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.

Slides:



Advertisements
Similar presentations
Scale & Affine Invariant Interest Point Detectors Mikolajczyk & Schmid presented by Dustin Lennon.
Advertisements

Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Su-ting, Chuang 2010/8/2. Outline Introduction Related Work System and Method Experiment Conclusion & Future Work 2.
1 Video Processing Lecture on the image part (8+9) Automatic Perception Volker Krüger Aalborg Media Lab Aalborg University Copenhagen
מערכות לבדיקת איכות חקלאיות: מערכת למיון פרחים מערכת לזיהוי ירקות הטכניון - מכון טכנולוגי לישראל : הפקולטה להנדסת חשמל - המעבדה לבקרה ורובוטיקה מגישים:
Project by Arie Kozak.  Mark it using personal biological visual system.
By shooting 2009/10/1. outline imTop overview imTop detection Finger Mobile Finger detection evaluation Mobile detection improvement.
Multi-Touch Navigation Engine Presented by Team Extra Touch: Chris Jones Shuopeng Yuan Nathan Wiedeback.
COMP322/S2000/L181 Pre-processing: Smooth a Binary Image After binarization of a grey level image, the resulting binary image may have zero’s (white) and.
Motion Detection And Analysis Michael Knowles Tuesday 13 th January 2004.
A new face detection method based on shape information Pattern Recognition Letters, 21 (2000) Speaker: M.Q. Jing.
Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de With, Senior Member, IEEE.
A 3D Approach for Computer-Aided Liver Lesion Detection Reed Tompkins DePaul Medix Program 2008 Mentor: Kenji Suzuki, Ph.D. Special Thanks to Edmund Ng.
CSSE463: Image Recognition Day 30 Due Friday – Project plan Due Friday – Project plan Evidence that you’ve tried something and what specifically you hope.
Stockman MSU Fall Computing Motion from Images Chapter 9 of S&S plus otherwork.
MULTIPLE MOVING OBJECTS TRACKING FOR VIDEO SURVEILLANCE SYSTEMS.
Tanmoy Mondal, Ashish Jain, and H. K. Sardana. Introdution the research advancement in the field of automatic detection of craniofacial structures has.
Multi-camera Video Surveillance: Detection, Occlusion Handling, Tracking and Event Recognition Oytun Akman.
Traffic Sign Recognition Jacob Carlson Sean St. Onge Advisor: Dr. Thomas L. Stewart.
Conference Room Laser Pointer System Preliminary Design Report Anna Goncharova Brent Hoover Alex Mendes.
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
Deep Green System for real-time tracking and playing the board game Reversi Nadav Erell Intro to Computational and Biological Vision, CS department, Ben-Gurion.
 Process of partitioning an image into segments  Segments are called superpixels  Superpixels are made up several pixels that have similar properties.
CSSE463: Image Recognition Day 30 This week This week Today: motion vectors and tracking Today: motion vectors and tracking Friday: Project workday. First.
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
The Correspondence Problem and “Interest Point” Detection Václav Hlaváč Center for Machine Perception Czech Technical University Prague
Filtering and Enhancing Images. Major operations 1. Matching an image neighborhood with a pattern or mask 2. Convolution (FIR filtering)
AdeptSight Image Processing Tools Lee Haney January 21, 2010.
UCF REU: Weeks 1 & 2. Gradient Code Gradient Direction of the Gradient: Calculating theta.
1 Ying-li Tian, Member, IEEE, Takeo Kanade, Fellow, IEEE, and Jeffrey F. Cohn, Member, IEEE Presenter: I-Chung Hung Advisor: Dr. Yen-Ting Chen Date:
Video Segmentation Prepared By M. Alburbar Supervised By: Mr. Nael Abu Ras University of Palestine Interactive Multimedia Application Development.
An Efficient Search Strategy for Block Motion Estimation Using Image Features Digital Video Processing 1 Term Project Feng Li Michael Su Xiaofeng Fan.
Efficient Matching of Pictorial Structures By Pedro Felzenszwalb and Daniel Huttenlocher Presented by John Winn.
Motion Analysis using Optical flow CIS750 Presentation Student: Wan Wang Prof: Longin Jan Latecki Spring 2003 CIS Dept of Temple.
