Su-ting, Chuang 1. Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 2.

Slides:



Advertisements
Similar presentations
A Natural Interactive Game By Zak Wilson. Background This project was my second year group project at University and I have chosen it to present as it.
Advertisements

© 2006 MVTec Software GmbH Press Colloquium Part II Building Technology for the Customer’s Advantage.
PlayAnywhere: A Compact Interactive Tabletop Projection-Vision System Professor : Tsai, Lian-Jou Student : Tsai, Yu-Ming PPT Production rate : 100% Date.
Vision-Based Finger Detection and Its Applications 基於電腦視覺之手指偵測及其應用 Yi-Fan Chuang Advisor: Prof. Yi-Ping Hung Prof. Ming-Sui Lee.
3D Laser Stripe Scanner or “A Really Poor Man’s DeltaSphere” Chad Hantak December 6, 2004.
VisHap: Guangqi Ye, Jason J. Corso, Gregory D. Hager, Allison M. Okamura Presented By: Adelle C. Knight Augmented Reality Combining Haptics and Vision.
Su-ting, Chuang 2010/8/2. Outline Introduction Related Work System and Method Experiment Conclusion & Future Work 2.
Luis Mejias, Srikanth Saripalli, Pascual Campoy and Gaurav Sukhatme.
Real-Time Accurate Stereo Matching using Modified Two-Pass Aggregation and Winner- Take-All Guided Dynamic Programming Xuefeng Chang, Zhong Zhou, Yingjie.
Reducing Drift in Parametric Motion Tracking
1 Towards Pervasive Connectivity in Mobile Computing Frank Siegemund European Microsoft Innovation Center November 2006.
Adviser : Ming-Yuan Shieh Student ID : M Student : Chung-Chieh Lien VIDEO OBJECT SEGMENTATION AND ITS SALIENT MOTION DETECTION USING ADAPTIVE BACKGROUND.
By shooting 2009/10/1. outline imTop overview imTop detection Finger Mobile Finger detection evaluation Mobile detection improvement.
Formation et Analyse d’Images Session 8
Modeling Pixel Process with Scale Invariant Local Patterns for Background Subtraction in Complex Scenes (CVPR’10) Shengcai Liao, Guoying Zhao, Vili Kellokumpu,
Computer Graphics Hardware Acceleration for Embedded Level Systems Brian Murray
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Soft Shadows using Hardware Cameras Kyle Moore COMP 870.
Motion based Correspondence for Distributed 3D tracking of multiple dim objects Ashok Veeraraghavan.
Computing motion between images
1 Pupil Detection and Tracking System Lior Zimet Sean Kao EE 249 Project Mentors: Dr. Arnon Amir Yoshi Watanabe.
Effective Gaussian mixture learning for video background subtraction Dar-Shyang Lee, Member, IEEE.
Touchscreen Implementation for Multi-Touch
Presented by Pat Chan Pik Wah 28/04/2005 Qualifying Examination
A Novel 2D To 3D Image Technique Based On Object- Oriented Conversion.
Paper by Alexander Keller
Jacinto C. Nascimento, Member, IEEE, and Jorge S. Marques
The objective of this senior design project was to design and build a multi-touch interface device that could allow users to interact with a computer application.
Image Subtraction for Real Time Moving Object Extraction Shahbe Mat Desa, Qussay A. Salih, CGIV’04.
Human Computer Interface based on Hand Tracking P. Achanccaray, C. Muñoz, L. Rojas and R. Rodríguez 4 th International Symposium on Mutlibody Systems and.
Overview and Mathematics Bjoern Griesbach
Conference Room Laser Pointer System Preliminary Design Report Anna Goncharova Brent Hoover Alex Mendes.
EE392J Final Project, March 20, Multiple Camera Object Tracking Helmy Eltoukhy and Khaled Salama.
MACHINE VISION GROUP Multimodal sensing-based camera applications Miguel Bordallo 1, Jari Hannuksela 1, Olli Silvén 1 and Markku Vehviläinen 2 1 University.
MULTI-TOUCH TABLE Athena Frazier Chun Lau Adam Weissman March 25, 2008 Senior Projects II.
Abstract Some Examples The Eye tracker project is a research initiative to enable people, who are suffering from Amyotrophic Lateral Sclerosis (ALS), to.
Supporting Beyond-Surface Interaction for Tabletop Display Systems by Integrating IR Projections Hui-Shan Kao Advisor : Dr. Yi-Ping Hung.
