International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Adapter Wavelet Thresholding for Image Denoising Using Various Shrinkage Unde...muhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
www.softroniics.com
Real Time Human Posture Detection with Multiple Depth SensorsWassim Filali
This thesis presents a comprehensive study of the state-of-the-art in human posture reconstruction, its contexts, and associated applications. The underlying research focuses on utilization of computer vision techniques for human activity recognition based on embedded system technologies and intelligent camera systems. It also focuses on human posture reconstruction as it plays a key role in subsequent activity recognition. In this work, we have relied on the latest technological advances in sensor technology, specifically on the advent of Kinect, an RGB-D sensor from Microsoft, to realize a low-level sensor fusion algorithm to fuse the outputs of multiple depth sensors for human posture reconstruction.
In this endeavor, the different challenges encountered are: (1) occlusions when using a single sensor; (2) the combinatorial complexity of learning a high dimensional space corresponding to human postures; and finally, (3) embedded systems constraints. The proposed system addresses and consequently resolves each of these challenges.
The fusion of multiple depth sensors gives better result than individual sensors as the fusion alleviates the majority of occlusions by resolving many incoherencies thus by guaranteeing improved robustness and completeness on the observed scene. In this manuscript, we have elaborated the low-level fusion strategy which makes up the main contribution of this thesis. We have adopted a learning technique based on decision forests. Our algorithm is applied on our own learning dataset acquired with our multi-platform kinect coupled to a commercial motion capture system.
The two main principal features are sensor data fusion and supervised learning. Specifically, the data fusion technique is described by acquisition, segmentation, and voxelization which generates a 3D reconstruction of the occupied space. The supervised learning is based on decision forests and uses appropriate descriptors extracted from the reconstructed data. Various experiments including specific parameter learning (tuning) runs have been realized.
Qualitative and quantitative comparative human articulation reconstruction precision evaluations against the state-of-the-art strategies have also been carried out.
The different algorithms have been implemented on a personal computer environment which helped to analyze the essential parts that needs hardware embedded integration. The hardware integration consisted of studying and comparing multiple approaches. FPGA is a platform that meets both the performance and embeddability criteria as it provides resources that reduce CPU cost. This allowed us to make a contribution which constitutes a hierarchically prioritized design via a layer of intermediary modules. Comparative studies have also been done using background subtraction implementation as a benchmark integrated on PC, GPU, and FPGA (the FPGA implementation has been presented in detail).
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
Adaptive denoising technique for colour imageseSAT Journals
Abstract
In digital image processing noise removal or noise filtering plays an important role, because for meaningful and useful processing images should not be corrupted by noises. In recent years, high quality televisions have become very popular but noise often affects TV broadcasts. Impulse noise corrupts the video during transmission and acquisition of signals. A number of denoising techniques have been introduced to remove impulse noise from images . Linear noise filtering technique does not work well when the noise is non-adaptive in nature and hence a number of non-linear filtering technique where introduced. In non-linear filtering technique, median filters and its modifications where used to remove noise but it resulted in blurring of images. Therefore here we propose an adaptive digital signal processing approach that can efficiently remove impulse noise from colour image. This algorithm is based on threshold which is adaptive in nature. This algorithm replaces the pixel only if it is found to be noisy pixel otherwise the original pixel is retained thus it results a better filtering technique when compared to median filters and its modified filters.
Keywords: impulse noise, Adaptive threshold, Noise detection, colour video
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Comparison of Wavelet Watermarking Method With & without Estimator Approachijsrd.com
In this paper we propose an Estimator approach with wavelet watermarking method which is capable to hide watermark in the host image based on wavelet domain technique. Using the Estimator approach the proposed technique becomes robust against different noise attacks. For the evaluation of Imperceptibility & Robustness of the proposed method we have calculated basic statistical parameters. We have tested watermarked image against different noise attacks at different noise densities. Due to the use of estimator the perceptible quality of extracted image is better though the image is degraded by high density noise.
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.
