Neural Network Based Eye Tracking
|
|
|
- Clemence Cooper
- 5 years ago
- Views:
Transcription
1 Neural Network Based Eye Tracking Workshop on Architectures of Smart Camera Instituto de Estudios Sociales Avanzados IESA-CSIC June 5/6, 2017, Córdoba, Spain Pavel Morozkin (SuriCog) Maria Trocan (ISEP) Marc Swynghedauw (SuriCog) 5 June 2017
2 2 2 Motivation & context (1/2) From eye image acquisition to measuring eye rotation Weetsy wearable system Embedded Weetsy device performs pre-processing 4 Pi-Box remote smart sensor Weetsy detection and 3D localization 5 Head data wire/wireless transmission 1 Image compression Miniaturized sensor captures eye movements 3 Eye data wire/wireless transmission 6 Processing unit performs eye-tracking, head localization and gaze estimation Processing unit (PC)
3 3 Motivation & context (2/2) From eye image acquisition to measuring eye rotation Weetsy wearable system 2 Embedded Weetsy device performs pre-processing Low-power, low-heat, low-mips requirements 1 Image compression Miniaturized sensor captures eye movements 3 Eye data wire/wireless transmission The issue: it is a challenge to satisfy the requirements and keep high image compression ratios (while maintaining acceptable image quality) Solution: use of non-typical approach based on neural network
4 4 Outline 1. Introduction: EyeDee by SuriCog 2. Eye tracking: simulation & ROI finding 3. Eye tracking: image processing vs. neural network 4. Neural network: employment, construction and training 5. Experimental results 6. Conclusion
5 5 Eye tracking: simulation From simulated eye images (theory) to real eye images (practice) Rotating pupil disk has a geometrical shape of an ellipse Eye images: real one and generated by simulator 5 ellipse parameters: center (x, y), major/minor (a/b) axis, rotation angle (φ).
6 6 Eye tracking algorithm: ROI finding First step in the eye traking ROI decimation Full image (640x640) Active ROI (400x256) ROI (120x120)
7 7 Eye tracking: image processing based Using image processing to generate 5 ellipse parameters Filtering the eye image is aimed on ellipse shape preservation, followed by its measuring.
8 8 Eye tracking: neural network based Using i neural network to generate 5 ellipse parameters Training of the neural network, followed by loss function optimization (minimization of the average ellipse reconstruction error).
9 9 Neural network: employment What is the correlation between decimated ROI image values and 5 ellipse parameters? Goal: finding a correlation between the floating-point values of a decimated ROI image and the five floating-point parameters of the pupil's ellipse. correlation Use neural network to perform regression analysis i.e., to estimate the relationships among variables: 9/25 floating-point values of decimated eye image (result of the 3D perspective projection of a model of rotating pupil disk) and 5 floating-point parameters of pupil's ellipse (result of the eye tracking)
![10 Neural network: construction and training Using Torch7 [1] software (neural network 'nn' and optimization 'optim' packages).](/docs-images/90/104470846/images/10-1.jpg "Neural network hyper-parameters: Number of epochs the number of iterations over the training dataset. Learning rate the size of the step taken at each stochastic estimate of the gradient.")
10 10 Neural network: construction and training Using Torch7 [1] software (neural network 'nn' and optimization 'optim' packages). Neural network hyper-parameters: Number of epochs the number of iterations over the training dataset. Learning rate the size of the step taken at each stochastic estimate of the gradient. Learning rate decay allows the algorithm to converge with high precision (it is often recommended to lower the learning rate as the training progresses). Weight decay used to L2-regularize the solution (model overfitting reduction), which prevents the weights from growing too large. Momentum used to prevent the system from converging to a local minimum or saddle point. [1] Which architecture, how many hidden layers, how many training iterations? Notes: Use of sequential class container (plugs layers in a feed-forward fully connected manner). Use of stochastic gradient descent (SGD) method The neural network has 9/25 inputs (18-50 inputs in case of using ellipse edges) and 5 outputs.
