Presentation made @3DBODY.TECH Lugano, 17th October 2018.
This paper presents partial results of a larger validation study of different Data-driven 3D Reconstruction (D3DR) technologies developed by IBV to create watertight 3D human models from measurements (1D3D), 2D images (2D3D) or raw scans (3D3D). This study quantifies the reliability (Standard Error of Measurement, SEM; Mean Absolute Deviation, MAD; Intra-class Correlation Coefficient, ICC; and Coefficient of Variation, CV) of body measurements taken on human subjects. Our results are also compared to similar studies found in literature assessing the reliability of digital and traditional anthropometry. Moreover, we assess the compatibility (bias and Mean Absolute Error, MAE) of measurements between D3DR technologies. The results show that 2D3D can provide visually accurate body shapes and, for the measurements assessed, 2D3D is as reliable as high-resolution 3D scanners. It is also more accurate than manual measurements taken by untrained users. Due to accessibility, cost and portability (e.g. 2D3D built in a smartphone app) they could be more suitable than other methods at locations where body scanners are not available such as homes, medical or physical therapy offices, and small retail stores and gyms.
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
Le Big Data, semble aujourd’hui la solution miraculeuse pour une gestion efficace des masses de donnée. Mais de quoi s’agit-il ? Un vrai levier pour améliorer son activité? ou simple poudre aux yeux ? Dans ce contexte, Nexialog s’intéresse de plus en plus à cette thématique porteuse, et a réalisé une première étude abordant le Big Data en lien avec les secteurs financiers et assurantiels.
Trois sujets de recherche ont également été lancés en interne :
-L’impact du Big data sur l’organisation de l’entreprise
-Les technologies Big Data
-Gestion de Risques dans l’environnement Big Data
Réduction de la dimension, Diagonalisation, études des valeurs propres, centrage et réduction, techniques de choix des axes factoriels, critère de coude, critère de Kaiser, plans factoriels, carte des individus, cercle de corrélation
This presentation inludes step-by step tutorial by including the screen recordings to learn Rapid Miner.It also includes the step-step-step procedure to use the most interesting features -Turbo Prep and Auto Model.
Le Big Data, semble aujourd’hui la solution miraculeuse pour une gestion efficace des masses de donnée. Mais de quoi s’agit-il ? Un vrai levier pour améliorer son activité? ou simple poudre aux yeux ? Dans ce contexte, Nexialog s’intéresse de plus en plus à cette thématique porteuse, et a réalisé une première étude abordant le Big Data en lien avec les secteurs financiers et assurantiels.
Trois sujets de recherche ont également été lancés en interne :
-L’impact du Big data sur l’organisation de l’entreprise
-Les technologies Big Data
-Gestion de Risques dans l’environnement Big Data
Réduction de la dimension, Diagonalisation, études des valeurs propres, centrage et réduction, techniques de choix des axes factoriels, critère de coude, critère de Kaiser, plans factoriels, carte des individus, cercle de corrélation
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
PR-228: Geonet: Unsupervised learning of dense depth, optical flow and camera...Hyeongmin Lee
이번 논문은, Video로부터 Unsupervised 방식을 통해 Flow, Depth, Camera Ego-motion까지 뽑아내는 GeoNet이라는 알고리즘입니다. Computer Vision에서 다루는 3D Geometry에 대해 간략히 설명 드린 후에 GeoNet 알고리즘을 소개하는 영상입니다.
Le Machine Learning, l’IA, le DeepLearning, les Statistiques, le Data Mining… bref, tous ces mots sont les buzz words du moment mais que se cache-t-il derrière ?
A travers des exemples concrets, on parcourra les différentes approches du Machine Learning, les grandes familles d’algorithmes (n’ayez crainte : sans rentrer dans le cœur de leurs implémentations), puis les outils et les frameworks à la disposition des Data Scientists… et pour finir, on essayera de prédire l’avenir !
