This document contains information about 36 MATLAB projects related to digital image processing and communications. It includes the project codes, titles, and descriptions. The projects cover topics like image recognition, biometrics, medical imaging, steganography, watermarking, and more. It also provides contact information for the company that developed these projects.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
Pantech offers b.tech and m.tech projects for engineering students.We support on IoT, Raspberry pi projects and open cv Image processing, Virtual reality, gesture recognition, segmentation, compression, image enhancement, image retrieval, image fusion, TMS320C5505
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
FPGA Based Pattern Generation and Synchonization for High Speed Structured Li...TELKOMNIKA JOURNAL
Recently, structured light 3D imaging devices have gained a keen attention due to their potential
applications to robotics, industrial manufacturing and medical imaging. Most of these applications require
high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This
paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA
based structured light pattern generation and projector-camera synchronization. Suggested setup reduces
the time for pattern projection and camera triggering to 16msec from 100msec that should be required by
conventional methods.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
Pantech offers b.tech and m.tech projects for engineering students.We support on IoT, Raspberry pi projects and open cv Image processing, Virtual reality, gesture recognition, segmentation, compression, image enhancement, image retrieval, image fusion, TMS320C5505
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...Ravi Kiran B.
Modern perception pipelines in autonomous driving (AD) systems are based on Deep Neural Networks (DNNs) which utilize multiple hyper-parameter configurations and training strategies. Data augmentations is now a well-established training strategy to improve the generalization of DNNs, especially in a low dataset regime. Self-supervised learning and semi-supervised methods depend heavily on data augmentation strategies. In this study we view generalization due to data augmentations training DNNs since they implicitly model the geometric, viewpoint based transformations present on images/pointclouds due to noise, perspective, motion of the ego-vehicle. We shortly review current data augmentation strategies for perception tasks in AD, and recent developments on understanding its effects on model generalization.
In the talk we shall review data augmentation strategies through two case studies:
- Improving model performance of monocular 3D object detection model by using geometry preserving data augmentations on images
- Understand the role of data augmentation in reducing data redundancy and improving label efficiency within an active learning pipeline
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A Blind Multiple Watermarks based on Human Visual Characteristics IJECEIAES
Digital watermarking is an alternative solution to prevent unauthorized duplication, distribution and breach of ownership right. This paper proposes a watermarking scheme for multiple watermarks embedding. The embedding of multiple watermarks use a block-based scheme based on human visual characteristics. A threshold is used to determine the watermark values by modifying first column of the orthogonal U matrix obtained from Singular Value Decomposition (SVD). The tradeoff between normalize crosscorrelation and imperceptibility of watermarked image from quantization steps was used to achieve an optimal threshold value. The results show that our proposed multiple watermarks scheme exhibit robustness against signal processing attacks. The proposed scheme demonstrates that the watermark recovery from chrominance blue was resistant against different types of attacks.
Quality - Security Uncompromised and Plausible Watermarking for Patent Infrin...CSCJournals
The most quoted applications for digital watermarking is in the context of copyright-protection of digital (multi-)media. In this paper we offer a new digital watermarking technique, which pledges both Security and Quality for the image for the Patent protection. This methodology uses tale techniques like Shuffling, Composition & Decomposition, and Encryption & Decryption to record the information of a protected primary image and the allied watermarks. The quadtree can aid the processing of watermark and AES provides added security to information. Besides that, we intend a novel architecture for Patent Protection that holds promise for a better compromise between practicality and security for emerging digital rights management application. Security solutions must seize a suspicious version of the application-dependent restrictions and competing objectives.
Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Tech...csandit
In this paper we present a comparison between stand
ard computer vision techniques and Deep
Learning approach for automatic metal corrosion (ru
st) detection. For the classic approach, a
classification based on the number of pixels contai
ning specific red components has been
utilized. The code written in Python used OpenCV li
braries to compute and categorize the
images. For the Deep Learning approach, we chose Ca
ffe, a powerful framework developed at
“Berkeley Vision and Learning Center” (BVLC). The
test has been performed by classifying
images and calculating the total accuracy for the t
wo different approaches.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
Images are undoubtedly the most efficacious and easiest means of communicating an idea. They are surely an indispensable part of human life .The trend of sharing images of various kinds for example typical technical figures, modern exceptional masterpiece from an artist, photos from the recent picnic to hill station etc, on the internet is spreading like a viral. There is a mandatory requirement for checking the privacy and security of our personal digital images before making them public via the internet. There is always a threat of our original images being illegally reproduced or distributed elsewhere. To prevent the misuse and protect the copyrights, an efficient solution has been given that can withstand many attacks. This paper aims at encoding of the host image prior to watermark embedding for enhancing the security. The fast and effective full counter propagation neural network helps in the successful watermark embedding without deteriorating the image perception. Earlier techniques embedded the watermark in the image itself but is has been observed that synapses of neural network provide a better platform for reducing the distortion and increasing the message capacity.
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
AUTO AI 2021 talk Real world data augmentations for autonomous driving : B Ra...Ravi Kiran B.
Modern perception pipelines in autonomous driving (AD) systems are based on Deep Neural Networks (DNNs) which utilize multiple hyper-parameter configurations and training strategies. Data augmentations is now a well-established training strategy to improve the generalization of DNNs, especially in a low dataset regime. Self-supervised learning and semi-supervised methods depend heavily on data augmentation strategies. In this study we view generalization due to data augmentations training DNNs since they implicitly model the geometric, viewpoint based transformations present on images/pointclouds due to noise, perspective, motion of the ego-vehicle. We shortly review current data augmentation strategies for perception tasks in AD, and recent developments on understanding its effects on model generalization.