11 11 Experimental results: testing scheme Image pre-processing (FPGA ROI-finder), wireless transmission Based on ellipse similarity
12 12 Experimental results: ellipse similarity (1/2) If you cannot measure it you cannot improve it We compute ellipse similarity, based on three coefficients: distance, shape and orientation. Three coefficients: 1. Distance based on centers (x, y) 2. Shape based on major/minor (a/b) axis 3. Orientation based on rotation angle (φ) Ellipse generated by image processing Ellipse generated by neural network
13 13 Experimental results: ellipse similarity (2/2) Flluctuation of ellipse similarity coefficients Average ellipse similarity for different ROI sizes We decided to use orientation coefficient to estimate quality of neural network training, because it fluctuates in more wide range than distance coefficient or shape coefficient. Generated by neural network
14 14 Experimental results: of training iterations What is the optimal number of training iterations? We selected 40k training iterations 3x3 4x4 5x5 0.8 o (average) Underlearning Optimal Overlearning Number of training iterations x 10 4
15 15 Experimental results: of hidden layers What is the optimal number of hidden layers? We selected 2 hidden layers 3x3 4x4 5x o (average) Number of hidden layers (40000 training iterations)
16 16 Experimental results: visual comparison It is acceptable practice to also perform visual comparison Visual comparison of ellipse reconstruction (increasing degradation order, from best (a) to worst(d)). Perfect reconstruction Center: OK Shape: OK Orient.: OK Center: KO Shape: OK Orient.: KO Center: OK Shape: OK Orient.: KO Center: KO Shape: KO Orient.: KO
17 17 Hardware: neural network deployment Uploading of the trained neural network directly into the Weetsy board FPGA Microcontroller Wireless module Trained neural newtork External RAM
18 18 Conclusion Summary on all the considerations 1. Efficient neural network based eye tracking approach 2. Best parameters (training iterations/hidden layers) 3. Ellipse fitting issues (training quality)
19 19 Future plans What are the steps expected to be done Experimentation with all parameters: 1. Adding more noise to simulated eye images 2. Adding the ROI offset (with use of real eye images, the pupil is not necessarily centered in the found ROI) and training of the neural network to produce correct results with non-centered ROI 3. Neural network hyper-parameters optimization 4. Testing the trained neural network on real images
20 20 Thank you For your interest, attention and time Questions?
Opening the black box of Deep Neural Networks via Information (Ravid Shwartz-Ziv and Naftali Tishby) An overview by Philip Amortila and Nicolas Gagné
Opening the black box of Deep Neural Networks via Information (Ravid Shwartz-Ziv and Naftali Tishby) An overview by Philip Amortila and Nicolas Gagné 1 Problem: The usual "black box" story: "Despite their
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability
Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability Janis Keuper Itwm.fraunhofer.de/ml Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern,
IRIS SEGMENTATION OF NON-IDEAL IMAGES
IRIS SEGMENTATION OF NON-IDEAL IMAGES William S. Weld St. Lawrence University Computer Science Department Canton, NY 13617 Xiaojun Qi, Ph.D Utah State University Computer Science Department Logan, UT 84322
CAP 6412 Advanced Computer Vision
CAP 6412 Advanced Computer Vision http://www.cs.ucf.edu/~bgong/cap6412.html Boqing Gong April 21st, 2016 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha
Deep Neural Networks Optimization
Deep Neural Networks Optimization Creative Commons (cc) by Akritasa http://arxiv.org/pdf/1406.2572.pdf Slides from Geoffrey Hinton CSC411/2515: Machine Learning and Data Mining, Winter 2018 Michael Guerzhoy
Keras: Handwritten Digit Recognition using MNIST Dataset
Keras: Handwritten Digit Recognition using MNIST Dataset IIT PATNA January 31, 2018 1 / 30 OUTLINE 1 Keras: Introduction 2 Installing Keras 3 Keras: Building, Testing, Improving A Simple Network 2 / 30
CS489/698: Intro to ML
CS489/698: Intro to ML Lecture 14: Training of Deep NNs Instructor: Sun Sun 1 Outline Activation functions Regularization Gradient-based optimization 2 Examples of activation functions 3 5/28/18 Sun Sun
A new approach for supervised power disaggregation by using a deep recurrent LSTM network
A new approach for supervised power disaggregation by using a deep recurrent LSTM network GlobalSIP 2015, 14th Dec. Lukas Mauch and Bin Yang Institute of Signal Processing and System Theory University
Neural Networks. CE-725: Statistical Pattern Recognition Sharif University of Technology Spring Soleymani
Neural Networks CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Biological and artificial neural networks Feed-forward neural networks Single layer
LECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS
LECTURE NOTES Professor Anita Wasilewska NEURAL NETWORKS Neural Networks Classifier Introduction INPUT: classification data, i.e. it contains an classification (class) attribute. WE also say that the class
Neural Network Learning. Today s Lecture. Continuation of Neural Networks. Artificial Neural Networks. Lecture 24: Learning 3. Victor R.