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Using facial coding technology to capture emotions on mobile - Millward BrownMerlien Institute
Presented by Pankaj Jha, Director of Global Innovation, Millward Brown
at Market Research in the Mobile World Asia-Pacific
30-31 January 2013, Kuala Lumpur, Malaysia
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://www.mrmw.net
Face Value: capturing fleeting emotions through facial coding to add depth to...Merlien Institute
Presented by Pankaj Jha, Director - Global Innocation, Millward Brown
at Qualitative360 Asia 2013
19-21 November 2013, Singapore
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://qual360.com/
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Adapter Wavelet Thresholding for Image Denoising Using Various Shrinkage Unde...muhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
www.softroniics.com
Real Time Human Posture Detection with Multiple Depth SensorsWassim Filali
This thesis presents a comprehensive study of the state-of-the-art in human posture reconstruction, its contexts, and associated applications. The underlying research focuses on utilization of computer vision techniques for human activity recognition based on embedded system technologies and intelligent camera systems. It also focuses on human posture reconstruction as it plays a key role in subsequent activity recognition. In this work, we have relied on the latest technological advances in sensor technology, specifically on the advent of Kinect, an RGB-D sensor from Microsoft, to realize a low-level sensor fusion algorithm to fuse the outputs of multiple depth sensors for human posture reconstruction.
In this endeavor, the different challenges encountered are: (1) occlusions when using a single sensor; (2) the combinatorial complexity of learning a high dimensional space corresponding to human postures; and finally, (3) embedded systems constraints. The proposed system addresses and consequently resolves each of these challenges.
The fusion of multiple depth sensors gives better result than individual sensors as the fusion alleviates the majority of occlusions by resolving many incoherencies thus by guaranteeing improved robustness and completeness on the observed scene. In this manuscript, we have elaborated the low-level fusion strategy which makes up the main contribution of this thesis. We have adopted a learning technique based on decision forests. Our algorithm is applied on our own learning dataset acquired with our multi-platform kinect coupled to a commercial motion capture system.
The two main principal features are sensor data fusion and supervised learning. Specifically, the data fusion technique is described by acquisition, segmentation, and voxelization which generates a 3D reconstruction of the occupied space. The supervised learning is based on decision forests and uses appropriate descriptors extracted from the reconstructed data. Various experiments including specific parameter learning (tuning) runs have been realized.
Qualitative and quantitative comparative human articulation reconstruction precision evaluations against the state-of-the-art strategies have also been carried out.
The different algorithms have been implemented on a personal computer environment which helped to analyze the essential parts that needs hardware embedded integration. The hardware integration consisted of studying and comparing multiple approaches. FPGA is a platform that meets both the performance and embeddability criteria as it provides resources that reduce CPU cost. This allowed us to make a contribution which constitutes a hierarchically prioritized design via a layer of intermediary modules. Comparative studies have also been done using background subtraction implementation as a benchmark integrated on PC, GPU, and FPGA (the FPGA implementation has been presented in detail).
Pattern Approximation Based Generalized Image Noise Reduction Using Adaptive ...IJECEIAES
The problem of noise interference with the image always occurs irrespective of whatever precaution is taken. Challenging issues with noise reduction are diversity of characteristics involved with source of noise and in result; it is difficult to develop a universal solution. This paper has proposed neural network based generalize solution of noise reduction by mapping the problem as pattern approximation. Considering the statistical relationship among local region pixels in the noise free image as normal patterns, feedforward neural network is applied to acquire the knowledge available within such patterns. Adaptiveness is applied in the slope of transfer function to improve the learning process. Acquired normal patterns knowledge is utilized to reduce the level of different type of noise available within an image by recorrection of noisy patterns through pattern approximation. The proposed restoration method does not need any estimation of noise model characteristics available in the image not only that it can reduce the mixer of different types of noise efficiently. The proposed method has high processing speed along with simplicity in design. Restoration of gray scale image as well as color image has done, which has suffered from different types of noise like, Gaussian noise, salt &peper, speckle noise and mixer of it.
Adaptive denoising technique for colour imageseSAT Journals
Abstract
In digital image processing noise removal or noise filtering plays an important role, because for meaningful and useful processing images should not be corrupted by noises. In recent years, high quality televisions have become very popular but noise often affects TV broadcasts. Impulse noise corrupts the video during transmission and acquisition of signals. A number of denoising techniques have been introduced to remove impulse noise from images . Linear noise filtering technique does not work well when the noise is non-adaptive in nature and hence a number of non-linear filtering technique where introduced. In non-linear filtering technique, median filters and its modifications where used to remove noise but it resulted in blurring of images. Therefore here we propose an adaptive digital signal processing approach that can efficiently remove impulse noise from colour image. This algorithm is based on threshold which is adaptive in nature. This algorithm replaces the pixel only if it is found to be noisy pixel otherwise the original pixel is retained thus it results a better filtering technique when compared to median filters and its modified filters.