Fingertip Tracking Based Active Contour for General HCI Application Proceedings of the First International Conference on Advanced Data and Information.
3D Fingertip and Palm Tracking in Depth Image Sequences
Multimedia Specification Design and Production 2013 / Semester 2 / week 8 Lecturer: Dr. Nikos Gazepidis
BraMBLe: The Bayesian Multiple-BLob Tracker By Michael Isard and John MacCormick Presented by Kristin Branson CSE 252C, Fall 2003.
資訊碩一 蔡勇儀  Introduction  Method  Background generation and updating  Detection of moving object  Shape control points.
LoCaF: Detecting Real-World States with Lousy Wireless Cameras Benjamin Meyer, Richard Mietz, Kay Römer 1.
Supporting Beyond-surface Interaction for Tabletop Systems by Integrating IR Projections Hui-Shan Kao.
Object Stereo- Joint Stereo Matching and Object Segmentation Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on Michael Bleyer Vienna.
Adaptively Sampled Distance Fields Representing Shape for Computer Graphics Ronald N. Perry and Sarah F. Frisken Mitsubishi Electric Research Laboratories.
1 Webcam Mouse Using Face and Eye Tracking in Various Illumination Environments Yuan-Pin Lin et al. Proceedings of the 2005 IEEE Y.S. Lee.
A New Fingertip Detection and Tracking Algorithm and Its Application on Writing-in-the-air System The th International Congress on Image and Signal.
Image Pool. (a)(b) (a)(b) (a)(c)(b) ID = 0ID = 1.
Video Segmentation Prepared By M. Alburbar Supervised By: Mr. Nael Abu Ras University of Palestine Interactive Multimedia Application Development.
Online Kinect Handwritten Digit Recognition Based on Dynamic Time Warping and Support Vector Machine Journal of Information & Computational Science, 2015.
Projector Calibration of Interactive Multi-Resolution Display Systems 互動式多重解析度顯示系統之投影機校正 Presenter: 邱柏訊 Advisor: 洪一平 教授.
- Laboratoire d'InfoRmatique en Image et Systèmes d'information
Su-ting, Chuang 2010/8/2. Outline Introduction Related Work System and Method Experiment Conclusion & Future Work 2.
Su-ting, Chuang 2010/8/2. Outline Introduction Related Works System and Method Experiment Conclusion & Future Work 2.
By shooting 2009/6/22. Flow chart Load Image Undistotion Pre-process Finger detection Show result Send Result to imTop Calculate Background image by 10.
By shooting. Optimal parameters estimation Sample collect Various finger size Hard press and soft press Exhaustive search.
Images for paper By shooting. Sample collection Hard/Soft vertical touch Finger touch position 5 timer 2.
Su-ting, Chuang 1. Outline Introduction Related work Hardware configuration Finger Detection system Optimal parameter estimation framework Conclusion.
MULTI TOUCH. Introduction Multi-touch is a human-computer interaction technique. Consists of a touch screen as well as software that recognizes multiple.
What you need: In order to use these programs you need a program that sends out OSC messages in TUIO format. There are a few options in programs that.
Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen.
Enabling Beyond-Surface Interactions for Interactive Surface with An Invisible Projection Li-Wei Chan, Hsiang-Tao Wu, Hui-Shan Kao, Ju-Chun Ko, Home-Ru.
Contents Introduction Requirements Design Technology Working Interaction.
Over the recent years, computer vision has started to play a significant role in the Human Computer Interaction (HCI). With efficient object tracking.
Design and Calibration of a Multi-View TOF Sensor Fusion System Young Min Kim, Derek Chan, Christian Theobalt, Sebastian Thrun Stanford University.
Video object segmentation and its salient motion detection using adaptive background generation Kim, T.K.; Im, J.H.; Paik, J.K.;  Electronics Letters 
Hand Gestures Based Applications
Dynamo: A Runtime Codesign Environment
C. Canton1, J.R. Casas1, A.M.Tekalp2, M.Pardàs1
Report 2 Brandon Silva.
Presentation transcript:

