below what is obtained by manual stopping. The results obtained with synthetically blurred images are
good and considerable, even when the blur operator is ill-conditioned and the blurred image is noisy.
Humans are able to process a face in a variety of
ways to categorize it by its identity, along with a number of
other demographic characteristics, including race, gender ,
and age. Experimental results are based on a face database
containing subjects. Race and gender also play an important
role in face-related applications. Experimental results are
indicated that participants categorized the race of the face
and this categorization drives the perceptual process. A face
image data set is collected from Internet, and divided into a
training dataset and a test dataset. Experimental results based
on a face database containing 250 subjects. The proposed
system can also be applied to other image-based classification
tasks.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
Template matching is a technique in computer vision used for finding a sub-image of a target image which matches a template image. This technique is widely used in object detection fields such as vehicle tracking, robotics , medical imaging, and manufacturing .
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
An error measure for evaluating disparity maps is presented. It offers advantages over conventional ground-truth based error measures.
Cabezas, I.; Padilla, V. & Trujillo, M. (2011), A Measure for Accuracy Disparity Maps Evaluation., in César San Martín & Sang-Woon Kim, ed., 'CIARP' , Springer, , pp. 223-231 .
Perceptual Weights Based On Local Energy For Image Quality AssessmentCSCJournals
This paper proposes an image quality metric that can effectively measure the quality of an image that correlates well with human judgment on the appearance of the image. The present work adds a new dimension to the structural approach based full-reference image quality assessment for gray scale images. The proposed method assigns more weight to the distortions present in the visual regions of interest of the reference (original) image than to the distortions present in the other regions of the image, referred to as perceptual weights. The perceptual features and their weights are computed based on the local energy modeling of the original image. The proposed model is validated using the image database provided by LIVE (Laboratory for Image & Video Engineering, The University of Texas at Austin) based on the evaluation metrics as suggested in the video quality experts group (VQEG) Phase I FR-TV test.
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
In the near future, there is an eminent demand for High Resolution images. In order to fulfil this
demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more
Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR
image in that set and combine the information into a single HR image. Conventional interpolation methods can
produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome
the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically
verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily,
outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable
for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim.
Image fusion technology is also used to fuse two processed images obtained through the algorithm
Effective Pixel Interpolation for Image Super ResolutionIOSR Journals
Abstract: In the near future, there is an eminent demand for High Resolution images. In order to fulfil this demand, Super Resolution (SR) is an approach used to renovate High Resolution (HR) image from one or more Low Resolution (LR) images. The aspiration of SR is to dig up the self-sufficient information from each LR image in that set and combine the information into a single HR image. Conventional interpolation methods can produce sharp edges; however, they are approximators and tend to weaken fine structure. In order to overcome the drawback, a new approach of Effective Pixel Interpolation method is incorporated. It has been numerically verified that the resulting algorithm reinstate sharp edges and enhance fine structures satisfactorily, outperforming conventional methods. The suggested algorithm has also proved efficient enough to be applicable for real-time processing for resolution enhancement of image. Statistical examples are shown to verify the claim. Image fusion technology is also used to fuse two processed images obtained through the algorithm. Keywords: Super Resolution, Interpolation, EESM, Image Fusion
AN ANN BASED BRAIN ABNORMALITY DETECTION USING MR IMAGEScscpconf
The Main purpose of this paper is to design, implement and evaluate a strong automatic diagnostic system that increases the accuracy of tumor diagnosis in brain using MR images.This presented work classifies the brain tissues as normal or abnormal automatically, usingcomputer vision. This saves lot of radiologist time to carryout monotonous repeated job. The
acquired MR images are processed using image preprocessing techniques. The preprocessed images are then segmented, and the various features are extracted. The extracted features are
fed to the artificial neural network as input that trains the network using error back propagation algorithm for correct decision making.
Image fusion is a technique of
intertwining at least two pictures of same scene to
shape single melded picture which shows indispensable
data in the melded picture. Picture combination
system is utilized for expelling clamor from the
pictures. Commotion is an undesirable material which
crumbles the nature of a picture influencing the
lucidity of a picture. Clamor can be of different kinds,
for example, Gaussian commotion, motivation clamor,
uniform commotion and so forth. Pictures degenerate
some of the time amid securing or transmission or
because of blame memory areas in the equipment.