Keywords: impulse noise, Adaptive threshold, Noise detection, colour video
Deep learning for image super resolutionPrudhvi Raj
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Comparison of Wavelet Watermarking Method With & without Estimator Approachijsrd.com
In this paper we propose an Estimator approach with wavelet watermarking method which is capable to hide watermark in the host image based on wavelet domain technique. Using the Estimator approach the proposed technique becomes robust against different noise attacks. For the evaluation of Imperceptibility & Robustness of the proposed method we have calculated basic statistical parameters. We have tested watermarked image against different noise attacks at different noise densities. Due to the use of estimator the perceptible quality of extracted image is better though the image is degraded by high density noise.
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.
Whitepaper: Image Quality Impact of SmartGrid Processing in Bedside Chest Ima...Carestream
Scattered radiation is known to degrade image quality in
diagnostic X-ray imaging. A new image processing tool, SmartGrid, has been developed that compensates for the effects of X-ray scatter in an image, and produces results comparable to those of a physical antiscatter grid. Read the white paper to learn more.
Using facial coding technology to capture emotions on mobile - Millward BrownMerlien Institute
Presented by Pankaj Jha, Director of Global Innovation, Millward Brown
at Market Research in the Mobile World Asia-Pacific
30-31 January 2013, Kuala Lumpur, Malaysia
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://www.mrmw.net
Face Value: capturing fleeting emotions through facial coding to add depth to...Merlien Institute
Presented by Pankaj Jha, Director - Global Innocation, Millward Brown
at Qualitative360 Asia 2013
19-21 November 2013, Singapore
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://qual360.com/
Facial Coding: The Missing Piece to Your Content Marketing StrategyYared Akalou
Before your team launches a marketing campaign, they need to truly understand what drivers motivate people to act. Launching campaigns only to realize the messaging failed to connect with intended audience can, in an instant, reverse brand perception and loyalty. So you want to connect with your customers at the right time with the right message?
Facial coding is the missing piece to your content marketing strategy.
Key takeaways:
Understand the science behind facial coding and expression analysis
How to measure emotions to learn what your customers truly feel about your messaging before campaign launches
How to deliver content that elicits the right emotions which drives customer action
Online Eye Tracking and Facial Coding SolutionsEyeSee Research
EyeSee is revolutionizing the market research industry! Our unique platform for tracking peoples’ eyes and facial expressions with their laptop and webcam at home enables delivery of fast, cost-effective and actionable insights. Learn how to increase impact of marketing communication by having insight in your customers' perspective.
Our faces reveal multitudes about what we are thinking, feeling, intending. A slack jaw hints that we’ve been surprised, flared nostrils suggest hostility. Drooping eyelids indicate sadness or perhaps just … exhaustion. This is to say nothing of the powerful messages communicated by the face in the embarrassed downward glance, the flirtatious “look away,” or the piercing stare.
Because our facial expressions are reliable indicators of our true emotional state, they are a liespotter’s best friend. While not every lift of the eyebrows or tightening of the lips will yield an infallible “truth” or “lie” verdict, trained liespotters can glean much from careful study of the face.
This presentation is based on the book liespotting - proven techniques to detect deception by Pamela Meyer, founder and CEO of Calibrate, a leading deception detection training company.
Micro Expressions are brief, involuntary facial expressions shown on the face of humans according to emotions experienced.
They occur when a person is consciously trying to conceal all signs of how he or she is feeling, or when a person does not consciously know how he or she is feeling.
In this deck, a brief history of micro expressions is introduced, along with a detailed analysis of the 7 universal facial expressions that could be found in almost anyone walking on this Earth.
Image archiving and preservation finds extensive application in culture heritage murals. The study of cultural heritage is of the extreme importance at national and international levels. Not only global organizations like UNESCO but also museums, libraries, culture, temples and private initiatives are working in these directions. During the last three decades, researchers in the field of imaging discipline have started to contribute an increasing set of algorithms for cultural heritage; in that way providing indispensable support to these efforts. A better comparison of the different compression methods presented in this proposed work for culture Heritage mural images. Compression methods usually applied some method to reduce the number of components within each spectrum. The effectiveness of mural image archiving and preservation is analyzed based on 2-D wavelets filtering. The optimum algorithm is also found based on the results.
