This document presents a reflectance perception model based face recognition approach that is robust to illumination variations. It proposes a preprocessing algorithm based on the reflectance perception model to generate illumination insensitive images. It then applies principal component analysis (PCA) for feature extraction to reduce the image dimension and remove unwanted vectors. Multiple classifiers are used to extract features from different Fourier domains and frequencies, and scores from these classifiers are combined using a weighted sum fusion method based on equal error rate weights. Experimental results on standard databases show the proposed approach delivers large performance improvements over other face recognition algorithms in handling illumination variations.
View and illumination invariant iterative based image matchingeSAT Journals
Abstract In this paper, the challenges in local-feature-based image matching are variations of view and illumination. Different methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination invariant image matching method. We iteratively estimate the relationship of the relative view and illumination of the images, transform the view of one image to the other, and normalize their illumination for accurate matching. The performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range. The proposed method would fail when the initial view and illumination method fails, which gives us a new sight to evaluate the traditional detectors. We propose two novel indicators for detector evaluation, namely, valid angle and valid illumination, which reflect the maximum allowable change in view and illumination, respectively. Keywords-Feature detector evaluation, image matching, Iterative algorithm.
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.
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
View and illumination invariant iterative based image matchingeSAT Journals
Abstract In this paper, the challenges in local-feature-based image matching are variations of view and illumination. Different methods have been recently proposed to address these problems by using invariant feature detectors and distinctive descriptors. However, the matching performance is still unstable and inaccurate, particularly when large variation in view or illumination occurs. In this paper, we propose a view and illumination invariant image matching method. We iteratively estimate the relationship of the relative view and illumination of the images, transform the view of one image to the other, and normalize their illumination for accurate matching. The performance of matching is significantly improved and is not affected by the changes of view and illumination in a valid range. The proposed method would fail when the initial view and illumination method fails, which gives us a new sight to evaluate the traditional detectors. We propose two novel indicators for detector evaluation, namely, valid angle and valid illumination, which reflect the maximum allowable change in view and illumination, respectively. Keywords-Feature detector evaluation, image matching, Iterative algorithm.
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.
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
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
NEIGHBOUR LOCAL VARIABILITY FOR MULTIFOCUS IMAGES FUSIONsipij
The goal of multi-focus image fusion is to integrate images with different focus objects in order to obtain a
single image with all focus objects. In this paper, we give a new method based on neighbour local
variability (NLV) to fuse multi-focus images. At each pixel, the method uses the local variability calculated
from the quadratic difference between the value of the pixel and the value of all pixels in its
neighbourhood. It expresses the behaviour of the pixel with respect to its neighbours. The variability
preserves the edge function because it detects the sharp intensity of the image. The proposed fusion of each
pixel consists of weighting each pixel by the exponential of its local variability. The quality of this fusion
depends on the size of the neighbourhood region considered. The size depends on the variance and the size
of the blur filter. We start by modelling the value of the neighbourhood region size as a function of the
variance and the size of the blur filter. We compare our method to other methods given in the literature.
We show that our method gives a better result.
Data-Driven Motion Estimation With Spatial AdaptationCSCJournals
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
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
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
CONTRAST ENHANCEMENT AND BRIGHTNESS PRESERVATION USING MULTIDECOMPOSITION HIS...sipij
Histogram Equalization (HE) has been an essential addition to the Image Enhancement world.
Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization
(AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE)
methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final
image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization
(MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input
image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in
correct order. The final image after MDHE gives us the best results based on contrast enhancement and
brightness preservation aspect compared to all other techniques mentioned above. We have calculated the
various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported
by bar graphs, histograms and the parameter calculations at the end.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
SHADOW DETECTION USING TRICOLOR ATTENUATION MODEL ENHANCED WITH ADAPTIVE HIST...ijcsit
Shadows create significant problems in many computer vision and image analysis tasks such as object
recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish
between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a
shadow region as an object. So the detection of shadows in images will enhance the performance of many
machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor
Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of
intensity attenuation of pixels in the shadow region which is different for the three color channels. It
originates from the idea that if the minimum attenuated color channel is subtracted from the maximum
attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will
be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the
contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the
quality of the detected shadow region.