In the talk we shall review data augmentation strategies through two case studies:
- Improving model performance of monocular 3D object detection model by using geometry preserving data augmentations on images
- Understand the role of data augmentation in reducing data redundancy and improving label efficiency within an active learning pipeline
IEEE 2012 Projects,academic projects in .net,academic projects in java,b tech mini projects,btech projects,electrical projects for students,electronic engineering final year project,electronic engineering final year projects,electronic final year project,electronics students projects,embedded in chennai,embedded projects chennai,engineering final project,engineering final projects,engineering projects in chennai,engineering projects in java,final year embedded projects,final year engineering projects,final year engineering projects chennai,final year engineering projects in chennai,final year ieee projects chennai,final year it projects,final year project chennai,final year project in chennai,final year project in electronics,final year project of electronics,final year projects for it,final year projects for mca,final year projects in .net,final year projects in chennai,final year projects in electronics,final year projects in embedded systems,final year projects in it,final year projects on embedded systems,final year student project,final year student projects,ieee embedded projects,ieee projects,ieee projects chennai,ieee projects for mca,ieee projects in .net,ieee projects in chennai,ieee projects in java,ieee projects in vlsi,ieee projects on embedded systems,ieee projects titles,ieee students projects,mca academic projects,mca final project,mca final year project,mca final year project in chennai,mca projects,mca projects chennai,mca projects titles,project in vlsi,project of mca,projects for mca,projects in vlsi,student project chennai,student projects in java,vlsi in chennai,year projects,Real Time IEEE Projects,Live Projects,Embedded Live Projects,Power Electronics Projects,Power System Projects,ME Projects,M.Tech Projects,VLSI Final Year projects,Embedded final Year Projects,Real Time Embedded Projects,Real Time Software Projects,Live Java Projects,Dot net Projects in Chennai,.Net Projects,B.tech projects,BE Projects,Real Time Project MBA, Real Time Project BE,Project Work BE,Real Time Project MCA,Real Time Project BE Electronic,Computer Software Training Embedded Systems, Real Time Project,Computer Project Work,Real Time Project IT,Embedded Training,Real Time Project Me,Project Work Ieee Based,Real Time Project B Tech,Project Work MCA,Project Work Computer Science,Project Work M E,Engineering Project Consultants,Real Time Project MSC,Real Time Project M Tech,Real Time Project Bio Medical,Project Consultants For Electronic,Project Work B Tech,Real Time Project BE Electrical,Real Time Project Dot Net,Real Time Project BCA,Project Work M Phil,Real Time Project M Phil,Project Work Embedded System,Real Time Project Embedded System,Project Work M Tech,Project Engineering,Real Time Project Java,Real Time Project PHD,Project Work IT,Real Time Project Networking,Real Time Project BSc,Real Time Project Matlab,Computer Software Training Embedded Network,Project Work Java,Real Time Project Vlsi,Real Time Project Animation,Project Work HTML,Real
A Blind Multiple Watermarks based on Human Visual Characteristics IJECEIAES
Digital watermarking is an alternative solution to prevent unauthorized duplication, distribution and breach of ownership right. This paper proposes a watermarking scheme for multiple watermarks embedding. The embedding of multiple watermarks use a block-based scheme based on human visual characteristics. A threshold is used to determine the watermark values by modifying first column of the orthogonal U matrix obtained from Singular Value Decomposition (SVD). The tradeoff between normalize crosscorrelation and imperceptibility of watermarked image from quantization steps was used to achieve an optimal threshold value. The results show that our proposed multiple watermarks scheme exhibit robustness against signal processing attacks. The proposed scheme demonstrates that the watermark recovery from chrominance blue was resistant against different types of attacks.
Quality - Security Uncompromised and Plausible Watermarking for Patent Infrin...CSCJournals
The most quoted applications for digital watermarking is in the context of copyright-protection of digital (multi-)media. In this paper we offer a new digital watermarking technique, which pledges both Security and Quality for the image for the Patent protection. This methodology uses tale techniques like Shuffling, Composition & Decomposition, and Encryption & Decryption to record the information of a protected primary image and the allied watermarks. The quadtree can aid the processing of watermark and AES provides added security to information. Besides that, we intend a novel architecture for Patent Protection that holds promise for a better compromise between practicality and security for emerging digital rights management application. Security solutions must seize a suspicious version of the application-dependent restrictions and competing objectives.
Corrosion Detection Using A.I : A Comparison of Standard Computer Vision Tech...csandit
In this paper we present a comparison between stand
ard computer vision techniques and Deep
Learning approach for automatic metal corrosion (ru
st) detection. For the classic approach, a
classification based on the number of pixels contai
ning specific red components has been
utilized. The code written in Python used OpenCV li
braries to compute and categorize the
images. For the Deep Learning approach, we chose Ca
ffe, a powerful framework developed at
“Berkeley Vision and Learning Center” (BVLC). The
test has been performed by classifying
images and calculating the total accuracy for the t
wo different approaches.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
Images are undoubtedly the most efficacious and easiest means of communicating an idea. They are surely an indispensable part of human life .The trend of sharing images of various kinds for example typical technical figures, modern exceptional masterpiece from an artist, photos from the recent picnic to hill station etc, on the internet is spreading like a viral. There is a mandatory requirement for checking the privacy and security of our personal digital images before making them public via the internet. There is always a threat of our original images being illegally reproduced or distributed elsewhere. To prevent the misuse and protect the copyrights, an efficient solution has been given that can withstand many attacks. This paper aims at encoding of the host image prior to watermark embedding for enhancing the security. The fast and effective full counter propagation neural network helps in the successful watermark embedding without deteriorating the image perception. Earlier techniques embedded the watermark in the image itself but is has been observed that synapses of neural network provide a better platform for reducing the distortion and increasing the message capacity.
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
ON THE IMAGE QUALITY AND ENCODING TIMES OF LSB, MSB AND COMBINED LSB-MSBijcsit
The Least Significant Bit (LSB) algorithm and the Most Significant Bit (MSB) algorithm are stenography algorithms with each one having its demerits. This work therefore proposed a Hybrid approach and compared its efficiency with LSB and MSB algorithms. The Least Significant Bit (LSB) and Most
Significant Bit (MSB) techniques were combined in the proposed algorithm. Two bits (the least significant bit and the most significant bit) of the cover images were replaced with a secret message. Comparisons were made based on Mean-Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and the encoding time between the proposed algorithm, LSB and MSB after embedding in digital images. The combined
technique produced a stego-image with minimal distortion in image quality than MSB technique independent of the nature of data that was hidden. However, LSB algorithm produced the best stego-image quality. Large cover images however made the combined algorithm’s quality better improved. The combined algorithm had lesser time of image and text encoding. Therefore, a trade-off exists between the encoding time and the quality of stego-image as demonstrated in this work.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Notes for Advanced Image Processing subject. This subject comes under Computer Science for B.E./B.Tech and M.E./M.Tech. students. Hope this will help you.
A Novel Approaches For Chromatic Squander Less Visceral Coding Techniques Usi...IJERA Editor
Recent advances in video capturing and display technologies, along with the exponentially increasing demand of
video services, challenge the video coding research community to design new algorithms able to significantly
improve the compression performance of the current H.264/AVC standard. This target is currently gaining
evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion
models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are
widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index
has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple
compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical
properties for optimization tasks. We propose a perceptual video coding method to improve upon the current
HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain
frame prediction residuals to a perceptually uniform space before encoding.