Salon Data - Nantes - 19 Septembre 2017
https://salondata.fr/2017/07/12/0930-1030-ml/
Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.
Generative adversarial network and its applications to speech signal and natu...宏毅 李
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
There are three parts in this tutorial. In the first part, we will give an introduction of generative adversarial network (GAN) and provide a thorough review about this technology. In the second part, we will focus on the applications of GAN to speech signal processing, including speech enhancement, voice conversion, speech synthesis, and the applications of domain adversarial training to speaker recognition and lip reading. In the third part, we will describe the major challenge of sentence generation by GAN and review a series of approaches dealing with the challenge. Meanwhile, we will present algorithms that use GAN to achieve text style transformation, machine translation and abstractive summarization without paired data.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
As proposed by the Paper, High-Resolution Image Synthesis with Latent Diffusion Models, latent diffusion models are a simple and efficient way that improve both the training and sampling efficiency of denoising diffusion models while retaining their quality
Deep Learning With Python Tutorial | EdurekaEdureka!
** Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This PPT on "Deep Learning with Python" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into the emergence. The various subparts of Data Science, how they are related and How Deep Learning is revolutionalizing the world we live in. This Tutorial covers the following topics:
Introduction To AI, ML, and DL
What is Deep Learning
Applications of Deep Learning
What is a Neural Network?
Structure of Perceptron
Demo: Perceptron from scratch
Demo: Creating Deep Neural Nets
Deep Learning blog series: https://bit.ly/2xVIMe1
Deep Learning With TensorFlow Playlist: https://goo.gl/cck4hE
Instagram:https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
발표자: 박태성 (UC Berkeley 박사과정)
발표일: 2017.6.
Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros.
His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University.
개요:
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
However, for many tasks, paired training data will not be available.
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.
Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Basics covered regarding Natural Language Processing, How ANN transformed to RNN, Architectures of vanila RNN, LSTM and GRU and few preprocessing techniques
[GomGuard] 뉴런부터 YOLO 까지 - 딥러닝 전반에 대한 이야기JungHyun Hong
뉴런, perceptron, cnn, r-cnn, fast r-cnn, faster r-cnn 및
backpropagation, activation function, batch normalization, cost function, optimizer 등 전반적인 딥뉴럴 네트워크에 대한 지식을 다루고 있습니다.
mail : knholic@gmail.com
blog : gomguard.tistory.com
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
In the fields of sports and health sciences, changes in body shape are one of the most important parameters in order to evaluate the effects of physical training or routine workout performed. Until now, these parameters were typically measured under manually skilled techniques in anthropometry. The purposes of present study were to develop 3D anthropometry and to determine feasibility of the measurements such as lengths, circumferences, body surface area BSA and body or segment volumes with comparing to other conventional methods. The colour information was used to detect the position of land mark seals which was pasted on the skin according to the anatomical basis in human anthropometry. Anthropometric data as well as body surface area and body volume measured by using 3D body scanning technologies might be widely prospective for evaluating the differences or changes in body shape in such fields as health and sports sciences. Mrs. M. Priscilla | Deepika. S | Gayathri. G | Rajalakshmi. M "3D Body Scanning for Human Anthropometry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33327.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/33327/3d-body-scanning-for-human-anthropometry/mrs-m-priscilla
Unsupervised learning representation with Deep Convolutional Generative Adversarial Network, Paper by Alec Radford, Luke Metz, and Soumith Chintala
(indico Research, Facebook AI Research).
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
PR-228: Geonet: Unsupervised learning of dense depth, optical flow and camera...Hyeongmin Lee
이번 논문은, Video로부터 Unsupervised 방식을 통해 Flow, Depth, Camera Ego-motion까지 뽑아내는 GeoNet이라는 알고리즘입니다. Computer Vision에서 다루는 3D Geometry에 대해 간략히 설명 드린 후에 GeoNet 알고리즘을 소개하는 영상입니다.