NEIGHBOUR LOCAL VARIABILITY FOR MULTIFOCUS IMAGES FUSIONsipij
The goal of multi-focus image fusion is to integrate images with different focus objects in order to obtain a
single image with all focus objects. In this paper, we give a new method based on neighbour local
variability (NLV) to fuse multi-focus images. At each pixel, the method uses the local variability calculated
from the quadratic difference between the value of the pixel and the value of all pixels in its
neighbourhood. It expresses the behaviour of the pixel with respect to its neighbours. The variability
preserves the edge function because it detects the sharp intensity of the image. The proposed fusion of each
pixel consists of weighting each pixel by the exponential of its local variability. The quality of this fusion
depends on the size of the neighbourhood region considered. The size depends on the variance and the size
of the blur filter. We start by modelling the value of the neighbourhood region size as a function of the
variance and the size of the blur filter. We compare our method to other methods given in the literature.
We show that our method gives a better result.
Data-Driven Motion Estimation With Spatial AdaptationCSCJournals
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
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
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
A comparative study of histogram equalization based image enhancement techniq...sipij
Histogram Equalization is a contrast enhancement te
chnique in the image processing which uses the
histogram of image. However histogram equalization
is not the best method for contrast enhancement
because the mean brightness of the output image is
significantly different from the input image. There
are
several extensions of histogram equalization has be
en proposed to overcome the brightness preservation
challenge. Contrast enhancement using brightness pr
eserving bi-histogram equalization (BBHE) and
Dualistic sub image histogram equalization (DSIHE)
which divides the image histogram into two parts
based on the input mean and median respectively the
n equalizes each sub histogram independently. This
paper provides review of different popular histogra
m equalization techniques and experimental study ba
sed
on the absolute mean brightness error (AMBE), peak
signal to noise ratio (PSNR), Structure similarity
index
(SSI) and Entropy.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
January 2011 - Brazil: Moving Up in the world?FGV Brazil
The Brazilian Economy is one of the oldest publications for expert economic analysis of both the Brazilian and international economies. Through this publication, FGV’s Brazilian Institute of Economics and Finance (FGV/IBRE) compares different periods of the economy, assessing both macroeconomic considerations and scenarios related to finance, administration, marketing, management, insurance, statistics, and price indices.
For more information, and Brazilian economic index results, visit: http://bit.ly/1EA1Loz
December 2010 - Domestic Market: Set to soarFGV Brazil
The Brazilian Economy is one of the oldest publications for expert economic analysis of both the Brazilian and international economies. Through this publication, FGV’s Brazilian Institute of Economics and Finance (FGV/IBRE) compares different periods of the economy, assessing both macroeconomic considerations and scenarios related to finance, administration, marketing, management, insurance, statistics, and price indices.
For more information, and Brazilian economic index results, visit: http://bit.ly/1EA1Loz
This presentation examines two articles related to topics on assistive technology and ethics, “Teaching Assistive Technology through Wikis and Embedded Video” by Oliver Dreon Jr. and Nanette I. Dietrich, and “When Dealing with Human Subjects: Balancing Ethical and Pratical Matters in the Field” by Michael A Evans and Liesl M. Combs. Topics covered in this presentation include defining/history of assistive technology, wikis & video, YouTube, and ethical issues surrounding assistive technologies.
Efficient 3D stereo vision stabilization for multi-camera viewpointsjournalBEEI
In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing
the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method.
Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.
Filtering Based Illumination Normalization Techniques for Face RecognitionRadita Apriana
The main challenge experienced by the present face recognition techniques and smooth filters
are the difficulty in managing illumination. The differences in face images that are created by illumination
are normally bigger compared to the differences in inter-person that is utilized to differentiate identities.
However, face recognition over illumination has more uses in a lot of applications that deal with subjects
that are not cooperative where the highest potential of the face recognition as a non-intrusive biometric
feature can be executed and utilized. A lot of work has been put into the research and development of
illumination and face recognition in the present era and a lot of critical methods have been introduced.
Nevertheless, there are some concerns with face recognition and illumination that require further
considerations which include the deficiencies in comprehending the sub-spaces in illumination pictures,
problems with intractability in face modelling and complicated mechanisms of face surface reflections.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSION acijjournal
ABSTRACT
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
COLOUR IMAGE REPRESENTION OF MULTISPECTRAL IMAGE FUSIONacijjournal
The availability of imaging sensors operating in multiple spectral bands has led to the requirement of
image fusion algorithms that would combine the image from these sensors in an efficient way to give an
image that is more perceptible to human eye. Multispectral Image fusion is the process of combining
images optically acquired in more than one spectral band. In this paper, we present a pixel-level image
fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um),
mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a
composite colour image. The work coalesces a fusion technique that involves linear transformation based
on Cholesky decomposition of the covariance matrix of source data that converts multispectral source
images which are in grayscale into colour image. This work is composed of different segments that
includes estimation of covariance matrix of images, cholesky decomposition and transformation ones.
Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
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
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.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
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
Image enhancement technique plays vital role in improving the quality of the image. Enhancement
technique basically enhances the foreground information and retains the background and improve the
overall contrast of an image. In some case the background of an image hides the structural information of
an image. This paper proposes an algorithm which enhances the foreground image and the background
part separately and stretch the contrast of an image at inter-object level and intra-object level and then
combines it to an enhanced image. The results are compared with various classical methods using image
quality measures
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human ...CSCJournals
In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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V.KARTHIKEYAN PUBLISHED ARTICLE
1. V.Karthikeyan * et al. / (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH
Volume No. 1, Issue No. 2, February - March 2013, 207 - 210
Reflectance Perception Model Based Face
Recognition in Dissimilar Illumination
Conditions
V. KARTHIKEYAN
Department of ECE,
SVS College of Engineering,
Coimbatore-109,
Tamilnadu, India
V. J. VIJAYALAKSHMI
Department of EEE,
SKCET,
Coimbatore – 46
Tamilnadu, India
P. JEYAKUMAR
Student,
Department of ECE,
Karpagam University,
Coimbatore-105
Abstract—Reflectance Perception Based Face Recognition in different Illuminating Conditions is presented. Face
recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images.
Many of the State-of-the art commercial face recognition algorithms still struggle with this problem. In this projected
work a new algorithm is stated for the preprocessing method which compensated for illumination variations in images
along with a robust Principle Component Analysis (PCA) based Facial Feature Extraction is stated which is used to
improve and reduce the dimension of the image by removing the unwanted vectors by the weighted Eigen faces. The
proposed work demonstrates large performance improvements with several standard face recognition algorithms across
multiple, publicly available face databases.
Keywords- Face Recognition, Principle Component Analysis, Reflectance Perception. Illumination Variations
I.
INTRODUCTION
Humans often use faces to recognize individuals and
advancements in computing capability over the past
few decades. Early face recognition algorithms used
simple geometric models but the recognition process
have matured into a science of sophisticated
mathematical representation and matching process.
Major advancements and initiatives in the past ten to
fifteen years have propelled face recognition
technology into the spotlight which can be used for
both identification and Verification. In order to build
robust and efficient face recognition system the
problem of lighting variation is one of the main
technical challenges faced by the designers. In this
paper the focus is mainly on robustness to lighting
variations. First in the processing stage a face image
is transformed into an illumination insensitive image.
The hybrid Fourier features are extracted from
different Fourier domains in different frequency
bandwidths and then each feature is individually
classified by Principal Component Analysis. Along
with this multiple face models generated by plural
normalized face images that have different eye
distances. Finally all the scores from multiple
complementary classifiers a weighted based score
fusion scheme is applied.
II. RELATED WORKS
Automated face recognition is a relatively new
concept. Developed in the 1960s the first semiautomated system for face recognition required the
administrator to locate features such as eyes, ears,
nose and mouth on the photographs before it
ISSN 2320 –5547
calculated distances and ratios to a common reference
point which were then compared to the referenced
data. Along with the pose variation, illumination is
the most significant factor affecting the appearance of
faces. Significant changes in lighting can be seen
greatly within between days and among indoor and
outdoor environments .Greater variations can be seen
for the two images of a same person taken under two
different conditions, say one is taken in a studio and
the other in an outdoor location. Due to the 3D shape
of the face, a direct lighting source can cast strong
shadows that highlight or diminish certain facial
features. Evaluations of face recognition algorithms
consistently show that state-of-the-art systems can
not deal with large differences in illumination
conditions between gallery and probe images [1-3].
In recent years many appearance-based algorithms
have been proposed to deal with this problem and
find a proper solution to it [4-7].Then [5] showed that
the set of images of an object in fixed pose but under
varying illumination forms a convex cone in the
space of images. The illumination cones of human
faces can be approximated well by low-dimensional
linear subspaces [8]. The linear subspaces are
typically estimated from training data, requiring
multiple images of the object under different
illumination conditions. Alternatively, model-based
approaches have been proposed to address the
problem. [9] showed that previously constructed
morph able 3D model to single images. Though the
algorithm works well across pose and illumination,
however, the computational expense is very high. To
eliminate the tarnished halo effect, [10] introduced
low curvature image simplifier (LCIS) hierarchical
@ 2013 http://www.ijitr.com All rights Reserved.