Lecture 24: Learning 3 Victor R. Lesser CMPSCI 683 Fall 2010 Today s Lecture Continuation of Neural Networks Artificial Neural Networks Compose of nodes/units connected by links Each link has a numeric
DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS
DEEP LEARNING OF COMPRESSED SENSING OPERATORS WITH STRUCTURAL SIMILARITY (SSIM) LOSS ABSTRACT Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small
Motivation. Problem: With our linear methods, we can train the weights but not the basis functions: Activator Trainable weight. Fixed basis function
Neural Networks Motivation Problem: With our linear methods, we can train the weights but not the basis functions: Activator Trainable weight Fixed basis function Flashback: Linear regression Flashback:
Efficient Deep Learning Optimization Methods
11-785/ Spring 2019/ Recitation 3 Efficient Deep Learning Optimization Methods Josh Moavenzadeh, Kai Hu, and Cody Smith Outline 1 Review of optimization 2 Optimization practice 3 Training tips in PyTorch
Robot Mapping. TORO Gradient Descent for SLAM. Cyrill Stachniss
Robot Mapping TORO Gradient Descent for SLAM Cyrill Stachniss 1 Stochastic Gradient Descent Minimize the error individually for each constraint (decomposition of the problem into sub-problems) Solve one
Deep Learning. Practical introduction with Keras JORDI TORRES 27/05/2018. Chapter 3 JORDI TORRES
Deep Learning Practical introduction with Keras Chapter 3 27/05/2018 Neuron A neural network is formed by neurons connected to each other; in turn, each connection of one neural network is associated
S+U Learning through ANs - Pranjit Kalita
S+U Learning through ANs - Pranjit Kalita - (from paper) Learning from Simulated and Unsupervised Images through Adversarial Training - Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda
Limited view X-ray CT for dimensional analysis
Limited view X-ray CT for dimensional analysis G. A. JONES ( GLENN.JONES@IMPERIAL.AC.UK ) P. HUTHWAITE ( P.HUTHWAITE@IMPERIAL.AC.UK ) NON-DESTRUCTIVE EVALUATION GROUP 1 Outline of talk Industrial X-ray
Parallel Stochastic Gradient Descent: The case for native GPU-side GPI
Parallel Stochastic Gradient Descent: The case for native GPU-side GPI J. Keuper Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern, Germany Mark Silberstein Accelerated Computer
SGD: Stochastic Gradient Descent
Improving SGD Hantao Zhang Deep Learning with Python Reading: http://neuralnetworksanddeeplearning.com/index.html Chapter 2 SGD: Stochastic Gradient Descent Main Idea: Given a set of input/output examples
Akarsh Pokkunuru EECS Department Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Akarsh Pokkunuru EECS Department 03-16-2017 Contractive Auto-Encoders: Explicit Invariance During Feature Extraction 1 AGENDA Introduction to Auto-encoders Types of Auto-encoders Analysis of different
Convolutional Neural Networks for Eye Tracking Algorithm
Convolutional Neural Networks for Eye Tracking Algorithm Jonathan Griffin Stanford University jgriffi2@stanford.edu Andrea Ramirez Stanford University aramire9@stanford.edu Abstract Eye tracking is an
Classification Lecture Notes cse352. Neural Networks. Professor Anita Wasilewska
Classification Lecture Notes cse352 Neural Networks Professor Anita Wasilewska Neural Networks Classification Introduction INPUT: classification data, i.e. it contains an classification (class) attribute
Finding Tiny Faces Supplementary Materials
Finding Tiny Faces Supplementary Materials Peiyun Hu, Deva Ramanan Robotics Institute Carnegie Mellon University {peiyunh,deva}@cs.cmu.edu 1. Error analysis Quantitative analysis We plot the distribution
CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm
CIS581: Computer Vision and Computational Photography Project 4, Part B: Convolutional Neural Networks (CNNs) Due: Dec.11, 2017 at 11:59 pm Instructions CNNs is a team project. The maximum size of a team
Image Restoration Using DNN
Image Restoration Using DNN Hila Levi & Eran Amar Images were taken from: http://people.tuebingen.mpg.de/burger/neural_denoising/ Agenda Domain Expertise vs. End-to-End optimization Image Denoising and
Neural Network Optimization and Tuning / Spring 2018 / Recitation 3
Neural Network Optimization and Tuning 11-785 / Spring 2018 / Recitation 3 1 Logistics You will work through a Jupyter notebook that contains sample and starter code with explanations and comments throughout.