Le Machine Learning, l’IA, le DeepLearning, les Statistiques, le Data Mining… bref, tous ces mots sont les buzz words du moment mais que se cache-t-il derrière ?
A travers des exemples concrets, on parcourra les différentes approches du Machine Learning, les grandes familles d’algorithmes (n’ayez crainte : sans rentrer dans le cœur de leurs implémentations), puis les outils et les frameworks à la disposition des Data Scientists… et pour finir, on essayera de prédire l’avenir !
Salon Data - Nantes - 19 Septembre 2017
https://salondata.fr/2017/07/12/0930-1030-ml/
Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.
Generative adversarial network and its applications to speech signal and natu...宏毅 李
Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
There are three parts in this tutorial. In the first part, we will give an introduction of generative adversarial network (GAN) and provide a thorough review about this technology. In the second part, we will focus on the applications of GAN to speech signal processing, including speech enhancement, voice conversion, speech synthesis, and the applications of domain adversarial training to speaker recognition and lip reading. In the third part, we will describe the major challenge of sentence generation by GAN and review a series of approaches dealing with the challenge. Meanwhile, we will present algorithms that use GAN to achieve text style transformation, machine translation and abstractive summarization without paired data.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
As proposed by the Paper, High-Resolution Image Synthesis with Latent Diffusion Models, latent diffusion models are a simple and efficient way that improve both the training and sampling efficiency of denoising diffusion models while retaining their quality
Deep Learning With Python Tutorial | EdurekaEdureka!
** Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This PPT on "Deep Learning with Python" will provide you with detailed and comprehensive knowledge of Deep Learning, How it came into the emergence. The various subparts of Data Science, how they are related and How Deep Learning is revolutionalizing the world we live in. This Tutorial covers the following topics:
Introduction To AI, ML, and DL
What is Deep Learning
Applications of Deep Learning
What is a Neural Network?
Structure of Perceptron
Demo: Perceptron from scratch
Demo: Creating Deep Neural Nets
Deep Learning blog series: https://bit.ly/2xVIMe1
Deep Learning With TensorFlow Playlist: https://goo.gl/cck4hE
Instagram:https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
발표자: 박태성 (UC Berkeley 박사과정)
발표일: 2017.6.
Taesung Park is a Ph.D. student at UC Berkeley in AI and computer vision, advised by Prof. Alexei Efros.
His research interest lies between computer vision and computational photography, such as generating realistic images or enhancing photo qualities. He received B.S. in mathematics and M.S. in computer science from Stanford University.
개요:
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
However, for many tasks, paired training data will not be available.
We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples.
Our goal is to learn a mapping G: X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Because this mapping is highly under-constrained, we couple it with an inverse mapping F: Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Qualitative results are presented on several tasks where paired training data does not exist, including collection style transfer, object transfiguration, season transfer, photo enhancement, etc.
Quantitative comparisons against several prior methods demonstrate the superiority of our approach.
Basics covered regarding Natural Language Processing, How ANN transformed to RNN, Architectures of vanila RNN, LSTM and GRU and few preprocessing techniques
[GomGuard] 뉴런부터 YOLO 까지 - 딥러닝 전반에 대한 이야기JungHyun Hong
뉴런, perceptron, cnn, r-cnn, fast r-cnn, faster r-cnn 및
backpropagation, activation function, batch normalization, cost function, optimizer 등 전반적인 딥뉴럴 네트워크에 대한 지식을 다루고 있습니다.
