ecen631 1 - class introductionece631web.groups.et.byu.net/lectures/ecen631 1 - class...

1/5/12

Copyright©2012 Robo3c Vision Lab Brigham Young University

Class Introduction

1/5/12 Copyright©2012 Robo3c Vision Lab Brigham Young University

Course Description

Textbook

Topics

Class Format

Grading

Projects


  Not an image processing or computer vision course

  Emphasis on real-time performance   Emphasis on engineering aspect   Review mathematical concepts   Algorithm study, review, and implementation   Assignments, team projects, team semester

project, final report, and exams

Course Overview


•  Machine Vision & Applications

•  3-D Vision

Geometry

Motion

•  Human vision is nature and seems easy

•  Single view or multiple views

•  Camera model, image processing, geometry

•  Image noise removal, feature detection, feature matching, reconstruction, and applications

Course Description


•  Machine Vision – Theory, Algorithms, & Applications •  Compute 3-D information from one or multiple views in

real time with hardware or software implementation. •  Study methods of using passive sensors for – Stereo vision – Motion analysis to achieve – 3-D information extraction – Static obstacle avoidance – Moving obstacle avoidance – Short-range Motion Estimation – Long-range Motion Estimation – Object recognition – Object localization

Course Objectives


Machine Vision E.R. Davies

Elsevier, 2005, 3rd Edition

Introductory Techniques for 3-D Computer Vision Emanuele Trucco & Alessandro Verri

Prentice Hall, 1998

An Invitation to 3-D Vision - From Images to Geometric Models

Yi Ma, Stefano Soatto, Jana Kosecka, Shankar Sastry Springer, 2004

References


References

Multiple View Geometry in Computer Vision Richard Hartley & Andrew Zisserman

Cambridge University Press, 2003

An Introduction to 3D Computer Vision Techniques and Algorithms

Boguslaw Cyganek & J. Paul Siebert Wiley, 2009

3D Computer Vision: Efficient Methods and

Applications Christian Wšhler

Springer-Verlag, 2009


Introduction to Machine Vision & 3-D Vision Introduction Theory, Algorithms, & Applications Features and Feature Detection Edge, Corner, Line, & Color Segmentation Imaging and Image Representation Imaging Devices Image Digitization Digital Image Properties Problems in Digital Images CCD vs. CMOS Camera Model & Calibration Camera Model and Geometry 3D Transformation Perspective Transformation Matrix Camera Calibration Camera Calibration Method

Topics


Stereo Vision Introduction Two Cameras – Stereo & Geometry Epipolar Geometry Stereo Correspondence Other Methods Motion Introduction Optical Flow Differential Method Feature-based Kalman filters

Topics


Image Acquisition

Noise Attenuation

Feature Extraction Differential Motion Analysis

Calibration

Recognition

Feature-Based Stereo

Intensity-Based Stereo

Features Optical Flow Shape from

Single Image

Feature-Based Motion Analysis

Optical Flow Analysis

3-D Motion 3-D Structure Object Localization

Object Identification

Camera/System Parameters

Image


Vision Algorithm Implementation

System on a Chip using FPGA Real-time vision processing Visual C++, OpenCV

“A rough, quickly calculated motion estimation is arguably more useful for robotic vision than a more

accurate, but slowly calculated estimate”


Class Format

•  January-February: 2 lectures a week & project discussion

•  March: 2 lectures a week, literature review, progress report, presentation

•  April: semester project

final report: conference proceeding format

demonstration and presentation


Assignments (30%): 6 assignments

Three team projects:

Machine vision inspection (5%)

Tennis ball catcher (15%)

Structure from Motion (10%)

One midterm exam (10%): in-class closed-book exam

One final exam (10%): in-class closed-book exam (04/17 7:00AM)

Semester Project (20%): quality (10%) demonstration & presentation (5%), final report (5%)

Grading


>=95: A 90: A- 85: B+ 80: B 75: B- 65: C <65:E

Historically: A(1/2), A-(1/4), B+ or below (1/4)

Grading


Assignments

OpenCV

Feature detection

Feature tracking

Camera calibration & distortion correction

Stereo calibration & rectification

Optical flow & time to impact

Motion field & structure from motion


Tennis Ball Catcher


  2~3 per team   25 shots   3 points each catch   Presenta3on 25 points (evaluated by the class)

  Presenta3on include algorithm, calibra3on, trajectory es3ma3on, etc.

Tennis Ball Catcher


Semester Project

  Preferably 3‐D   Camera live input   Real‐3me performance   Real‐3me response   Quality of project (10%)   Demonstra3on and presenta3on 5% (evaluated by the class)   Presenta3on in technical conference format   Final report 5% (technical paper format)


  Past projects – Rock, paper, scissors – Small ground robot – obstacle avoidance – Stereo vision – Gaze‐direc3on Input device for the PC – Es3ma3on of op3mal landing area – Structure from Mo3on

Semester Project


Schedule   Six assignments: follow the lecture schedule   Semester project proposal and presenta3on: 01/31   Real‐3me visual inspec3on project: 02/16   First exam: 03/06   Tennis ball catcher demonstra3on: 03/15   Tennis ball catcher presenta3on: 03/22   Structure from mo3on project: 03/29   Final exam: 04/17   Semester project demonstra3on and presenta3on: 04/05, 04/10


Smart Vehicle


Vision-Guided Mobile Robot


Gaze-direction Input device


3-D Face Model


Rock, paper, scissors

50 100 150 200 250 300 350 400 450 500

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40050 100 150 200 250 300 350 400 450

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50 100 150 200 250 300 350 400 450 500 550

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Vision-guided Path Planning


Threat Assessment


Stereo Vision


3-D Modeling


Structure from Motion


Optical Flow

3-D Pose Estimation

Roll = 0.3° Pitch = 10.8° Yaw = -36.4°

X Trans. = 1.3m Y Trans. = -0.7m Z Trans. = 3.6m

(0,0) (1,0)

(0,1)

(1,1)

•  Calculate the Pose of aircraft by solving the Orthogonal Procrustes Problem. •  Pose Estimation Scheme will be implemented on a Gumstix Embedded System

allowing for on-board vision processing.

1/5/12

Copyright©2012 Robo3c Vision Lab Brigham Young University


Light Saber


Body Tracking


Magic Mirror


Finger Paint

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