Su-ting, Chuang 1

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 2

Introduction 3

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 4

Related Work FTIR (Frustrated Total Internal Reflection) J. Y. Han, “Low-cost multi-touch sensing through frustrated total internal reflection," in Proceedings of the 18th annual ACM symposium on User interface software and technology (UIST '05). New York, NY, USA: ACM Press, 2005, pp

Related Work DI (Diffused Illumination) J. Rekimoto and N. Matsushita, “Perceptual surfaces: Towards a human and object sensitive interactive display," Workshop on Perceptural User Interfaces (PUI'97),

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 7

Hardware configuration Table setup 8

Hardware configuration Order of diffuser layer and touch-glass layer 9 Diffuser layer IR illuminator IR camera spot IR illuminator IR camera Touch-glass layer IR camera spot IR camera

Hardware configuration Problem: IR rays will be reflected by the touch-glass and resulting IR spot regions in camera views Solution: Use other cameras to recover the regions which are sheltered by IR spots 10

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 11

Detection system IR cam Pre- processing Image processing Image processing Finger Detection Finger Detection Data Association Data Association Data Transmission Data Transmission IR cam GPU CPU 12

Detection system Pre-processing Image processing Image Fusion (Blend) IR Camera IR camera Undistortion HomoWarp Background Subtraction Normalization Simple Highpass MonoThreshold 13 I (x,y) = a x I 1 (x,y) + (1-a) x I 2 (x,y)

Pre-processing Undistortion Undistort camera image Warp Unify finger size among different position of table Image fusion Increase intuition of vision Simplify foreground object matching among cameras 14

Pre-processing Advantage of implementing on GPU Increase performance High frame rate Preserve CPU for application computation Enable detection system and interactive application on the same computer Reduce unsynchronized problem among different computers 15

Image processing Normalization Motivation Eliminate influence due to ununiform lighting condition Various finger touch response Hard to decide a good threshold Method Model each pixel’s dynamic range Using specific material to simulate foreground Stretch dynamic range to

Image processing Finger Detection Connected component Finger analyzer finger size evaluation 17

Data association Fingertip matching Matching fingertips among frames Using bipartite algorithm Fingertip tracking Smooth detected results and fix lost results Using Kalman filter 18

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 19

Software architecture 20 Detection system Sample set Training Parameter Set Detection Result Ground Truth Optimal Parameter Set Verify Next Parameter Set Generator Detection Result Ground Truth Error Rate Parameter Set

Optimal parameters estimation framework for finger detection Motivation Parameter set Procedure Collect samples Various finger size Hard press and soft press Search exhaustively Verify performance of all possible parameter combinations 21

Optimal parameters estimation framework for finger detection Task Soft /Hard touch Vertical/Oblique touch Various fingers Sample set Each task has 2x2x5 samples Sample collection Step-by-step instruction Straightforward UI design Finger touch position 5 timer Instructions…. 22

Method Exhaustive search Test various parameter combination in each set Step Each parameter combination Detect finger touch Calculate precision and error rate 23

Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 24

25

Sample collection Hard/Soft vertical touch Finger touch position 5 timer 26

Background Subtraction Normalization Simple Highpass MonoThreshold 27

Image Fusion (Blend) IR Camera IR camera Undistortion HomoWarp 28

Detection Module Verify Next Parameter Set Generator Detection Result Ground Truth Error Rate Parameter Set Parameter Set’ 29

30

31 Detection system Sample set Training Parameter Set Detection Result Ground Truth Optimal Parameter Set

32 Verify Next Parameter Set Generator Detection Result Ground Truth Error Rate

33 Detection system Sample set Training Parameter Set Detection Result Ground Truth Optimal Parameter Set Verify Next Parameter Set Generator Detection Result Ground Truth Error Rate Parameter Set

34