Based on the residual divisive normalization process, we define a distortion model for mode selection and show
that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion
optimization procedure. We further adjust the divisive normalization factors based on local content of the video
frame. Experiments show that the scheme can achieve significant gain in terms of rate-SSIM performance and
better visual quality when compared with HEVC
A Novel Approaches For Chromatic Squander Less Visceral Coding Techniques Usi...IJERA Editor
Recent advances in video capturing and display technologies, along with the exponentially increasing demand of
video services, challenge the video coding research community to design new algorithms able to significantly
improve the compression performance of the current H.264/AVC standard. This target is currently gaining
evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion
models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are
widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index
has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple
compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical
properties for optimization tasks. We propose a perceptual video coding method to improve upon the current
HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain
frame prediction residuals to a perceptually uniform space before encoding.
Based on the residual divisive normalization process, we define a distortion model for mode selection and show
that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion
optimization procedure. We further adjust the divisive normalization factors based on local content of the video
frame. Experiments show that the scheme can achieve significant gain in terms of rate-SSIM performance and
better visual quality when compared with HEVC
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
1. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2013 - DIGITAL IMAGE PROCESSING
MP01
Context-Dependent Logo Matching and Recognition
Image Recognition, Logo Detection/ Recognition, Scale Invariant Feature Transform, Identify the Original or Fake Products/Certificates/Etc By Using Logos.
MP02
Thermal Imaging as a Biometrics Approach to Facial Signature Authentication
Biometric Recognition (Face), Thermal Imaging, Authentication, Localized Contouring Algorithms, Information Security, Law Enforcement, Smart cards.
MP03
Robust Document Image Binarization Technique for Degraded Document Images
Image Restoration/ Degradation Degraded Document Image Binarization, Used in Restoring Document Images that suffer from Smear, Historical Documents.
MP04
A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference- Histogram Modification
Steganography, Reversible Data Hiding, Difference-Pair-Mapping, Two- Dimensional Difference- Histogram, Military, Medicine and Art.
MP05
Fully Automated Segmentation and Tra c k i n g o f t h e I n t i m a M e d i a T h i c k n e s s i n U l t ra s o u n d V i d e o Sequences of the Common Carotid Artery
Bio-Medical, Image Segmentation, Thresholding, IMC Segmentation and Tracking Algorithm, Diagnosis and Treatment Planning, Early Detection of Blockage thus Avoids Strokes and Heart Attacks.
MP06
Cross-Scale Coefficient Selection for
Volumetric Medical Image Fusion
Bio-Medical, Medical Image Fusion, 3-D Medical Image Fusion, Cross- Scale Fusion Rule, Facilitates Image Processing.
MP07
Automatic Segmentation of Scaling in
2-D Psoriasis Skin Images
Bio-Medical, Image Segmentation, Markov Random Field (MRF), Support Vector Machine (SVM), Pixel Labeling Algorithm, Improvement in Psoriasis Treatment, Used to solve a wide range of Scaling Problems.
MP08
Super pixel Classification based Optic Disc and Optic Cup Segmentation for Glaucoma Screening
Bio-Medical, Fundus Image Processing, Super Pixel Classification, Thresholding, Histogram Equalization, Simple LinearIterative Clustering Algorithm (SLIC), Early Treatment can Prevent Patients getting affected from Severe Condition and progression of Disease can be slowed down.
MP09
Fingerprint Combination for Privacy
Protection
Biometric Recognition, Finger, Reference Points Detection, Two-Stage Fingerprint Matching (Query Minutiae Determination and Matching Score Calculation), Minutiae-based Finger Print Matching Algorithms, Authentication, Security, Biometric Time Attendance Systems, Privacy protection.
2. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2013 - DIGITAL IMAGE PROCESSING
MP10
Vertical-Edge-Based Car-License- Plate Detection Method
Image Segmentation, Adaptive Thresholding (AT), Sobel Operator, Unwanted-Line Elimination Algorithm (ULEA), Vertical Edge Detection Algorithm (VEDA), Payment of Parking Fees, Highway Toll Fees, Traffic Data Collection, Crime Prevention, Vehicle Access Control, and Border Control.
MP11
A Hierarchical Approach to Change Detection in Very High Resolution SAR Images for Surveillance Applications
Image Fusion, Ratio Operator, 2-D Discrete Stationary Wavelet Transform (2D-SWT), Thresholding, The Bayes Decision Rule, Expectation- Maximization (EM) Algorithm, Change Detection (CD) maps, Remote Sensing, Freight Traffic Surveillance.
MP12
A Watermarking Based Medical Image Integrity Control System and an Image Moment Signature for Tampering Characterization
In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations as well as identifying the nature of a global processing an image may have undergone. The proposed integrity analysis process is based on non significant region watermarking with signatures extracted from different pixel blocks of interest and which are compared with the recomputed ones at the verification stage.
MP13
C o m p r e s s i v e F r a m e w o r k f o r
Demosaicing of Natural Images
In this paper, we present compressive demosaicing (CD), a framework for demosaicing natural images based on the theory of compressed sensing (CS). Given sensed samples of an image, CD employs a CS solver to find the sparse representation of that image under a fixed sparsifying dictionary Ψ. As opposed to state of the art CS- based demosaicing approaches, we consider a clear distinction between the interchannel (color) and interpixel correlations of natural images.
MP14
Context-Based Hierarchical Unequal
Merging for SAR Image Segmentation
This paper presents an image segmentation method named Context- based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context.
MP15
Discrete Wavelet Transform and Data Expansion Reduction in Homo- morphic Encrypted Domain
In this paper, we propose a method for implementing discrete wavelet transform (DWT) and multiresolution analysis (MRA) in homomorphic encrypted domain. We first suggest a framework for performing DWT and inverse DWT (IDWT) in the encrypted domain, then conduct an analysis of data expansion and quantization errors under the framework. To solve the problem of data expansion, which may be very important in practical applications,
MP16
Estimating Information from Image Colors An Application to Digital Cameras and Natural Scenes
The colors present in an image of a scene provide information about its constituent elements. But the amount of information depends on the imaging conditions and on how information is calculated. This work had two aims. The first was to derive explicitly estimators of the information available and the information retrieved from the color values at each point in images of a scene under different illuminations.