Page | 207
2. V.Karthikeyan * et al. / (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH
Volume No. 1, Issue No. 2, February - March 2013, 207 - 210
decomposition of an image. Each component in this
hierarchy is computed by solving a partial differential
equation inspired by anisotropic diffusion [11]. At
each hierarchical level the method segments the
image into smooth (low-curvature) regions while
stopping at sharp discontinuities. The algorithm is
computationally intensive and requires manual
selection of no less than 8 different parameters.
III. PREPROCESSING ALGORITHM
3.1. The Reflectance Perception Model
This algorithm is makes use of two widely accepted
assumptions about human vision 1) human vision is
mostly sensitive to scene reflectance and mostly
insensitive to the illumination conditions and 2)
human vision responds to local changes in contrast
rather than to global brightness levels. Both these
assumptions have a close relation since local contrast
is given as a function of reflectance. Our main motive
is to find an estimating conditions of L(x; y) such that
when it divides I(x; y) it produces R(x; y) in which
the local contrast is appropriately and accurately
enhanced. In this view R(x; y) takes the place of
perceived sensation, while I(x; y) takes the place of
the input stimulus. L(x; y) is then called perception
gain which maps the input sensation into the
perceived stimulus that is given as
=
(1)
Here in this equation, R is mostly the reflectance of
the scene, and L is mostly the illumination field,
After all, humans perceive reflectance details in both
shadows as well as in bright regions, but they are also
aware of the presence of shadows. To derive our
model we look into evidences form the experimental
technology. The Weber's Law states that the
sensitivity threshold to a small intensity change
increases proportionally to the signal level [12]. This
law follows from the experimentation on brightness
perception that consists of exposing an observer to a
uniform field of intensity I in which a disk is
gradually increased in brightness by a quantity
.The value
from which the observer perceives
the existence of the desk against the background is
called as the brightness discrimination threshold.
Weber concluded that
of intensity values.
is constant for a wide range
Fig 1: Compressive logarithmic mapping emphasizes
changes at low stimulus levels and attenuates changes
at high stimulus levels.
Weber's law gives a theoretical justification for
assuming a logarithmic mapping from input stimulus
to perceived sensation which can be observed from
fig 1. We can see that due to the logarithmic mapping
when the stimulus is weak, for example in deep
shadows, small changes in the input stimulus extract
large changes in perceived sensation. When the
stimulus is strong, small changes in the input
stimulus are mapped to even smaller changes in
perceived sensation. In facts Local variations in the
input stimulus are mapped to the perceived sensation
Variations with the gain
=
Where
which is given as
,
(2)
is the stimulus level in a small
neighborhood
in the input image by comparing
above equations we can finally obtain the model for
the perception gain:
(3)
Where the neighborhood stimulus level is by
definition taken to be the stimulus at point
the
solution for
can be found by minimizing the
equation
J(L)= ʃʃρ ρx,yL-12dxdy+λʃʃ (L2x+L2y)dxdy
(4)
Where the first term drives the solution for following
the perception gain model, while the second term
imposes a smoothness of the constraint here refers
to the image. The parameter
controls the relative
importance of the two terms. The space varying
permeability weight
controls the anisotropic
nature of the smoothing constraint. The EulerLagrange equation for this calculus of variation
problem yields
(5)
On a rectangular lattice, the linear differential
equation becomes
ISSN 2320 –5547
@ 2013 http://www.ijitr.com All rights Reserved.
Page | 208
3. V.Karthikeyan * et al. / (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH
Volume No. 1, Issue No. 2, February - March 2013, 207 - 210
(
Where
)
denotes the pixel grid size and the each
value is taken in the middle of the edge between
the center pixel and each of the corresponding
neighbor. In this formulation
controls the
anisotropic nature of the smoothing by modulating
permeability between pixel neighbors. Multi-grid
algorithms are fairly efficient having complexity. To
get a relative measure of local contrasts that will
equally respect boundaries in shadows and regions
(different illumination conditions) and the equation is
given as
(7)
Where
(a)
(6)
is the weight between two neighboring
pixels whose intensities are
(b)
Fig.2. (a) Image before PCA Fig (b) Image after PCA
Algorithm
SCORE FUSION BASED WEIGHTED SUM
METHOD
There are many ways to score the values of extracted
features of human face, among those the simple and
accurate robust method to combine the scores is
Weighted sum Technique. To compute a weighted
sum as follows:
(7)
and
Where the weight
IV. PRINCIPAL COMPONENT ANALYSIS
BASED FEATURE EXTRACTION
PCA commonly referred to as the use of Eigen faces,
is the technique pioneered by Kirby and Sirivich.