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
A MEDIAN BASED DIRECTIONAL CASCADED WITH MASK FILTER FOR REMOVAL OF RVINijistjournal
In this paper A Median Based Directional Cascaded with Mask (MBDCM) filter has been proposed, which is based on three different sized cascaded filtering windows. The differences between the current pixel and its neighbors aligned with four main directions are considered for impulse detection. A direction index is used for each edge aligned with a given direction. Minimum of these four direction indexes is used for impulse detection under each masking window. Depending on the minimum direction indexes among these three windows new value to substitute the noisy pixel is calculated. Extensive simulations showed that the MBDCM filter provides good performances of suppressing impulses from both gray level and colored benchmarked images corrupted with low noise level as well as for highly dense impulses. MBDCM filter gives better results than MDWCMM filter in suppressing impulses from highly corrupted digital images.
A MEDIAN BASED DIRECTIONAL CASCADED WITH MASK FILTER FOR REMOVAL OF RVINijistjournal
In this paper A Median Based Directional Cascaded with Mask (MBDCM) filter has been proposed, which is based on three different sized cascaded filtering windows. The differences between the current pixel and its neighbors aligned with four main directions are considered for impulse detection. A direction index is used for each edge aligned with a given direction. Minimum of these four direction indexes is used for impulse detection under each masking window. Depending on the minimum direction indexes among these three windows new value to substitute the noisy pixel is calculated. Extensive simulations showed that the MBDCM filter provides good performances of suppressing impulses from both gray level and colored benchmarked images corrupted with low noise level as well as for highly dense impulses. MBDCM filter gives better results than MDWCMM filter in suppressing impulses from highly corrupted digital images.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
발표자: 김준태 (KAIST 박사과정)
발표일: 2018.10
Voice activity detection (VAD) and speech enhancement (SE) are important front-end technologies for noise robust speech recognition system.
From incoming noisy signal, VAD detects the speech signal only and SE removes the noise signal while conserving the speech signal.
For VAD and SE, this presentation will cover the traditional methods, deep learning based methods, and our papers as follows:
1. J. Kim and M. Hahn, "Voice Activity Detection Using an Adaptive Context Attention Model," in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1181-1185, Aug. 2018.
2. J. Kim and M. Hahn, "Speech Enhancement Using a Two Step Network," submitted to IEEE Signal Processing Letters, 2018.
Also, this presentation will briefly introduce some experimental results in real-world environment (far-field, noisy environment), conducted on the embedded board.
For VAD,
Traditional VAD methods.
Deep learning based VAD methods.
Paper presentation: J. Kim and M. Hahn, "Voice Activity Detection Using an Adaptive Context Attention Model," in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1181-1185, Aug. 2018.
End point detection based on VAD.
Experimental results of DNN-EPD on embedded board in real-world environment.
For SE,
Traditional SE methods.
Deep learning based SE methods.
Paper presentation: J. Kim and M. Hahn, "Speech Enhancement Using a Two Step Network," submitted to IEEE Signal Processing Letters, 2018.
Experimental results in real-world environment.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
Performance Analysis of CRT for Image Encryption ijcisjournal
With the fast advancements of information technology, the security of image data transmitted or stored over
internet is become very difficult. To hide the details, an effective method is encryption, so that only
authorized persons can decrypt the image with the keys available. Since the default features of digital
image such as high capacity data, large redundancy and large similarities among pixels, the conventional
encryption algorithms such as AES, , DES, 3DES, and Blow Fish, are not applicable for real time image
encryption. This paper presents the performance of CRT for image encryption to secure storage and
transmission of image over internet.
[Japanese]Obake-GAN (Perturbative GAN): GAN with Perturbation Layersyumakishi
Abstract
Obake-GAN (Perturbative GAN), which replaces convolution layers of existing convolutional GANs (DCGAN, WGAN-GP , BIGGAN, etc.) with perturbation layers that adds a fixed noise mask, is proposed. Compared with the convolutional GANs, the number of parameters to be trained is smaller, the convergence of training is faster, the inception score of generated images is higher, and the overall training cost is reduced. Algorithmic generation of the noise masks is also proposed, with which the training, as well as the generation, can be boosted with hardware acceleration. Obake-GAN is evaluated using conventional datasets (CIFAR10, LSUN, ImageNet), both in the cases when a perturbation layer is adopted only for Generators and when it is introduced to both Generator and Discriminator .