Neural Network Neurons
Neural Networks Neural Network Neurons 1 Receives n inputs (plus a bias term) Multiplies each input by its weight Applies activation function to the sum of results Outputs result Activation Functions Given
Analyzing the Relationship Between Head Pose and Gaze to Model Driver Visual Attention
Analyzing the Relationship Between Head Pose and Gaze to Model Driver Visual Attention Sumit Jha and Carlos Busso Multimodal Signal Processing (MSP) Laboratory Department of Electrical Engineering, The
Chapter 4. Adaptive Self-tuning : A Neural Network approach. 4.1 Introduction
Chapter 4 Adaptive Self-tuning : A Neural Network approach 4.1 Introduction Machine learning is a method of solving real world problems by employing the hidden knowledge present in the past data or data
Perceptron: This is convolution!
Perceptron: This is convolution! v v v Shared weights v Filter = local perceptron. Also called kernel. By pooling responses at different locations, we gain robustness to the exact spatial location of image
Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging
Guided Image Super-Resolution: A New Technique for Photogeometric Super-Resolution in Hybrid 3-D Range Imaging Florin C. Ghesu 1, Thomas Köhler 1,2, Sven Haase 1, Joachim Hornegger 1,2 04.09.2014 1 Pattern
Pitch Prediction from Mel-frequency Cepstral Coefficients Using Sparse Spectrum Recovery
Pitch Prediction from Mel-frequency Cepstral Coefficients Using Sparse Spectrum Recovery Achuth Rao MV, Prasanta Kumar Ghosh SPIRE LAB Electrical Engineering, Indian Institute of Science (IISc), Bangalore,
A STUDY ON ENVIRONMENT RECOGNITION METHOD USING MOTION INFORMATION
A STUDY ON ENVIRONMENT RECOGNITION METHOD USING MOTION INFORMATION Hiroshi TAKAHASHI, Seigo WATANABE, Hidekazu NISHIUCHI Yoshihito SANO and Hiroshi SATOH Nissan research Center Nissan Motor Co., Ltd. Yokosuka,
3D Polygon Rendering. Many applications use rendering of 3D polygons with direct illumination
Rendering Pipeline 3D Polygon Rendering Many applications use rendering of 3D polygons with direct illumination 3D Polygon Rendering What steps are necessary to utilize spatial coherence while drawing
Incremental Real-time Bundle Adjustment for Multi-camera Systems with Points at Infinity
Incremental Real-time Bundle Adjustment for Multi-camera Systems with Points at Infinity Johannes Schneider, Thomas Läbe, Wolfgang Förstner 1 Department of Photogrammetry Institute of Geodesy and Geoinformation
Deep Learning. Vladimir Golkov Technical University of Munich Computer Vision Group
Deep Learning Vladimir Golkov Technical University of Munich Computer Vision Group 1D Input, 1D Output target input 2 2D Input, 1D Output: Data Distribution Complexity Imagine many dimensions (data occupies
Warm-up. Translations Using arrow notation to write a rule. Example: 1) Write a rule that would move a point 3 units to the right and 5 units down.