Picture combination should be possible at three
dimensions, for example, pixel level combination,
highlight level combination and choice dimension
combination. There are essentially two kinds of picture
combination methods which are spatial area
combination systems and transient space combination
procedures. (PCA) combination, Normal strategy, high
pass sifting are spatial area techniques and strategies
which incorporate change, for example, Discrete
Cosine Transform, Discrete wavelet change are
transient space combination strategies. There are
different techniques for picture combination which
have numerous favorable circumstances and
detriments. Numerous procedures experience the ill
effects of the issue of shading curios that comes in the
intertwined picture shaped. Also, the Cyclopean One
of the most astonishing properties of human stereo
vision is the combination of the left and right
perspectives of a scene into a solitary cyclopean one.
Under typical survey conditions, the world shows up as
observed from a virtual eye set halfway between the
left and right eye positions. The apparent picture of
the world is never recorded specifically by any tangible
exhibit, however developed by our neural equipment.
The term cyclopean alludes to a type of visual
upgrades that is characterized by binocular
dissimilarity alone. He suspected that stereo-psis may
find concealed articles, this may be helpful to discover
disguised items. The critical part of this examination
when utilizing arbitrary dab stereo-grams was that
uniqueness is adequate for stereo-psis, and where had
just demonstrated that binocular difference was vital
for stereo-psis.
Three-dimensional multimodal models of objective classes are a great tool in modeling and recognition. The multimodal involuntary emotion recognition during a mentally challenged-based communication is presented. We have easily found the mentally disorder people without a doctor. The features are built upon the emotion, motion and frequency to identifying the percentage of mentally disorder peoples. Using Different categories of an image, video, audio and emotions can be discriminated. An image using an algorithms for classification is 3DMM (Three-dimensional morph able models) used to fit the model to images, and a framework for face emotion recognition. GPSO (Guided Particle Swarm Optimization) the emotion finding problem is basically an exploration problem, where at every point; we are pointed to recognize which of the thinkable emotions ensures the current facial expression denotes and GA (Genetic Algorithm) has the virtues of overflowing coding, and decoding, assigning complex information flexibly. GA is calculating the percentage of mental disorder. We proposed using different algorithm to identify the mentally challenged persons.
AUTOMATED IMAGE MOSAICING SYSTEM WITH ANALYSIS OVER VARIOUS IMAGE NOISEijcsa
Mosaicing is blending together of several arbitrarily shaped images to form one large balanced image such
that boundaries between the original images are not seen. Image mosaicing creates a large field of view
using of scene and the result image can be used for texture mapping of a 3D environment too. Blended
image has become a wide necessity in images captured from real time sensor devices, bio-medical
equipment, satellite images from space, aerospace, security systems, brain mapping, genetics etc. Idea
behind this work is to automate the Image Mosaicing System so that blending may be fast, easy and
efficient even if large number of images are considered. This work also provides an analysis of blending
over images containing different kinds of distortion and noise which further enhances the quality of the
system and make the system more reliable and robust.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
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!
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Perceptual image distortion
1. Perceptual Image Distortion
Patrick Teo and David Heeger
Stanford University
First IEEE International Conference on Image Processing, v2, pp 982-986,
November 1994
1
2. Distorted Images with Similar
Mean Squared Errors
Many imaging and image processing
methods are evaluated by how well
the images they output resemble
some given image. Examples include:
image data compression, dithering
algorithms, flat-panel display and
printer design. In all of these cases, the
human visual system is the judge of
image fidelity. Most of these methods
use the mean squared error (MSE) or
root mean squared error (RMSE)
between the two images as a measure
of visual distortion. These measures
are popular largely because of their
analytical tractability. It has long been A commonly used image of Albert Einstein
in image processing.
accepted that MSE (or RMSE) is
inaccurate in predicting perceived
distortion. This is illustrated in the
following paradoxical example.
2
3. Similar Mean Squared Errors
(cont’d)
The top two images on the right were
created by adding different types of
distortions to the original image; the
original image is shown below them.
Root Mean Squared Error of 8.5 The root mean squared error (RMSE)
between each of the distorted images
and the original were computed. The
root mean squared error is the square
root of the average squared difference
between every pixel in the distorted
image and its counterpart in the
original image.
The RMSE between the first distorted
image and the original is 8.5 while the
Root Mean Squared Error of 9.0
RMSE between the second distorted
image and the original is 9.0. Although
the RMSE of the first image is less than
that of the second, the distortion
introduced in the first image is more
visible than the distortion added to
the second. Thus, the root mean
squared error is a poor indicator of
perceptual image fidelity.