修士論文「Obake-GAN: GAN with Perturbation Layers」の発表資料
GANの畳込層の代わりに摂動層を導入し、
・Generator 学習パラメータ52%削減
・Discriminator 学習パラメータ87%削減
・ImageNetでInception Score 45%改善
・学習の収束を高速化
SLIC Superpixel Based Self Organizing Maps Algorithm for Segmentation of Micr...IJAAS Team
We can find the simultaneous monitoring of thousands of genes in parallel Microarray technology. As per these measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, Intensity extraction, Enhancement and Segmentation are important steps in microarray image analysis. This paper gives simple linear iterative clustering (SLIC) based self organizing maps (SOM) algorithm for segmentation of microarray image. The clusters of pixels which share similar features are called Superpixels, thus they can be used as mid-level units to decrease the computational cost in many vision applications. The proposed algorithm utilizes superpixels as clustering objects instead of pixels. The qualitative and quantitative analysis shows that the proposed method produces better segmentation quality than k-means, fuzzy cmeans and self organizing maps clustering methods.
Exploring temporal graph data with Python: a study on tensor decomposition o...André Panisson
Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures.
The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.
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.
Similar to Facial Feature Analysis For Model Based Coding (20)
Phone As A Sensor Technology: mHealth and Chronic Disease Eric Larson
The mHealth “revolution” has promised to deliver in-home healthcare that parallels the care we might receive in a physician’s office. However, the panacea of digital health has proven to be more problematic and messy than its vision, especially for collecting and interpreting medical quantities from the home. In this talk I will discuss several successful projects for sensing medical quantities from a mobile phone using the embedded sensors (i.e., camera, microphone, accelerometer) and how these projects can increase compliance as well as enhance doctor patient relationships. I will focus on the reliability and calibration of the sensing and the role of computer scientists and engineers in the future of mHealth.
For the talk today, I will introduce the concept of model based coding. In particular, I will be discussing facial analysis and the concept of dynamic bandwidths. I will spend several slides on this material as my project has less to do with the particulars of algorithm as it does with the appropriate application of the algorithm. Next I will discuss the changes made to NSGA-II in order to make it more real time fro the application. In particular, I did this by combining it with a deterministic search. I will then present the results and conclude the presentation.
Mention the video games on essential parameter bullet Alternative to sending raw video footage Creation of “essential” parameters needed to reconstruct a scene Imagine the most recent multiplayer video games. The characters interact with each other, and the surroundings seemlessly. This is because the elements are based upon a model, not a video. A real-time analysis nightmare This is where the similarity ends, however. Video games take no real analysis. It is all built into the game software. For model based analysis, we need to find out what the user is doing by video processing instead of controller inputs.
Before discussing facial analysis it is good to get an idea of the power of model based coding. Take this scene for example, the participants in the conference are being transmitted extremely well. If you have ever participated in a conference on video, you know how bad it is. Other application include Gaming, where the character on the screen mimics your face, Man Machine interaction, where a computer can get an idea of you emotions using facial expressions. And video telephony. Most model based coding is at or below 1kb/s, and the last time I checked cellular communication, it was close to 9600 b/s at transmission and 1200 b/s when idle.
Okay here is how facial analysis by optimization is done. We need key animation parameters to manipulate the model of the face shown. The model based coder… But the analysis can be completed using synthesis…
The power of the coding is well illustrated from these example models… Ideally, I would have models as good as the ones seen here, but…
I have no funding for an expensive rendering software, and am a fairly dismal programmer. So this rendering will have to do.
The current research uses stochastic training to know when to do adjust based upon the error in the image. Gradient methods can be done in real time but catches up quickly with large movements of the head. To combat this, direct methods are used thast are not dependent on training. Direct methods are farther from being real time, but a more robust. TO make them more real time, researchers throw out FAPs. I agree that not all FAPs are needed fro realism, but currently only about 2 frames a second can be done using 12 parameters. FAP reduction is daunting, which FAPs are most important? For every facial scenario? And, no one is approaching this from a multiple objective standpoint.
What do I mean by dynamic bandwidth
I chose to use PSNR for its computational convenience. It is fast and easy to implement.
Use NSGA-II for the multiple objective optimization Assign a premature stopping criteria Choose bandwidth Select FAP sets Use deterministic algorithm
Have a two dimensional picture here, and explain the discrete line search