MP17
General Constructions for Threshold Multiple-Secret Visual Cryptographic Schemes
A conventional threshold (k out of n) visual secret sharing scheme encodes one secret image P into n transparencies (called shares) such that any group of k transparencies reveals P when they are superimposed, while that of less than k ones cannot. We define and develop general constructions for threshold multiple-secret visual cryptographic schemes (MVCSs)
MP18
General Framework to Histogram- Shifting-Based Reversible Data Hiding
In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction.
3. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2013 - DIGITAL IMAGE PROCESSING
MP19
Hyperspectral Imagery Restoration U s i n g N o n l o ca l S p e c t ra l - Sp at i a l Structured Sparse Representation With Noise Estimation
In this paper, we develop a sparse representation based noise reduction method for hyperspectral imagery, which is dependent on the assumption that the non-noise component in an observed signal can be sparsely decomposed over a redundant dictionary while the noise component does not have this property.
MP20
I n t e r a c t i v e S e g m e n t a t i o n f o r Change Detection in Multispectral Remote-Sensing Images
In this letter, we propose to solve the change detection (CD) problem in multitemporal remote-sensing images using interactive segmentation methods. The user needs to input markers related to change and no- change classes in the difference image. Then, the pixels under these markers are used by the support vector machine classifier to generate a spectral-change map.
MP21
I nt ra - a n d - I nte r - C o n st ra i nt - B a s e d V i d e o E n h a n c e m e n t B a s e d o n Piecewise Tone Mapping
In this paper, we propose a new intra-and-inter-constraint-based video enhancement approach aiming to: 1) achieve high intraframe quality of the entire picture where multiple regions-of-interest (ROIs) can be adaptively and simultaneously enhanced, and 2) guarantee the interframe quality consistencies among video frames. We first analyze features from different ROIs and create a piecewise tone mapping curve for the entire frame such that the intraframe
MP22
Latent Fingerprint Matching Using
Descriptor-Based Hough Transform
In this paper, we propose a new fingerprint matching algorithm which is especially designed for matching latents. The proposed algorithm uses a robust alignment algorithm (descriptor-based Hough transform) to align fingerprints and measures similarity between fingerprints by considering both minutiae and orientation field information. To be consistent with the common practice in latent matching (i.e., only minutiae are marked by
MP23
LDFT-Based Watermarking Resilient to Local Desynchron+ization Attacks
In this paper, we present a blind image watermarking resynchronization scheme against local transform attacks. First, we propose a new feature transform named local daisy feature transform (LDFT), which is not only globally but also locally invariable. Then, the binary space partitioning (BSP) tree is used to partition the geometrically invariant LDFT space. In the BSP tree, the location of each pixel is fixed under global transform,
MP24
Local Directional Number Pattern for Face Analysis Face and Expression Recognition
This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts
MP25
Log-Gabor Filters for Image-Based
Vehicle Verification
This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information,
MP26
Noise Reduction Based on Partial- Refe re n c e , D u a l - Tre e C o m p l ex - Wavelet Transform Shrinkage
images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise.
MP27
Query-Adaptive Image Search With
Hash Codes
This paper introduces an approach that enables query-adaptive ranking of the returned images with equal Hamming distances to the queries. This is achieved by firstly offline learning bitwise weights of the hash codes for a diverse set of predefined semantic concept classes. We formulate the weight learning process as a quadratic programming problem that minimizes intra-class distance while preserving inter-class relationship
4. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
IEEE 2013 - DIGITAL IMAGE PROCESSING
CODE
TITLE
DESCRIPTION
MP28
R e v e a l i n g t h e Tr a c e s o f J P E G Compression Anti-Forensics
In this paper, we study the processing chain that arises in the case of JPEG compression anti- forensics. We take the perspective of the forensic analyst, and we show how it is possible to counter the aforementioned anti-forensic method revealing the traces of JPEG compression, regardless of the quantization matrix being used.
MP29
Reversible Data Hiding With Optimal
Value Transfer
In reversible data hiding techniques, the values of host data are modified according to some particular rules and the original host content can be perfectly restored after extraction of the hidden data on receiver side. In this paper, the optimal rule of value modification under a payload- distortion criterion is found by using an iterative procedure, and a practical reversible data hiding scheme is proposed.
MP30
Reversible Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting
In this paper, we propose a new reversible watermarking scheme. One first contribution is a histogram shifting modulation which adaptively takes care of the local specificities of the image content. By applying it to the image prediction- errors and by considering their immediate neighborhood, the scheme we propose nserts ata n textured areas where other methods fail to do so.
MP31
R o b u s t F a c e R e c o g n i t i o n f o r Uncontrolled Pose and Illumination Changes
Face recognition has made significant advances in the last decade, but o b u s t c o m m e r c i a l a p p l i c a t i o n s a r e s t i l l l a c k i n g . C u r r e n t authentication/identification applications are limited to controlled settings, e.g., limited pose and illumination changes, with the user usually aware of being screened and collaborating in the process. Among others, pose and illumination changes are limited.
MP32
Secure Watermarking for Multimedia
Content Protection
The paper illustrates recent results regarding secure watermarking to the signal processing community, highlighting both benefits and still open issues. Secure signal processing, by which indicates a set of techniques able to process sensitive signals that have been obfuscated either by encryption or by other privacy-preserving primitives, may offer valuable solutions to the aforementioned issues.
MP33
V i s u a l l y L o s s l e s s E n c o d i n g Fo r
JPEG2000
This paper presents a method of encoding color images in a visually lossless manner using JPEG2000. In order to hide coding artifacts caused by quantization, visibility thresholds (VTs) are measured and used for quantization of subband signals in JPEG2000. The VTs are experimentally determined from statistically modeled quantization distortion, which is based on the distribution of wavelet coefficients and the dead-zone quantizer of JPEG2000.
MP34
Audio Watermarking Via EMD
In this paper a new adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs).
MP35
A Novel Attention-Based Key-Frame
Determination Method
In this paper, we proposed a novel attention-based key frame Determination system by integrating the object-based visual attention maps and the contextual on-going game outcomes. The decision of the number of key-frames is determined by utilizing the contextual attention score
MP36
A M o d e l - B a s e d S h o t B o u n d a r y Detection Technique Using Frame Transition Parameters
The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.
5. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2013 -
DIGITAL IMAGE PROCESSING
MP37
Video based Tracking, Learning, And Recognition Method For Multiple Moving Objects
This paper proposes a cost reduction method for the MCMC approach by taking moves, i.e., birth and death, out of the iteration loop of the Markov chain when different moving objects interact. For stable and robust tracking, an ellipse model with stochastic model parameters is used. Moreover, our HMM method integrates several different modules in order to cope with multiple discontinuous trajectories.