With PCA, the probe and gallery images must be the
same size and must first be normalized to line up the
eyes and mouth of the subjects within the image. The
PCA approach is then used to reduce the dimension
of the data by means of data compression basics and
reveals the most effective low dimensional structure
of facial patterns. This reduction in dimensions
removes information that is not useful and precisely
decomposes the face structure into orthogonal
components known as Eigen faces. Each face image
may be represented as the weighted sum of the Eigen
faces, which are stored in a 1D array. A probe image
is compared against a gallery image by measuring the
distance between their respective feature vectors. The
PCA approach typically requires the full frontal face
to be presented each time; otherwise the image
results in poor performance. The primary advantage
of this technique is that it can reduce the data needed
to identify the individual to 1/1000th of the data
presented.
The application of the PCA algorithm to the Yale
databases illustrates the
improvement in the
accuracy. This can be shown in the figure as
ISSN 2320 –5547
have in the
wi is the amount of confidence we
ith classifier and its score. In this work,
we use 1/EER as a measure of such confidence. Thus,
we have a new score
(8)
The weighted sum method based upon the EER is
heuristic but intuitively appealing and easy to
implement. In addition, this method has the
advantage that it is robust to the difference in
statistics between training data and test data. Even
though the training data and test data have different
statistics, the relative strength of the component
classifiers is less likely to change significantly,
suggesting that the weighting still makes sense.
V. CONCLUSION
Thus a robust face recognition system is being
proposed which is insensitive to illumination
variations. The pre processing algorithm of RPM
states the experimentation on brightness perception
that consists of exposing an observer to a uniform
field of intensity. The PCA based feature extraction
method coveys that it reduces the dimension of the
image by removing the unwanted information.
Finally the score fusion of the extracted features is
done by the weighted sum method. We introduced a
simple and automatic image-processing algorithm for
compensation of illumination-induced variations in
images. At the high level, the algorithm mimics some
aspects of human visual perception. If desired, the
user may adjust a single parameter whose meaning is
intuitive and simple to understand. The algorithm
delivers large performance improvements for
@ 2013 http://www.ijitr.com All rights Reserved.
Page | 209
4. V.Karthikeyan * et al. / (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH
Volume No. 1, Issue No. 2, February - March 2013, 207 - 210
standard face recognition algorithms across multiple
face databases.
VI. EXPERIMENTAL RESULTS
Fig.7.Data base Image
[1]
Fig.3. Test Image
[2]
[3]
[4]
Fig. 4 Preprocessed Image
[5]
[6]
[7]
Fig.5.Preprocessed Image 2
[8]
[9]
[10]
Fig.6.Preprocessed Image 3
ISSN 2320 –5547
VII. REFERENCES
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The FERET evaluation methodology for facerecognition algorithms. IEEE
PAMI 22
(2000) 1090{1104
Blackburn, D., Bone, M., Philips, P.: Facial
Recognition vendor test 2000: evaluation
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Gross, R., Shi, J., Cohn, J.: Quo vadis face
Recognition In: Third Workshop on Empirical
Evaluation Methods in Computer Vision
(2001)
Belhumeur, P.N., Hespanha, J.P., Kriegman,
D.J.: Eigenfaces vs. Fisherfaces: Recognition
using class specific linear Projection IEEE
PAMI 19 (1997)
Belhumeur, P., Kriegman, D.: What is the set
of images of an object under allPossible
Lighting conditions. Int. J. of Computer Vision
28 (1998)
Georghiades, A., Kriegman, D., Belhumeur,
P.: From few to many: Generative models for
recognition under variable pose and
illumination. IEEE PAMI (2001)
Riklin-Raviv, T., Shashua, A.: The Quotient
image:
class-based
re-rendering
and
recognition
with
varying
illumination
conditions. In: IEEE PAMI. (2001)
Georghiades, A., Kriegman, D., Belhumeur,
P.: Illumination cones for recognition Under
variable lighting: Faces. In: Proc. I EEE
Conf. on CVPR. (1998)
Blanz, V., Romdhani, S., Vetter, T.: Face
identi_cation across di_erent poses and
illumination with a 3D morphable model. In:
IEEE Conf. on Automatic Face and Gesture
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Tumblin, J., Turk, G.: LCIS: A boundary
hierarchy for detail-preserving contrast
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