Original
3
4. A Computational Model of
Perceptual Image Distortion
We have developed a perceptual distortion measure based on a model
of spatial pattern detection.
It is important to recognize the relevance of these empirical spatial
pattern detection results to developing measures of image integrity. In a
typical spatial pattern detection experiment, the contrast of a visual
stimulus (called the target) is adjusted until it is just barely detectable.
Threshold contrasts of the target are measured over a range of spatial
frequencies, mean luminance, and spatial extents.
In some experiments (called contrast masking experiments), the target
is also superimposed on a background pattern (called the masker). In
other experiments (called luminance masking experiments), the target
is superimposed on a brief, bright, uniform background. In either case
(contrast or luminance masking), the contrast of the target is adjusted
(while the masker is held fixed) until the target is just barely detectable.
Typically, a target is harder to detect (i.e., a higher contrast is required)
in the presence of a masker.
A model that predicts spatial pattern detection is obviously useful in
image processing applications. In the context of image compression, for
example, the target takes the place of quantization error and the
masker takes the place of the original image.
4
5. Perceptual Image Distortion
Our model consists of three main
parts: a retinal component, a cortical
component, and a detection
mechanism. The retinal component is
responsible for contrast sensitivity and
its dependence on mean luminance
masking. The cortical component
accounts for contrast masking. To
compute perceptual image distortion,
the reference and distorted images are
passed through these two stages of
the model independently. At this
point, the images have been
normalized for the differential
sensitivities of the human visual
system. The final (detection
mechanism) component of the model
A model of perceptual image distortion. compares these two normalized
images to give a measure of image
fidelity. The final result is an image
representing the probability of
perceiving a distortion at each position
in the distorted image.
5
6. Model Predictions of
Visible Distortion
The top image on the right is the
original image. The two images
directly below it were created by
adding different types of distortions to
the original image. The root mean
squared error (RMSE) between the left
distorted image and the original (8.5)
is smaller than the root mean squared
error between the right distorted
image and the original (9.0). In spite of
that, the distortion is more visible in
the left image.
The images directly below each
distorted image are the predictions of
the perceptual image distortion
model. Lighter areas indicate regions
where the distortion is more visible
while darker areas indicate regions
where the distortion is less visible. The
model correctly predicts that the left
distorted image is more visibly
distorted than the right.
Model predictions: lighter areas indicate greater visible errors.
6
7. Visible Distortion in JPEG
Compressed Images
To further validate the model's
performance, we applied the model to
JPEG compressed images. The original
image was compressed using the JPEG
algorithm at different quality settings.
The model was then used to predict
the visibility of the distortion between
each compressed image and the
original.
The pairs of images on the left are
images compressed using the JPEG
algorithm along with the model's
predictions of the amount of visible
distortion when compared with the
JPEG qual. setting = 80, RMSE = 9.5, PDM = 1.2 original. The image compressed at a
quality setting of 80 is virtually
indistinguishable from the original.
The model's prediction corroborates
this observation. The average
distortion value computed by the
model is 1.2, which indicates that the
distortion is slightly above threshold
(threshold is set at 1.0).
7
8. JPEG Compressed Images
(cont’d)
The image compressed at a quality
setting of 20 is slightly deteriorated
JPEG qual. setting = 20, RMSE=11.4, PDM = 5.7
while the image compressed at a
quality setting of 10 shows marked
blocking artifacts. The model's
predictions agree with these trends
fairly well.
JPEG qual. setting = 10, RMSE = 12.9, PDM = 9.8
8
9. Error Histograms of JPEG
Compressed Images
The top graph plots a histogram of the
squared error differences for individual
pixels. The bottom graph plots a
histogram of the perceptual distortion
predictions of the model for individual
pixels. Both histograms have been
normalized so that the vertical axis
represents fractions of the total
number of pixels.
The histograms of squared error
differences for the different
compressed images are very similar to
one another. The histograms of
perceptual distortion predictions of
the different compressed images are
dramatically different from one
another. It is clear, for example, that
the model predicts that the images
compressed at quality settings of 20
and 10 (the "middle" and "right"
images) are more severely distorted
than the image compressed at a
Histograms of Squared Error (top) and Perceptual quality setting of 80 (the "left" image).
Distortion Measure (bottom) predicted by the model for
the 3 JPEG compressed images: “left” (JPEG quality 80),
“middle” (JPEG quality 20), “right” (JPEG quality 10).
9