Update September 21, 2011 Adrian Fletcher, Jacob Schreiver, Justin Clark, & Nathan Armentrout.
Expectation-Maximization (EM) Case Studies
3d Pose Detection Used by Kinect
Machine Vision Introduction to Using Cognex DVT Intellect.
Edges.
Segmentation of Vehicles in Traffic Video Tun-Yu Chiang Wilson Lau.
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.
A Tutorial on using SIFT Presented by Jimmy Huff (Slightly modified by Josiah Yoder for Winter )
Su-ting, Chuang 1. Outline Introduction Related work Hardware configuration Detection system Optimal parameter estimation framework Conclusion 2.
Motion Detection and Processing Performance Analysis Thomas Eggers, Mark Rosenberg Department of Electrical and Systems Engineering Abstract Histograms.
CSSE463: Image Recognition Day 29 This week This week Today: Surveillance and finding motion vectors Today: Surveillance and finding motion vectors Tomorrow:
Visual Tracking by Cluster Analysis Arthur Pece Department of Computer Science University of Copenhagen
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.
Real-time foreground object tracking with moving camera P Martin Chang.
Frank Bergschneider February 21, 2014 Presented to National Instruments.
MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.
Compression and Security of Surveillance Videos Exercise 6 – Shot Change Detection M 陳威佑.
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.
Motion tracking TEAM D, Project 11: Laura Gui - Timisoara Calin Garboni - Timisoara Peter Horvath - Szeged Peter Kovacs - Debrecen.
Over the recent years, computer vision has started to play a significant role in the Human Computer Interaction (HCI). With efficient object tracking.
Content Based Coding of Face Images
Things about pattern recognition OGD. Pattern recognition ● Simplify the input ● Extract features ● Process ● Learn? ● Output results.
Table of contents INTRODUCTION Background Problem Statement Scope of The Research Objective Method DESIGN SYSTEM TESTING AND EVALUATION CONCLUSION.
CS 4501: Introduction to Computer Vision Sparse Feature Detectors: Harris Corner, Difference of Gaussian Connelly Barnes Slides from Jason Lawrence, Fei.
Fig. 1. proFIA approach for peak detection and quantification
Traffic Sign Recognition Using Discriminative Local Features Andrzej Ruta, Yongmin Li, Xiaohui Liu School of Information Systems, Computing and Mathematics.
Image Primitives and Correspondence
Group 1: Gary Chern Paul Gurney Jared Starman
Tremor Detection Using Motion Filtering and SVM Bilge Soran, Jenq-Neng Hwang, Linda Shapiro, ICPR, /16/2018.
9th Lecture - Image Filters
Fits for Pinhole and FresnelZonePlate
Report 7 Brandon Silva.
Presentation transcript:

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 initial frames ( 前 10) Calculate Background image by 10 initial frames ( 前 10) Background subtraction ( 超過 10 之後 ) Background subtraction ( 超過 10 之後 )

Finger detection Detect lighter spot Connected component finger analyze Set finger result Image preprocessing

Smooth current image Smooth Image by Gaussian filter (kernel 3x3) Build integral image Calculate the M+2 by N+2 integral image of current M by N background subtracted image

Detect lighter spot Searching for region where the average intensity of inner part is higher enough then that of outer part Threshold related code (these 3 parameters can be adjusted) nThres = nBigArea * nSmlArea * m_nThresKernel / 100; Corners & Finger-size regions will be detected

Connected component Using previous finger candidates Using queue Calculate the mean position of each connected candidates Mark each connected component with groupID which is the position of the initial candidate

Finger analyze Calculate region center and energy of each connected component Accept as a Finger input if Sufficient Energy The Center is within its region

Result management Match current result to the previous Process for un-matched finger points Smooth the detection results Send results Copy current results from each camera