Translations Using arrow notation to write a rule. Example: 1) Write a rule that would move a point 3 units to the right and 5 units down. (x, y) 2) Write a rule that would move a point 6 units down. (x,
Supplementary material for: BO-HB: Robust and Efficient Hyperparameter Optimization at Scale
Supplementary material for: BO-: Robust and Efficient Hyperparameter Optimization at Scale Stefan Falkner 1 Aaron Klein 1 Frank Hutter 1 A. Available Software To promote reproducible science and enable
Motion Estimation for Video Coding Standards
Motion Estimation for Video Coding Standards Prof. Ja-Ling Wu Department of Computer Science and Information Engineering National Taiwan University Introduction of Motion Estimation The goal of video compression
An introduction to 3D image reconstruction and understanding concepts and ideas
Introduction to 3D image reconstruction An introduction to 3D image reconstruction and understanding concepts and ideas Samuele Carli Martin Hellmich 5 febbraio 2013 1 icsc2013 Carli S. Hellmich M. (CERN)
Perceptrons and Backpropagation. Fabio Zachert Cognitive Modelling WiSe 2014/15
Perceptrons and Backpropagation Fabio Zachert Cognitive Modelling WiSe 2014/15 Content History Mathematical View of Perceptrons Network Structures Gradient Descent Backpropagation (Single-Layer-, Multilayer-Networks)
Protecting Web Servers from Web Robot Traffic
Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) 11-14-2014 Protecting Web Servers from Web Robot Traffic Derek Doran
Non-Differentiable Image Manifolds
The Multiscale Structure of Non-Differentiable Image Manifolds Michael Wakin Electrical l Engineering i Colorado School of Mines Joint work with Richard Baraniuk, Hyeokho Choi, David Donoho Models for
Compressive Sensing for Multimedia. Communications in Wireless Sensor Networks
Compressive Sensing for Multimedia 1 Communications in Wireless Sensor Networks Wael Barakat & Rabih Saliba MDDSP Project Final Report Prof. Brian L. Evans May 9, 2008 Abstract Compressive Sensing is an
Parallel Stochastic Gradient Descent
University of Montreal August 11th, 2007 CIAR Summer School - Toronto Stochastic Gradient Descent Cost to optimize: E z [C(θ, z)] with θ the parameters and z a training point. Stochastic gradient: θ t+1
In stochastic gradient descent implementations, the fixed learning rate η is often replaced by an adaptive learning rate that decreases over time,
Chapter 2 Although stochastic gradient descent can be considered as an approximation of gradient descent, it typically reaches convergence much faster because of the more frequent weight updates. Since
Deep Learning with Tensorflow AlexNet
Machine Learning and Computer Vision Group Deep Learning with Tensorflow http://cvml.ist.ac.at/courses/dlwt_w17/ AlexNet Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification
Robust Regression. Robust Data Mining Techniques By Boonyakorn Jantaranuson
Robust Regression Robust Data Mining Techniques By Boonyakorn Jantaranuson Outline Introduction OLS and important terminology Least Median of Squares (LMedS) M-estimator Penalized least squares What is
Introduction.
Product information Image Systems AB Main office: Ågatan 40, SE-582 22 Linköping Phone +46 13 200 100, fax +46 13 200 150 info@imagesystems.se, Introduction TEMA Automotive is the world leading system
White Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space
White Pixel Artifact Caused by a noise spike during acquisition Spike in K-space sinusoid in image space Susceptibility Artifacts Off-resonance artifacts caused by adjacent regions with different
The Mathematics Behind Neural Networks
The Mathematics Behind Neural Networks Pattern Recognition and Machine Learning by Christopher M. Bishop Student: Shivam Agrawal Mentor: Nathaniel Monson Courtesy of xkcd.com The Black Box Training the
Deep Learning and Its Applications
Convolutional Neural Network and Its Application in Image Recognition Oct 28, 2016 Outline 1 A Motivating Example 2 The Convolutional Neural Network (CNN) Model 3 Training the CNN Model 4 Issues and Recent
Gaze interaction (2): models and technologies
Gaze interaction (2): models and technologies Corso di Interazione uomo-macchina II Prof. Giuseppe Boccignone Dipartimento di Scienze dell Informazione Università di Milano boccignone@dsi.unimi.it http://homes.dsi.unimi.it/~boccignone/l
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting.