mail : knholic@gmail.com
blog : gomguard.tistory.com
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
In the fields of sports and health sciences, changes in body shape are one of the most important parameters in order to evaluate the effects of physical training or routine workout performed. Until now, these parameters were typically measured under manually skilled techniques in anthropometry. The purposes of present study were to develop 3D anthropometry and to determine feasibility of the measurements such as lengths, circumferences, body surface area BSA and body or segment volumes with comparing to other conventional methods. The colour information was used to detect the position of land mark seals which was pasted on the skin according to the anatomical basis in human anthropometry. Anthropometric data as well as body surface area and body volume measured by using 3D body scanning technologies might be widely prospective for evaluating the differences or changes in body shape in such fields as health and sports sciences. Mrs. M. Priscilla | Deepika. S | Gayathri. G | Rajalakshmi. M "3D Body Scanning for Human Anthropometry" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33327.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/33327/3d-body-scanning-for-human-anthropometry/mrs-m-priscilla
Presenetation of the results of the validation study of data-driven 3D reconstruction technologies for the foot developed by IBV: DomeScan and Avatar 3D app. It also presents an insight to the 3D reconstruction of full bodies from raw scans and from two images taken with a smartphone.
Role of 3D printing & 3D model in Complex Total Hip Replacement Queen Mary Hospital
Role of 3D printing & 3D model in Complex Total Hip Replacement
Dr. Kalaivanan Kanniyan
for queries - drkkbriyan@gmail.com / drkkbriyan@outlook.com
Asian Joint Reconstruction Institute
AJRI
chennai
India
Tamil nadu
complex hip replacement , knee replacment, knee navigation
Long version of the demo/presentation made @ MODINT Sizing Seminar 2016 (23rd June 2016) in Zeist (The Netherlands). It includes videos from Kidsize and Eurofit projects.
Wireless network implementation is a viable option for building network infrastructure in rural communities. Rural people lack network infrastructures for information services and socio-economic development. The aim of this study was to develop a wireless network infrastructure architecture for network services to rural dwellers. A user-centered approach was applied in the study and a wireless network infrastructure was designed and deployed to cover five rural locations. Data was collected and analyzed to assess the performance of the network facilities. The results shows that the system had been performing adequately without any downtime with an average of 200 users per month and the quality of service has remained high. The transmit/receive rate of 300Mbps was thrice as fast as the normal Ethernet transmit/receive specification with an average throughput of 1 Mbps. The multiple output/multiple input (MIMO) point-to-multipoint network design increased the network throughput and the quality of service experienced by the users.
3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition, the goal of this work also included making the 3D scanning process fully automated by building and integrating a turntable alongside the software. This means the user can perform a full 3D scan only by a press of a few buttons from our dedicated graphical user interface. Three main steps were followed to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.
3D reconstruction is a technique used in computer vision which has a wide range of applications in areas like object recognition, city modelling, virtual reality, physical simulations, video games and special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required. Such systems were often very expensive and was only available for industrial or research purpose. With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition, the goal of this work also included making the 3D scanning process fully automated by building and integrating a turntable alongside the software. This means the user can perform a full 3D scan only by a press of a few buttons from our dedicated graphical user interface. Three main steps were followed to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and convert the acquired point cloud data into a watertight mesh of good quality. Third, export the reconstructed model to a 3D printer to obtain a proper 3D print of the model.
COMPLETE END-TO-END LOW COST SOLUTION TO A 3D SCANNING SYSTEM WITH INTEGRATED...ijcsit
3D reconstruction is a technique used in computer vision which has a wide range of applications in
areas like object recognition, city modelling, virtual reality, physical simulations, video games and
special effects. Previously, to perform a 3D reconstruction, specialized hardwares were required.
Such systems were often very expensive and was only available for industrial or research purpose.
With the rise of the availability of high-quality low cost 3D sensors, it is now possible to design
inexpensive complete 3D scanning systems. The objective of this work was to design an acquisition and
processing system that can perform 3D scanning and reconstruction of objects seamlessly. In addition,
the goal of this work also included making the 3D scanning process fully automated by building and
integrating a turntable alongside the software. This means the user can perform a full 3D scan only by
a press of a few buttons from our dedicated graphical user interface. Three main steps were followed
to go from acquisition of point clouds to the finished reconstructed 3D model. First, our system
acquires point cloud data of a person/object using inexpensive camera sensor. Second, align and
convert the acquired point cloud data into a watertight mesh of good quality. Third, export the
reconstructed model to a 3D printer to obtain a proper 3D print of the model.