CM01
Pitch Autopilot Design for Agile Missiles with Uncertain Aerodynamic Coefficients
Communication, Autopilot Design For Agile Missiles, Angles Of Attack, Integrator Back Stepping, H -Norm Minimization, Aerodynamics.
IEEE 2013 - Communication
CM02
Cognitive Radio Networks with Orthogonal Space-Time Block Coding and Multiuser Diversity
Communication, Cognitive Radio, Mobile Communication, OSTBC, Transmit Antenna Selection (TAS), Multiuser Selection.
CM03
Single-Carrier Frequency-Domain
E q u a l i z e r w i t h M u l t i - A n t e n n a
Transmit Diversity
Communication, Wi-Max, Alamouti Signaling, Minimum Mean Square
Error (MMSE), Zero Forcing, Cyclic Delay Diversity (CDD).
CM04
A Novel Phase Offset SLM Scheme for PAPR Reduction in Alamouti MIMO- O F D M S y s t e m s W i t h o u t S i d e Information
Communication, PAPR, SLM, space-frequency block coding (SFBC), video broadcasting and 3GPP, MIMO - OFDM.
CM05
A n I n te r fe re n c e N u l l i n g B a s e d Channel Independent Precoding for M I M O - O F D M S y s t e m s w i t h Insufficient Cyclic Prefix
Communication, Cyclic Prefix (CP), Interference Alignment (IA), CIR, ICI And ISI, LTE or Mobile Communication, MIMO - OFDM.
CM06
Pilot Symbol Parameter Optimization Based on Imperfect Channel State Prediction for OFDM Systems
Communication, WIMAX, Channel Estimation, Minimum Mean Square
Error (MMSE), Pilot Symbol Assisted Modulation (PSAM), OFDM.
CM07
Quantized CSI-Based Tomlinson- Harashima Precoding in Multiuser MIMO Systems
Communication, MIMO Systems, Tomlinson-Harashima Precoding, Quantized Channel State Information (CSI), QR Decomposition, Random Vector Quantization, Zero-Forcing, Wireless Communication.
CM08
Inner Bound on the GDOF of the K- User MIMO Gaussian Symmetric Interference Channel
Communication, MIMO Systems, Generalized Degrees Of Freedom, Interference Channel, Han- Kobayashi Scheme, Wireless Communication, Interference Alignment, Zero-Forcing.
6. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
DIGITAL SIGNAL
CODE
TITLE
DESCRIPTION
IEEE 2013 - Communication
CM09
Spectrum Sensing for Digital Primary Signals in Cognitive Radio: A Bayesian Approach for Maximizing Spectrum Utilization
Communication, Cognitive Radio, Mobile Communication, Bayesian
Detector, PSK modulation.
CM10
Multiple Primary User Spectrum
Sensing in the Low SNR Regime
Communication, Low SNR Regime, Spectrum Sensing, Multipath channels, Multiple primary users, Wireless communication.
CM11
A Peak Power Efficient Cooperative D i ve rs i t y u s i n g S ta r - Q A M w i t h Coherent/Non-coherent Detection
Communication, Power amplifier, QAM, Amplify and-forward (AF), Pair wise error probability (PEP), Satellite communication, DVB-SH.
CM12
V i d e o - B a s e d C r o w d D e n s i t y Estimation and Prediction System for Wide-Area Surveillance
Communication, Crowd Density Estimation, Prediction System, Accumulated Mosaic Image Difference (AMID), GMM, Visual Surveillance, Automated Monitoring Crowd Movements.
CM13
Delay-Limited Source and Channel Coding of Quasi-Stationary Sources over Block Fading Channels: Design and Scaling Laws
Communication, MIMO systems, Outage Capacity, Quasi-Stationary Source, Outage Distortion, Source And Channel Coding, Rate And Power Adaptation, Wireless Communication.
CM14
Interference Alignment Techniques for MIMO Multi-Cell Interfering Broadcast Channels
Communication, Cellular Network, Medium Access Channel-Broadcast
Channel(MAC-BC), Interference Alignment, Beam Forming.
CM15
S p a c e - T i m e C o d e D e s i g n f o r Multiple- Access Channels With Quantized Feedback
Communication, TDMA, Space Time Block Coding (STBC), Full Diversity, Mobile Ad-Hoc Networks
CM16
I n t e r f e r e n c e A l i g n m e n t W i t h
Differential Feed Back
Interference alignment (IA) has been recognized as a promising technique to obtain large multiplexing gain in multiple-input-multiple-output (MIMO) interference channels. Most existing IA schemes require global channel state information (CSI) at the transmitter to design precoding vectors and, thus, result in significant capacity overhead in the feedback link.
DSP01
Wavelet Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems
DSP, Data Security, Encryption And Wavelet Decomposition, Water
Marking, Biomedical Applications.
IEEE 2013 -
PROCESSING
7. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2013 - DIGITAL SIGNAL PROCESSING
DSP02
Audio Watermarking Via EMD
DSP, Water Marking , Empirical Mode Decomposition (EMD), Synchronization Code, IMF, Signal Security And Copy Rights.
DSP03
Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal from Single- Channel ECG: a Comparison
DSP, Encryption and Wavelet Decomposition, Embedding, Empirical Mode Decomposition, Respiratory Sensing System, Biomedical Applications.
DSP04
Cooperative Secure Beam forming for AF Relay Networks With Multiple Eavesdroppers
DSP, Secure Communication, Amplify-And-Forward Relaying, Cooperative
Beam Forming, Physical-Layer Security, Secrecy Rate Maximization
DSP05
Performance of Two Low-Rank STAP Filters in a Heterogeneous Noise
DSP, Space Time Adaptive Processing, Low-rank clutter, Normalized sample Covariance matrix, Perturbation method, SIRV, Sample Covariance Matrix. RADAR
MP01
C h a n ge D e te c t i o n i n Sy nt h e t i c Aperture Radar Images based on Image Fusion and Fuzzy Clustering
In this paper is based on unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved
IEEE 2012 - DIGITAL IMAGE PROCESSING
MP02
Image Quality Assessment Based on
Gradient Similarity
In this proposed scheme considers both luminance and contrast structural changes to effectively assess image quality. The effects of the changes in luminance and contrast structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases have confirmed the effectiveness, robustness, and efficiency of the proposed
MP03
Removing Boundary Artifacts for Real- T i m e I t e r a t e d S h r i n k a g e d e convolution
In this paper we propose a solution to the problem of boundary artifacts appearing in several recently published fast de blurring algorithms based on iterated shrinkage thresholding in a sparse domain and Fourier domain de convolution. Our approach adapts an idea proposed by Reeves for de convolution by the Wiener filter.