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting. Mohammad Mahmudul Alam Mia, Shovasis Kumar Biswas, Monalisa Chowdhury Urmi, Abubakar
FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu
FMA901F: Machine Learning Lecture 3: Linear Models for Regression Cristian Sminchisescu Machine Learning: Frequentist vs. Bayesian In the frequentist setting, we seek a fixed parameter (vector), with value(s)
Lecture 17: Neural Networks and Deep Learning. Instructor: Saravanan Thirumuruganathan
Lecture 17: Neural Networks and Deep Learning Instructor: Saravanan Thirumuruganathan Outline Perceptron Neural Networks Deep Learning Convolutional Neural Networks Recurrent Neural Networks Auto Encoders
Notes on Multilayer, Feedforward Neural Networks
Notes on Multilayer, Feedforward Neural Networks CS425/528: Machine Learning Fall 2012 Prepared by: Lynne E. Parker [Material in these notes was gleaned from various sources, including E. Alpaydin s book
Speech Driven Synthesis of Talking Head Sequences
3D Image Analysis and Synthesis, pp. 5-56, Erlangen, November 997. Speech Driven Synthesis of Talking Head Sequences Peter Eisert, Subhasis Chaudhuri,andBerndGirod Telecommunications Laboratory, University
Contents. 1 Introduction Background Organization Features... 7
Contents 1 Introduction... 1 1.1 Background.... 1 1.2 Organization... 2 1.3 Features... 7 Part I Fundamental Algorithms for Computer Vision 2 Ellipse Fitting... 11 2.1 Representation of Ellipses.... 11
Studying Leakages on an Embedded Biometric System Using Side Channel Analysis
Studying Leakages on an Embedded Biometric System Using Side Channel Analysis M. Berthier, Y. Bocktaels, J. Bringer, H. Chabanne, T. Chouta, J-L. Danger, M. Favre, T. Graba Institut Mines-Télécom Outline
Index. Umberto Michelucci 2018 U. Michelucci, Applied Deep Learning,
A Acquisition function, 298, 301 Adam optimizer, 175 178 Anaconda navigator conda command, 3 Create button, 5 download and install, 1 installing packages, 8 Jupyter Notebook, 11 13 left navigation pane,
Advanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung
Advanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung Dr.-Ing. Carsten Bockelmann Institute for Telecommunications and High-Frequency Techniques Department of Communications
CafeGPI. Single-Sided Communication for Scalable Deep Learning
CafeGPI Single-Sided Communication for Scalable Deep Learning Janis Keuper itwm.fraunhofer.de/ml Competence Center High Performance Computing Fraunhofer ITWM, Kaiserslautern, Germany Deep Neural Networks
Scalable Compression and Transmission of Large, Three- Dimensional Materials Microstructures
Scalable Compression and Transmission of Large, Three- Dimensional Materials Microstructures William A. Pearlman Center for Image Processing Research Rensselaer Polytechnic Institute pearlw@ecse.rpi.edu
3D Digitization of a Hand-held Object with a Wearable Vision Sensor
3D Digitization of a Hand-held Object with a Wearable Vision Sensor Sotaro TSUKIZAWA, Kazuhiko SUMI, and Takashi MATSUYAMA tsucky@vision.kuee.kyoto-u.ac.jp sumi@vision.kuee.kyoto-u.ac.jp tm@i.kyoto-u.ac.jp
CPSC 340: Machine Learning and Data Mining. Principal Component Analysis Fall 2016
CPSC 340: Machine Learning and Data Mining Principal Component Analysis Fall 2016 A2/Midterm: Admin Grades/solutions will be posted after class. Assignment 4: Posted, due November 14. Extra office hours:
A novel approach to motion tracking with wearable sensors based on Probabilistic Graphical Models
A novel approach to motion tracking with wearable sensors based on Probabilistic Graphical Models Emanuele Ruffaldi Lorenzo Peppoloni Alessandro Filippeschi Carlo Alberto Avizzano 2014 IEEE International
Digital Image Processing
Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments
Wednesday, November 7, 2018
Wednesday, November 7, 2018 Warm-up Use the grid from yesterday s warm-up space to plot the pre-image ABCD and the points that are transformed by the rule (x, y) (2x, 2y) 5 2 2 5 2 4 0 0 Talk about quiz
Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks
Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks Ritika Luthra Research Scholar Chandigarh University Gulshan Goyal Associate Professor Chandigarh University ABSTRACT Image Skeletonization
Function Approximation. Pieter Abbeel UC Berkeley EECS
Function Approximation Pieter Abbeel UC Berkeley EECS Outline Value iteration with function approximation Linear programming with function approximation Value Iteration Algorithm: Start with for all s.