Presented the 28th October 2015 at the 6th International Conference and Exhibition on body Scanning Technologies 2015, Hometrica Consulting, Lugano, Switzerland.
The access to the 3D representation of people’s body shape has multiple applications to consumer goods which performance is related to human body dimensions or shape. This is the case of wearables such as clothing, footwear, headgear, orthotics, or equipment/environments such as furniture, transports or workstations. Some of the existing and potential applications of 3D human representations include personalisation, virtual try-on or size allocation for wearables or product configuration/adjustment for equipment/environments.
However, the cost of 3D scanners is high; the devices are too bulky for homes and retail stores; and its proper use requires expertise to get the relevant parameters from the 3D object (e.g. measurements). These three barriers are currently hindering the massive spreading of 3D scanners as consumer good or as typical in-store appliance.
This paper describes an array of approaches for realistically estimating human 3D shapes (i.e. full bodies or feet) using a regular smartphone or just entering a set of parameters (e.g. age, gender and self-taken measurements). The proposed approaches are based on data-driven 3D reconstructions, using parameterised shape spaces created from large 3D human body or feet databases. The algorithm finds the combination of shape parameters that best matches either the silhouettes extracted from the images or the body measurements entered.
Despite not being actual body scanners, these solutions are easy-to-use and can provide enough accuracy for applications such as virtual try-on, made-to-measure or size allocation of certain types of wearables. Moreover, they can be distributed to the final consumer or to the points of sale at a really reduced cost (or even for free), thus overcoming the main barriers to the massive spreading of its use in e-commerce, new retail experiences, new production pipelines or new business models.
In order to illustrate these technologies, some examples of application to different contexts (i.e. virtual worlds, e-commerce and personalisation) are presented: virtual try-on of female fashion (VisuaLook), size allocation for childrenswear (KIDSIZE), personalised comfort insoles (Sunfeet) and personalised shoes (Feetz).
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
Modern computer 3D technologies of craniofacial identification with the use of the software system TADD SM and 3D scanner ARTEC in Russia. The use of 3D skull images in craniofacial identification when applying the photo superimposition method.
Kidsize is a proof-of-concept developed by IBV. This presentation describes the two innovations underpinning Kidsize concept and presents the results of their validation. The first one is a mobile phone app to measure a child in 3D by taking two pictures. This new method is more accurate and consistent than an untrained person using a measuring tape at home or in the shop. The second one is an expert system that recommends the size that best fits the child and assesses the fit of the garment at different body areas. Project results show that it can provide nearly 90% right size recommendations, thus outperforming existing methods like age- or height-based size guides, which achieve 40 and 60% right recommendations respectively.
Reliability of Three-dimensional Photonic Scanner Anthropometry Performed by ...CSCJournals
This work explored the relative and absolute reliability of three-dimensional (3D) anthropometry performed by skilled and naïve operators using a fast, pose tolerant whole-body 3D scanner device. Upon skin landmarking by an experienced operator (skilled anthropometrist, SA), twelve subjects (six males and six females) underwent a thorough 3D anthropometric evaluation by the SA and two naïve operators (NA). Using the same landmarks, the SA also performed traditional anthropometry measurements. All measurements were taken twice. Relative reliability was tested with the Pearson’s correlation coefficient r and the intraclass correlation coefficient (ICC); absolute reliability was tested calculating the percentage coefficient of variation (%CV), the standard error of measurement (SEM), the percentage technical error of measurement (%TEM), and paired Student’s t test. Results showed that intra-operator relative and absolute reliability was excellent for all and most 3D measurement items, respectively, independently of the operator’s skill. Inter-operator (SA vs. individual NA) relative reliability was excellent as well; inter-operator absolute reliability was not acceptable for about only 30% of measurement items. Results of this work show that 3D anthropometry has strong potential in anthropometry due to high intrinsic reliability and less need for operator training vs. traditional anthropometry.