MP04
Interpolation-Based Image Super- Resolution using multi surface Fitting
In this paper, we propose a new interpolation-based method of image super- resolution reconstruction. The idea is using Multi surface fitting to take full advantage of spatial structure information. Each site of low- resolution pixels is fitted with one surface, and the final estimation is made by fusing the multi sampling values on these surfaces in the maximum a
MP05
A Semi supervised Segmentation
Model for Collections of Images
In this paper, we consider the problem of segmentation of large collections of images. We propose a semi supervised optimizationmodel that determines an efficient Segmentation of many input images. The Advantages of the model are twofold. The proposed model is effective for segmentation and is computationally efficient
8. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - DIGITAL IMAGE PROCESSING
MP06
Image Fusion Using Higher Order
Singular Value Decomposition
In this paper proposes novel higher order singular value decomposition (HOSVD)- based image fusion. This paper proposes a novel and flexible sigmoid-function-like coefficient-combining scheme, which incorporates the usual choose-max scheme and the weighted average scheme, and easily extends the proposed algorithm to fuse multiple or color images.
MP07
Blind Separation of Image Sources via
Adaptive Dictionary Learning
In this paper, we address fail to successfully recover the sources problem and attempt to give a solution via fusing the dictionary Learning into the source separation. The proposed algorithm is designed to adaptively learn the dictionaries from the mixed images within the source separation Process. In the proposed hierarchical method, a local dictionary is adaptively
MP08
A N e w M e t h o d f o r C r o s s - Normalization and multi temporal Visualization of SAR Images for the Detection of Flooded Areas
This paper is based on multi temporal synthetic aperture radar (SAR) images. Cross-calibration/normalization is proposed to solve this problem. This, in turn, facilitates image enhancement and the numerical comparison of different image takes together with data fusion and Visualization processes.
MP09
Human Identification Using Finger
Images
In this proposed system simultaneously acquires the finger-vein and low- resolution fingerprint images and combines these two evidences using a novel score-level Combination strategy. Our finger-vein identification approach utilizes peg-free and more user-friendly unconstrained imaging. We develop and investigate two new score- level combinations, i.e., holistic and nonlinear fusion,
MP15
Scalable Coding of Encrypted images
In this paper proposes a novel scheme of Scalable coding for encrypted images. In the encryption phase, the original pixel values are masked by a modulo-256 addition with pseudorandom numbers that are derived from a secret key. After decomposing the encrypted data into a down sampled sub image and several data sets with a multiple-resolution construction At the receiver side, the quantized coefficients can be used to reconstruct
MP18
No stationary Harmonic Modeling for
ECG Removal in Surface EMG Signals
We present a compact approach for mitigating the presence of (ECG) in surface (EMG) signals by Means of time-variant harmonic modeling of the cardiac artifact. Once the model parameters (Polynomial coefficients) are estimated, the ECG signal component is generated and subtracted from the mixture in order to obtain the EMG
MP19
An Ensemble-Based System for Micro aneurysm Detection and Diabetic Retinopathy Grading
We propose an ensemble-based framework to improve micro aneurysm detection. We provide an ensemble creation framework to select the best combination. An exhaustive Quantitative analysis is also given to prove the superiority of our approach over individual algorithms. We also investigate the grading performance of our method, which is proven to be competitive with Other screening systems.
MP20
A B l u r - r o b u s t D e s c r i p t o r w i t h
Applications to Face Recognition
Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition, by a fusion of image-formation models, and differential geometric tools.
MP21
A Complete Processing Chain for Shadow Detection and Re- construction in VHR Images
This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine
9. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - DIGITAL IMAGE PROCESSING
MP22
A Generalized Logarithmic Image Processing Model Based on the Giga vision Sensor Model
In this paper, by studying these two seemingly unrelated Models, we develop a generalized LIP (GLIP) model. With the LIP model being its special case, the GLIP model not only provides new insights into the LIP model but also defines new image representations and operations for solving general image processing problems that are not necessarily Related to the GVS. A new parametric LIP model is also developed.
MP23
A Novel Data Embedding Method
Using Adaptive Pixel Pair Matching
This paper proposes a new data-hiding method based on pixel pair matching (PPM). The basic idea of PPM is to use the values of pixel pair as a reference coordinate, and search a coordinate in the neighborhood set of this pixel pair according to a given message digit.learned for each source along with Separation. This process improves the quality of source separation even in noisy
MP24
A Primal–Dual Method for Total- Variation-Based Wavelet Domain In painting
Loss of information in a wavelet domain can occur during storage or transmission when the images are formatted and stored in terms of wavelet coefficients. This calls for image in painting in wavelet domains. In this paper, a variational approach is used to formulate the reconstruction problem. We propose a simple but very efficient iterative scheme to calculate an Optimal solution and prove its convergence.
MP25
BM3D Frames and Variation al Image
Deblurring
In this paper, we construct analysis and synthesis frames, formalizing BM3D image modeling, and use these frames to develop novel iterative deblurring algorithms. We consider two different formulations of the deblurring problem, i.e., one given by the minimization of the single- objective function and another based on the generalized Nash equilibrium (GNE) balance of two objective functions.
MP26
Color Local Texture Features for Color
Face Recognition
This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face Region.
MP27
G a u s s i a n - M i x t u r e - M o d e l - B a s e d Spatial Neighborhood Relationships for Pixel Labeling Problem
In this paper, we present a new algorithm for pixel labeling and image segmentation based on the standard Gaussian mixture model (GMM). Unlike the standard GMM where pixels themselves are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account, the proposed method incorporates this spatial relationship into the standard GMM.
MP28
Image Prediction Based on Neighbor- Embedding Methods
This paper describes two new intra image prediction methods based on two data dimensionality reduction methods: Nonnegative matrix factorization (NMF) and locally linear embedding. These two methods aim at approximating a block to be predicted in the image as a linear combination of -nearest neighbors Determined on the known pixels in a causal neighborhood of The input block.