Geometry: Outline. Projections. Orthographic Perspective
Geometry: Cameras Outline Setting up the camera Projections Orthographic Perspective 1 Controlling the camera Default OpenGL camera: At (0, 0, 0) T in world coordinates looking in Z direction with up vector
Multiple Model Estimation : The EM Algorithm & Applications
Multiple Model Estimation : The EM Algorithm & Applications Princeton University COS 429 Lecture Dec. 4, 2008 Harpreet S. Sawhney hsawhney@sarnoff.com Plan IBR / Rendering applications of motion / pose
Lecture on Modeling Tools for Clustering & Regression
Lecture on Modeling Tools for Clustering & Regression CS 590.21 Analysis and Modeling of Brain Networks Department of Computer Science University of Crete Data Clustering Overview Organizing data into
Binary Images Clustering with k-means
Binary Images Clustering with k-means João Ferreira Nunes Instituto Politécnico de Viana do Castelo joao.nunes@estg.ipvc.pt Faculdade de Engenharia da Universidade do Porto pro09001@fe.up.pt Presentation
Introduction to Computer Graphics 4. Viewing in 3D
Introduction to Computer Graphics 4. Viewing in 3D National Chiao Tung Univ, Taiwan By: I-Chen Lin, Assistant Professor Textbook: E.Angel, Interactive Computer Graphics, 5 th Ed., Addison Wesley Ref: Hearn
Learning Log Title: CHAPTER 6: TRANSFORMATIONS AND SIMILARITY. Date: Lesson: Chapter 6: Transformations and Similarity
Chapter 6: Transformations and Similarity CHAPTER 6: TRANSFORMATIONS AND SIMILARITY Date: Lesson: Learning Log Title: Date: Lesson: Learning Log Title: Chapter 6: Transformations and Similarity Date: Lesson:
Hyperparameter optimization. CS6787 Lecture 6 Fall 2017
Hyperparameter optimization CS6787 Lecture 6 Fall 2017 Review We ve covered many methods Stochastic gradient descent Step size/learning rate, how long to run Mini-batching Batch size Momentum Momentum
COSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor
COSC579: Scene Geometry Jeremy Bolton, PhD Assistant Teaching Professor Overview Linear Algebra Review Homogeneous vs non-homogeneous representations Projections and Transformations Scene Geometry The
How Learning Differs from Optimization. Sargur N. Srihari
How Learning Differs from Optimization Sargur N. srihari@cedar.buffalo.edu 1 Topics in Optimization Optimization for Training Deep Models: Overview How learning differs from optimization Risk, empirical
Practice Exam Sample Solutions
CS 675 Computer Vision Instructor: Marc Pomplun Practice Exam Sample Solutions Note that in the actual exam, no calculators, no books, and no notes allowed. Question 1: out of points Question 2: out of
Image Reconstruction Using Multi-activation Wavelet Network
Australian Journal of Basic and Applied Sciences, 6(6): 410-417, 2012 ISSN 1991-8178 Image Reconstruction Using Multi-activation Wavelet Network 1 Prof. Dr. Waleed A. Mahmud Al-Jouhar, 1 Mayce Ibrahim
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning Tianyi Chen Georgios Giannakis Tao Sun Wotao Yin UMN, ECE UCLA, Math NeurIPS 208 Overview M := {,...,M} aaacaxicbvdlssnafj3uv62vqbvbzwarxjssseerhiibn4uk9gfnkjpjpb06mqkze6geuvfx3lhqxk/4c6/cdpmoa0hlhzouzd77wksrpv2ng+rslk6tr5r3cxtbe/s7tn7b20luoljcwsmzddaijdksuttzug3kqtfasodyhqz9tsprcoq+l0ej8sp0ydtigkkjds3jzipiwybk6trl3mrhgufvpwgn+nbzafqzacxizutmsjr7ntfxihwghoumunk9vwn0x6gpkaykunjsxvjeb6haekzylfmlj/nppjau6oemblsfndwpv6eyfcsdgotgem9fatelpxp6+x6ujszyhpuk04ni+kugagnm4yeglwzqndufyunmrxemkedymtjijwv8ezm0z6uuu3xvauv6ly+jci7bctgdlrgadxalmqafmhgez+avvflpovbn3mwwtwpnmi/sd6/aeiejx9
Abstract Purpose: Results: Methods: Conclusions:
Abstract Purpose: Current gaze-tracking systems can monitor point of gaze allowing for free head movement, but do not perform well when used with the large display surfaces and wide range of head positions
So we have been talking about 3D viewing, the transformations pertaining to 3D viewing. Today we will continue on it. (Refer Slide Time: 1:15)
Introduction to Computer Graphics Dr. Prem Kalra Department of Computer Science and Engineering Indian Institute of Technology, Delhi Lecture - 8 3D Viewing So we have been talking about 3D viewing, the
Deep Learning. Volker Tresp Summer 2014
Deep Learning Volker Tresp Summer 2014 1 Neural Network Winter and Revival While Machine Learning was flourishing, there was a Neural Network winter (late 1990 s until late 2000 s) Around 2010 there
Optimization. Industrial AI Lab.