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Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
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3D Human Models from 1D, 2D & 3D Inputs @3DBODY.TECH 17th Oct 2018
1. 3D human models
from 1D, 2D & 3D inputs
reliability and compatibility
of body measurements
Alfredo Ballester
Anthropometry Research Group of IBV
alfredo.ballester@ibv.org
3. IBV is a private not-for-profit R&D organisation
Consultancy
for manufacturing industries
Research & Development
for technology companies
Apparel Sports Transport
Health
Safety
Leisure
Appliances Elderly
Orthotics
Motion Analysis
Anthropometry
Human Factors
4. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Digital Anthropometry at IBV
2004 Start gathering 3D foot scan data
2007 Start gathering body scan data
2012 Start developing own automatic 3D
processing SW for research
2018 Launch of 3D BODY reconstruction
with smartphone photographs
2015 Launch of 3D FOOT reconstruction
with smartphone photographs
5. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Data-driven
3D Recons-
truction
Data-driven 3D Reconstruction
2D3D
1D3D
3D3D
human shape & pose
data model learnt
from large 3D databases
Virtual Fashion
Virtual
Ergonomics
Measurements Joints3D model
7. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
Point
Cloud
Incomplete
or noisy
mesh
Artefacted
mesh
Watertight
complete
model
Markerless
(A-Pose)
Robust
Automatic
Fast
Adjustable to
input quality
8. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
Anatomical surface completion Anatomical correction of artefacts and noise
9. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
3D3D – Raw scans to 3D models
• 3Dfy.me
• 3dMD
• 4Ddynamics
• CyberWare
• Human Solutions
• Fit3D
• H3ALTH TECH.
• Lemotive
• NOMO
• Passen
• Scanologics
• ShapeMe
• Artec
• SizeStream
• SpaceVision
• Telmat
• TC2
• Treedys
• Twinster
• Voxelan
• Youdome
CAESAR Size Korea Sizing Portugal Size UK Spanish Survey HQL Japan Smartfit Belgium
10. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – Images to 3D models
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
Ballester et al. 2016 [43]
11. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old
method [43]
new
method
Poor guide
outline fit
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
12. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
13. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old method [43]
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
new method
14. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
old method new method
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
15. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
2D3D – deep learning improvements
Back leg
visible
Back leg
visible
Lumbar
occlusion
2 images, gravity sensor
& camera parameters
Segmentation
& keypoints
(deep learning)
3D Reconstruction
Measuring, rigging, etc.
3D Body Model
Projection
matrix
estimation
Data-Driven
Space of Shapes of
Human Body
Guiding outline
Data-Driven
Space of Body
outlines
Age, weight, height
16. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
1D3D – Parameters to 3D models
𝑋𝑋 =
𝑝𝑝1
1
⋯ 𝑝𝑝𝑚𝑚
1
⋮ ⋱ ⋮
𝑝𝑝1
𝑛𝑛
⋯ 𝑝𝑝𝑚𝑚
𝑛𝑛
𝑛𝑛,𝑚𝑚
𝒀𝒀 = 𝒀𝒀𝟎𝟎 + �𝑩𝑩𝑷𝑷𝑷𝑷𝑷𝑷 · (𝑿𝑿 − 𝑿𝑿𝟎𝟎) + �𝑭𝑭
𝑌𝑌 =
𝑡𝑡𝑃𝑃𝑃𝑃1
1
⋯ 𝑡𝑡𝑃𝑃𝑃𝑃𝑃𝑃
1
⋮ ⋱ ⋮
𝑡𝑡𝑃𝑃𝑃𝑃1
𝑛𝑛
⋯ 𝑡𝑡𝑃𝑃𝑃𝑃𝑃𝑃
𝑛𝑛
𝑛𝑛,𝑝𝑝
Input parameters (X) can be
body measurements or other
metrics (e.g. age or weight)
17. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Data-driven 3D Reconstruction
Accuracy of the 3D model
• Age
• Weight
• Height
• Waist
• Hips
• …
1D-3D 2D-3D 3D-3D LoQ 3D-3D HiQ
19. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Body shape variability due to:
Pose, muscle contraction,
respiration, garments, etc…
Objectives of the experiment
#2 Assessment of the REALIBILITY of measurements
from 2D3D and 3D3D
• Quantification of errors: SEM, MAD, ICC, CV
• Comparison with 20 similar studies using 3D body
scanners and Expert manual measurements
#3 Assessment of the COMPATIBILITY of measurements
between 3D3D and the other techs, 2D3D and 1D3D
• Quantification of errors: Bias and MAE
#1 Visual Assessment of body SHAPE ACCURACY of
2D3D and 1D3D wrt 3D3D
20. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Design of the experiment
Method Input data
1D3D(3) Age, Height, Weight
1D3D(6) Age, Height, Weight, Chest girth, Waist girth, Hip girth
1D3D(7) Age, Height, Weight, Chest girth, Waist girth, Hip girth, Crotch height
2D3D Age, Height, Weight, front image, side image
3D3D Raw 3D scan
Participants
• 77 (39♀ 38♂) volunteers
• Variety of body shapes
o Weight 44-136 kg
o Height 149-189 cm
o Age 19-58 y.o.
Equipment
• Vitus XXL (Human Solutions)
• Motorola Nexus 6
• Self-reported measurements
taken at home (37 users)
3D processing
Procedure
• Skin-tight clothing
• A-Pose
• 2 repetitions with repositioning
32. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Conclusions
Method Qualitative Assessment Quantitative Assessment
• Visually perfect results
• Surface-to-scan accuracy adjustable to
accuracy of input
• MAD 0.1-0.5 cm, SEM 0.2-0.8 cm
• ICC > 0.98, CV < 2%
• Realistic and visually
accurate 3D shapes for all
body types
• Accurate and reliable measurements
• MAD 0.2-0.6 cm, SEM 0.3-1 cm
• ICC > 0.93, CV < 2%
• MAE 0.4-2.2 cm
• Indicative body shapes
• Body shapes tend to average
• Measurements tend to average
• Accuracy is highly dependent on user skills
• MAE 0.7-4.6 cm
3D3D
1D3D
2D3D
33. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Ongoing research 3D3D
Objectives: Any pose, clearing scene of objects, noise, floor
Methods: deep learning for automatic landmarking in any
pose and noise filtering
3D3D modelShape+Pose+
+Soft tissue
Shape Shape+Pose
34. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Ongoing research 2D3D
Objectives: less restrictive input such as casual clothing and more relaxed/natural poses
Methods: different alternatives but all making intensive use of deep learning
35. 9th 3DBODY.TECH, Lugano, Switzerland, 16-17 Oct 20183D human models from 1D, 2D & 3D inputs
Presentation References (numbering of the paper)
[24] M. Kouchi and M. Mochimaru, “Errors in landmarking
and the evaluation of the accuracy of traditional and
3D anthropometry,” Appl. Ergon., vol. 42, no. 3, pp.
518–527, Mar. 2011.
[25] A. Kuehnapfel et al., “Reliability of 3D laser-based
anthropometry and comparison with classical
anthropometry,” Sci. Rep., vol. 6, p. 26672, May 2016.
[26] N. Koepke et al., “Comparison of 3D laser-based
photonic scans and manual anthropometric
measurements of body size and shape in a validation
study of 123 young Swiss men,” PeerJ, vol. 5, Feb.
2017.
[33] B. Allen et al., “The Space of Human Body Shapes:
Reconstruction and Parameterization from Range
Scans,” in ACM SIGGRAPH 2003 Papers, New York, NY,
USA, 2003, pp. 587–594.