MP29
Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic Seizures
In this paper, we consider a novel low-complexity real-time image processing-based approach to the detection of Neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal Representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body by evaluating the periodicity of the extracted average
MP30
Monotonic Regression: A New Way for Correlating Subjective and Objective Ratings in Image Quality Research
To assess the performance of image quality metrics (IQMs), some regressions, such as logistic regression and polynomial regression, are used to correlate objective ratings with subjective scores. However, some defects in optimality are shown in these regressions. In this correspondence, monotonic regression (MR) is found to be an effective correlation method in the performance assessment of IQMs.
10. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - DIGITAL IMAGE PROCESSING
MP31
Patch-Based Near-Optimal Image
Denoising
In this paper, we propose a denoising method motivated by our previous analysis of the performance bounds for image denoising. Insights from that study are used here to derive a high-performance practical denoising algorithm.A patch-based Wiener filter that exploits patch redundancy for image denoising. Our framework uses both geometrically and photometrically similar patches to estimate the different filter parameters.
MP32
Performance Analysis of a Block- Neighborhood-Based Self-Recovery Fragile Watermarking Scheme
In this paper,we present the performance analysis of a self-recovery fragile watermarking scheme using block-neighborhood tamper characterization. This method uses a pseudorandom Sequence to generate the nonlinear block-mapping and employs an optimized neighborhood characterization method to detect the tampering. Performance of the proposed method and its resistance to malicious attacks are analyzed.
MP33
Semi supervised Biased Maximum Margin Analysis for Interactive Image Retrieval
With many potential practical applications, content- based image retrieval (CBIR) has attracted substantial attention during the past few years. A variety of relevance feedback (RF) schemes have been developed as a powerful tool to bridge the semantic gap between low-level visual features and high-level semantic concepts, and thus to improve the performance of CBIR systems.
MP34
Separable Reversible Data Hiding in
Encrypted Image
This work proposes a novel scheme for separable reversible data hiding in encrypted images. In the first phase, a content owner encrypts the original uncompressed image using an encryption key. Then, a data-hider may compress the least significant bits of the encrypted image using a data- hiding key to create a sparse space to accommodate some additional
MP35
Automatic Image Equalization and C o n t r a s t E n h a n c e m e n t U s i n g Gaussian Mixture Modeling
In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals.
MP36
Active Curve Recovery of Region
Boundary Patterns
This study investigates the recovery of region boundary patterns in an image by a variational level set method which drives an active curve to coincide with boundaries on which a feature distribution matches a reference distribution. We formulate the scheme for both the Kullback- Leibler and the Bhattacharyya similarities,
MP37
Adaptive Perona–Malik Model Based on the Variable Exponent for Image Denoising
This paper introduces a class of adaptive Perona–Malik (PM) diffusion, which combines the PM equation with the heat equation. The PM equation provides a potential algorithm for image segmentation, noise removal, edge detection, and image enhancement.
MP38
A d a p t i ve S te ga n a l ys i s o f L e a st S i g n i f i ca nt B i t Re p l a c e m e nt i n Grayscale Natural Images
This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always Independent of the unknown image parameters, while ensuring a high probability of hidden bits detection.
MP39
Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval
In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference.
11. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - DIGITAL IMAGE PROCESSING
MP40
L o w D i s t o r t i o n Tr a n s f o r m f o r
Reversible Watermarking
This paper proposes a low-distortion transform for prediction-error expansion reversible watermarking. The transform is derived by taking a simple linear predictor and by embedding the expanded prediction
error not only into the current pixel but also into its prediction context.
MP41
PDE-Based Enhancement of Color
Images in RGB Space
A novel method for color image enhancement is proposed as an extension of the scalar-diffusion–shock-filter coupling model, where noisy and blurred images are de noised and sharpened. The proposed model is based on using the single vectors of the gradient magnitude and the second derivatives as a manner to relate different color components of the image. Situations.
MP42
A n A p p r o a c h F o r I r i s P l a n t
Classification Using Neural Network
This paper is related to the use of multi layer feed-forward neural networks (MLFF) and back propagation algorithm towards the identification of IRIS plants on the basis of sepal length, sepal width, petal length, and petal width. A variety of constructive neural-network learning algorithms have been proposed for solving the general
MP43
Edge Strength Filter Based Color Filter
Array Interpolation
The basis of the proposed System is the observation that the constant color difference assumption tends to fail across edges. If one can effectively utilize edge information to avoid averaging non-correlated color differences, demosaicing performance could increase dramatically.
MP44
P o w e r - C o n s t r a i n e d C o n t r a s t Enhancement for Emissive Displays Based on Histogram Equalization
We propose a PCCE algorithm for emissive displays based on HE. First, we develop a histogram modification (HM) scheme, which reduces large histogram values to alleviate the contrast overstretching of the conventional HE technique. Then, we make a power-consumption model for emissive displays and formulate an objective function, consisting of the histogram-equalizing term and the power terms.
DSP01
Finitely Supported L2- Optimal
Kernels for Digital Signal Interpolation
In this project We derive a new family of unconstrained, finitely supported L2- optimal interpolation kernels HL(x), and compare their properties to the previously Known results. Our research demonstrates that L2 -optimal kernels provide superior
IEEE 2012 - DIGITAL SIGNAL PROCESSING
DSP02
Minimum Euclidean Distance Based Pre coders for MIMO Systems Using Rectangular QAM Modulations
In this paper, an efficient pre coder is designed that maximizes the minimum Distance of two received vectors is studied. This criterion leads to a non diagonal Pre coding scheme and allows achieving a full diversity order. So we propose herein a general form of minimum Euclidean distance based Pre coders for all
DSP03
Global Stabilization of the Least
Mean Fourth Algorithm
In this project fully deals with stability of the least mean fourth algorithm. This is achieved by normalizing the weight
vector update term by a term that is fourth order in the regress or and second order in the estimation error.
DSP04
Derivation of the Bias of the Normalized Sample Covariance Matrix in a Heterogeneous Noise With Application to Low Rank STAP Filter
In this project, we have developed a low rank (LR) spatiotemporal adaptive processing (STAP) filter when the disturbance is modeled as the sum of a low rank spherically invariant random vector (SIRV) clutter and a zero mean white Gaussian noise. This LR-STAP filter is built from the normalized sample covariance matrix (NSCM) and exhibits good robustness properties to secondary data contamination by target components.
12. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - DIGITAL SIGNAL PROCESSING
DSP05
Opportunistic Distributed Space- T i m e C o d i n g f o r D e c o d e - a n d - Forward Cooperation Systems
In this paper, we consider a decode-and- forward (DF) cooperation system consisting of two cooperative users in sending their information to a common destination, for which the distributed space-time coding (DSTC) is applied in an opportunistic manner, called opportunistic DSTC (O-DSTC), depending on whether the two users succeed in decoding each other's information or not.
DSP06
Fast Algorithms for Low-Delay SBR Filter banks in MPEG-4 AAC-ELD
In this paper, we propose fast algorithms for computing low delay SBR analysis and synthesis filter banks in AAC-ELD. Our algorithms are derived by establishing a mapping between SBR matrix operation in AAC-ELD and discrete cosine transform of type-IV (DCT-IV). We then further isolate leading factors in DCT-IV by converting it to type-II DCT (DCT-II), and merge these factors with factors in a window
DSP07
Interference Suppression Strategy for
Cell-Edge Users in the Downlink
In this paper we focus on the cell-edge users whose performance is severely limited by the interfering signals of diverse rates and strengths. In contrast to the suboptimal singleuser detection, we propose an interference suppression strategy based on a low complexity matched filter (MF) based receiver. This proposed receiver exploits the structure of dominant interference in the detection process, instead of assuming it to be Gaussian and merging it in noise.
DSP08
Range Side lobe Reduction Filter Design for Binary Coded Pulse Compression System
In proposed system A new filter structure is introduced here to suppress the side lobes of radar signals that result from standard matched filtering. The proposed filter is applicable for any type of binary coding signals. Several techniques are used to calculate the filter coefficients such as Wiener filter technique, Lagrange multiplier method, and linear programming (LP) algorithm.
CM01
O n t h e M i n i m u m D i f f e r e n t i a l F e e d b a c k f o r T i m e - C o r r e l a t e d Rayleigh Block-Fading
Channels
In this paper, we investigate the differential channel state information (CSI) feedback problem for a general multiple input multiple output (MIMO) system over time-correlated Rayleigh block-fading channels.
IEEE 2012 - Communication
CM02
O p t i m a l C h a n n e l a n d R e l a y Assignment in OFDM-Based Multi- R e l a y M u l t i - P a i r T w o - W a y Communication Networks
This paper considers a wireless relay network where multiple user pairs conduct bidirectional communications via multiple relays based on orthogonal frequency-div multiplexing (OFDM) transmission.
CM03
On Performance Improvement of Wireless Push Systems via Smart Antennas
In this paper, we propose an adaptive smart antenna based wireless push system where the beamwidth of each smart antenna is altered based on the current placement of clients within the system area.
CM04
Secure Communication in the
Low-SNR Regime
In this project Energy efficiency is analyzed by finding the minimum bit energy required for secure and reliable communications, and the wideband slope.
CM05
On Optimal Front-End Filter for Single-User Detection in IR- UWB Systems
In this paper, we show that by employing a new front-end filter at the receiver, the non- whiteness of the MAI can be exploited to improve the performance of the single-user Detector.
13. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - Communication
CM06
C o o p e ra t i v e S p e c t r u m S h a r i n g Protocol with Selective Relaying System
In this paper, we propose a two-phase protocol based on cooperative relaying for a secondary system to achieve spectrum access along with a selective relaying Primary system.
CM07
Diversity Gain and Outage Probability for MIMO Free-Space Optical Links with Misalignment
A novel statistical channel model for multiple-input multiple-output (MIMO) free-space optical (FSO) communication systems impaired by atmospheric and Misalignment fading is developed. A slow- fading channel model is considered and the outage probability is derived as a Performance measure.
CM08
Uncoordinated Beam forming for
Cognitive Networks
In this paper, we propose jointly-optimized Beam forming algorithms for cognitive networks to maximize the achievable rates, where primary and cognitive users share the same spectrum and are equipped with Multiple antennas.
CM09
Asymptotic Capacity of Large Relay
Networks with Conferencing Links
In this project, we consider a half- duplex large relay network, consisting of one source-destination pair and N relay nodes, each of which is connected with a subset of the other relays via signal-to-noise ratio (SNR)limited out- of-band conferencing links
CM10
Two Useful Bounds Related to Weighted Sums of Rayleigh Random Va r i a b l e s w i t h A p p l i c a t i o n s t o Interference Systems
In this letter, we derive an upper bound on the distribution of the ratio of a Rayleigh faded signal to a sum of weighted Rayleigh RVs plus a nonnegative constant, dubbed the generalized ratio(GR).
CM11
Performance Analysis over Slow Fading Channels of a Half- Duplex Single-Relay Protocol: Decode or Quantize and Forward
In this work, a static relaying protocol, called Decode or Quantize and Forward (DoQF), is introduced for half duplex single-relay networks, and its performance is studied in the context of communications over slow fading wireless channels.
CM12
Analytic Framework for the Effective
Rate of MISO Fading Channels
In this paper, we pursue a detailed effective rate analysis of Nakagami-m, Rician and generalized-K multiple input single-output (MISO) fading channels by deriving new, analytical expressions for their exact effective rate.
CM13
Diversity-Multiplexing Tradeoff of MIMO Multiple-Access Systems with Successive Cancellation Receivers Having Imperfect Cancellation
In order to analyze the asymptotic performance of each user in a practical multiple-access system, we derive the per- user DMT considering error propagation due to imperfect cancellation of the SC process.
CM14
Diversity-Multiplexing-Delay Tradeoff in Selection Cooperation Networks with ARQ
In this paper, we combine the distributed selection cooperation protocols with ARQ mechanism to develop more powerful cooperative schemes for delay-tolerant wireless networks and analyze their performance from the perspective of diversity multiplexing-delay (D-M-D) tradeoff.
14. MATLAB
Contact: 9158547792
Email: projects@candorminds.com, info@candorminds.com, www.candorminds.com
CODE
TITLE
DESCRIPTION
IEEE 2012 - Communication
CM15
An Analysis of the Bidirectional LMS Algorithm over Fast-Fading Channels
We analyze the tracking performance of the bidirectional LMS algorithm by deriving a novel step-size dependent steady-state MSE and optimal step-size expressions over fast frequency- selective time-varying channels.
CM16
SNR Performance Analysis of Rake
Receiver for WCDMA
In this paper WCDMA was designed to meet such high data rate up to 3.84
MCPS and high bandwidth. The higher bandwidth causes a more channel distortion. It is modeled as a multi-path phase and amplitude distortion. Two major effects resulting from multipath propagation first, the signal energy may arrive at the receiver across clearly distinguishable time instants and second is fast fading.