Optimization Industrial AI Lab. Optimization An important tool in 1) Engineering problem solving and 2) Decision science People optimize Nature optimizes 2 Optimization People optimize (source: http://nautil.us/blog/to-save-drowning-people-ask-yourself-what-would-light-do)
Convolution Neural Networks for Chinese Handwriting Recognition
Convolution Neural Networks for Chinese Handwriting Recognition Xu Chen Stanford University 450 Serra Mall, Stanford, CA 94305 xchen91@stanford.edu Abstract Convolutional neural networks have been proven
Machine Learning on Physical Robots
Machine Learning on Physical Robots Alfred P. Sloan Research Fellow Department or Computer Sciences The University of Texas at Austin Research Question To what degree can autonomous intelligent agents
Machine Learning Basics. Sargur N. Srihari
Machine Learning Basics Sargur N. srihari@cedar.buffalo.edu 1 Overview Deep learning is a specific type of ML Necessary to have a solid understanding of the basic principles of ML 2 Topics Stochastic Gradient
9/20/2017 High School Geometry Common Core G.SRT.A.1 - Dilation Properties - Teacher Notes - Patterson
HOME Organize UNIT #1 UNIT #2 UNIT #3 UNIT #4 UNIT #5 UNIT #6 Resources Assessment Notes Teaching Items Teacher Notes CONCEPT 1 The properties and characteristics of dilations. Dilation is the term that
COMPUTATIONAL INTELLIGENCE SEW (INTRODUCTION TO MACHINE LEARNING) SS18. Lecture 6: k-nn Cross-validation Regularization
COMPUTATIONAL INTELLIGENCE SEW (INTRODUCTION TO MACHINE LEARNING) SS18 Lecture 6: k-nn Cross-validation Regularization LEARNING METHODS Lazy vs eager learning Eager learning generalizes training data before
Neural Networks for Obstacle Avoidance
Neural Networks for Obstacle Avoidance Joseph Djugash Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 josephad@andrew.cmu.edu Bradley Hamner Robotics Institute Carnegie Mellon University
CT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION
CT NOISE POWER SPECTRUM FOR FILTERED BACKPROJECTION AND ITERATIVE RECONSTRUCTION Frank Dong, PhD, DABR Diagnostic Physicist, Imaging Institute Cleveland Clinic Foundation and Associate Professor of Radiology
Memory Bandwidth and Low Precision Computation. CS6787 Lecture 10 Fall 2018
Memory Bandwidth and Low Precision Computation CS6787 Lecture 10 Fall 2018 Memory as a Bottleneck So far, we ve just been talking about compute e.g. techniques to decrease the amount of compute by decreasing
3D object recognition used by team robotto
3D object recognition used by team robotto Workshop Juliane Hoebel February 1, 2016 Faculty of Computer Science, Otto-von-Guericke University Magdeburg Content 1. Introduction 2. Depth sensor 3. 3D object
Local Image Registration: An Adaptive Filtering Framework
Local Image Registration: An Adaptive Filtering Framework Gulcin Caner a,a.murattekalp a,b, Gaurav Sharma a and Wendi Heinzelman a a Electrical and Computer Engineering Dept.,University of Rochester, Rochester,
Nonlinear projections. Motivation. High-dimensional. data are. Perceptron) ) or RBFN. Multi-Layer. Example: : MLP (Multi(
Nonlinear projections Université catholique de Louvain (Belgium) Machine Learning Group http://www.dice.ucl ucl.ac.be/.ac.be/mlg/ 1 Motivation High-dimensional data are difficult to represent difficult
Energy Based Models, Restricted Boltzmann Machines and Deep Networks. Jesse Eickholt
Energy Based Models, Restricted Boltzmann Machines and Deep Networks Jesse Eickholt ???? Who s heard of Energy Based Models (EBMs) Restricted Boltzmann Machines (RBMs) Deep Belief Networks Auto-encoders