[42] S. D. Walter et al., “Sample size and optimal designs for
reliability studies,” Stat. Med., vol. 17, no. 1, pp. 101–
110, 1998.
[43] A. Ballester et al., “Data-driven three-dimensional
reconstruction of human bodies using a mobile phone
app,” Int. J. Digit. Hum., vol. 1, no. 4, pp. 361–388,
2016.
[46] M. Eliasziw et al., “Statistical Methodology for the
Concurrent Assessment of Interrater and Intrarater
Reliability: Using Goniometric Measurements as an
Example,” Phys. Ther., vol. 74, no. 8, pp. 777–788, Aug.
1994.
[47] B. Ng et al., “Clinical anthropometrics and body
composition from 3D whole-body surface scans,” Eur.
J. Clin. Nutr., vol. 70, no. 11, pp. 1265–1270, Nov. 2016.
[48] J. Wang et al., “Validation of a 3-dimensional photonic
scanner for the measurement of body volumes,
dimensions, and percentage body fat,” Am. J. Clin.
Nutr., vol. 83, no. 4, pp. 809–816, Apr. 2006.
[49] T. E. Vonk and H. A. M. Daanen, “Validity and
Repeatability of the Sizestream 3D Scanner and Poikos
Modeling System,” in 6th International Conference on
3D Body Scanning Technologies, Lugano, Switzerland,
27-28 October 2015, 2015.
[50] J. C. K. Wells et al., “Acceptability, Precision and
Accuracy of 3D Photonic Scanning for Measurement of
Body Shape in a Multi-Ethnic Sample of Children Aged
5-11 Years: The SLIC Study,” PLoS One San Franc., vol.
10, no. 4, 2015.
[51] M. R. Pepper et al., “Validation of a 3-Dimensional
Laser Body Scanner for Assessment of Waist and Hip
Circumference,” J. Am. Coll. Nutr., vol. 29, no. 3, pp.
179–188, Jun. 2010.
[52] Ł. Markiewicz et al., “3D anthropometric algorithms for
the estimation of measurements required for
specialized garment design,” Expert Syst. Appl., vol. 85,
pp. 366–385, Nov. 2017.
[53] J. M. Lu and M. J. J. Wang, “The Evaluation of Scan-
Derived Anthropometric Measurements,” IEEE Trans.
Instrum. Meas., vol. 59, no. 8, pp. 2048–2054, Aug.
2010.
[54] L. D. Dekker, “3D human body modelling from range
data,” Doctoral, Univ. of London, 2000.
[55] K. M. Robinette and H. A. M. Daanen, “Precision of the
CAESAR scan-extracted measurements,” Appl. Ergon.,
vol. 37, no. 3, pp. 259–265, May 2006.
[56] W. C. Chumlea et al., “Replicability for anthropometry
in the elderly,” Hum. Biol., pp. 329–337, 1984.
[57] T. G. Lohman et al., Anthropometric standardization
reference manual, vol. 177. Human kinetics books
Champaign, 1988.
[58] L. M. Verweij et al., “Measurement error of waist
circumference: gaps in knowledge,” Public Health
Nutr., vol. 16, no. 02, pp. 281–288, Feb. 2013.
[59] J. Nada et al., “Intraobserver and interobserver
variability of measuring waist circumference,” Med.
Sci. Monit., vol. 14, no. 1, pp. CR15–CR18, 2008.
36. Thank you!
Sandra Alemany
Ana Piérola
Eduardo Parrilla
Jordi Uriel
Alfredo Remón
Juan A. Solves
Ana V. Ruescas
Julio A. Vivas
Juan V. Durá
Alfredo Ballester
Juan C. González
Beatriz Mañas
Rosa Porcar
https://antropometria.ibv.org/en/
Youtube Channel: https://www.youtube.com/channel/UChFTNRmt3veDBWuVoJsugTg
Full Paper: http://www.3dbody.tech/cap/papers/2018/18132ballester.pdf