Computer Vision CS 682: Understanding Visual Perception and Building Machine Systems, Study notes of Computer Science

Information about computer vision (cs 682) course offered by jana kosecka at george mason university. The goals, prerequisites, required textbooks, and software for the course. It also discusses the biological motivations behind computer vision, the challenges in image understanding, and the synergies with other disciplines and applications. The course focuses on geometry of single and multiple views, object detection and recognition, and modeling with multiple images.

Typology: Study notes

Pre 2010

Uploaded on 02/10/2009

koofers-user-6fj
koofers-user-6fj 🇺🇸

9 documents

1 / 8

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
1
Computer Vision
CS 682
Jana Kosecka
http://cs.gmu.edu/~kosecka/cs682.html
2-9827
Logistics
Grading: Homeworks (about every 2 weeks) 30% Midterm: 30% Fin al
project: 40%
Prerequisites: linear algebra, calculus
Required Text:
From Ima ges to Geometr ic Models: Y. Ma, S. Soatto, J.Kosecka an d S.
Sastry, Springer Verlag 2003
Introductory Techniques for 3D Computer Vis ion
(E. Trucco, A. Verri, Prentice Hall, 1998)
Computer Vision a Modern Approach
(D. Forsyth, J. Ponce, Prentice Hall 2002)
Required Software MATLAB (with Image Processing toolbox)
Biological motivations
Understanding visual sensing modality and its role
- We have no difficulties to navigate, manipulate objects
recognize familiar places and faces
- How can we successfully carry out all these everyday tasks ?
- How does the visual perception mediates these activities ?
- Overall system
Sensation and perception – int egrate and interpret sensory readings
Behavior – control musc les and glands to mediate some behavior
Memory – long term me mory (declarative – faces, places, events)
short term memory (procedural associations, skills)
Higher level functions – internal models and r epresentations of the
environments
Goal of Computer Vision
Build machines and develop algorithms which can
automatically replicate some funcionalities of
biological visual system
- Systems which navigate in cluterred environments
- Systems which can recognize objects, activities
- Systems which can interact with humans/world
Synergies with other disciplines and various applications
Artificial Intelligence ( Robotics, Natural Language
Understanding)
Vision as a sensor – medical imaging, Geospatial Imaging,
robotics
visual surveilance, inspection
pf3
pf4
pf5
pf8

Partial preview of the text

Download Computer Vision CS 682: Understanding Visual Perception and Building Machine Systems and more Study notes Computer Science in PDF only on Docsity!

Computer Vision CS 682

Jana Kosecka

http://cs.gmu.edu/~kosecka/cs682.html

[email protected]

Logistics

  • Grading: Homeworks (about every 2 weeks) 30% Midterm: 30% Final project: 40%
  • Prerequisites: linear algebra, calculus
  • Required Text:
  • From Images to Geometric Models: Y. Ma, S. Soatto, J.Kosecka and S. Sastry, Springer Verlag 2003
  • Introductory Techniques for 3D Computer Vision (E. Trucco, A. Verri, Prentice Hall, 1998)
  • Computer Vision a Modern Approach (D. Forsyth, J. Ponce, Prentice Hall 2002)
  • Required Software MATLAB (with Image Processing toolbox)

Biological motivations

Understanding visual sensing modality and its role

  • We have no difficulties to navigate, manipulate objects recognize familiar places and faces
  • How can we successfully carry out all these everyday tasks?
  • How does the visual perception mediates these activities?
  • Overall system Sensation and perception – integrate and interpret sensory readings Behavior – control muscles and glands to mediate some behavior Memory – long term memory (declarative – faces, places, events) short term memory (procedural – associations, skills) Higher level functions – internal models and representations of the environments

Goal of Computer Vision

  • Build machines and develop algorithms which can automatically replicate some funcionalities of biological visual system
  • Systems which navigate in cluterred environments
  • Systems which can recognize objects, activities
  • Systems which can interact with humans/world

Synergies with other disciplines and various applications Artificial Intelligence ( Robotics, Natural Language Understanding) Vision as a sensor – medical imaging, Geospatial Imaging, robotics visual surveilance, inspection

Computer VisionComputer Vision

Visual SensingVisual Sensing

Images I(x,y) –Images I(x,y)– brightness patternsbrightness patterns

  • image appearance depends on structure of the scene
  • material and reflectance properties of the objects
  • position and strength of light sources

Challenges/IssuesChallenges/Issues

  • About 60% of our brain is devoted to vision
  • We see immediately and can form and understand

images instantly

  • Several strategies for forming a representation

of the scenes

  • Detailed representations are often not necessary
  • Different approaches in the past Animate Vision

(biologically inspired), Purposive Vision (depending

on the task/purpose)

  • Recovery of the properties of the environment•Recovery of the properties of the environment

from single or multiple viewsfrom single or multiple views

Vision problems (towards image understanding)Vision problems (towards image understanding)

  • Segmentation•Segmentation
  • • RecognitionRecognition
  • • (^) ReconstructionReconstruction
  • • Vision Based ControlVision Based Control -- ActionAction

Visual CuesVisual Cues

SegmentationSegmentation –– partition image into separate objectspartition image into separate objects

  • Clustering and search algorithms in the space of visual cues•Clustering and search algorithms in the space of visual cues

Modeling with Multiple Images

University High School, Urbana, Illinois Three of Twelve Images

Image Based Rendering – View Interpolation

Vision-Based Control, Surveillance applications

  • continuously changing action in response to video input

Visual navigation (^) Automated Landing

Automated Driving

Visual surveillance

wide area surveillance, traffic monitoring Interpretation of different activities

Virtual and Augmented Reality, Human computer Interaction

Virtual object insertion various gesture based interfaces Interpretation of human activities Enabling technologies of intelligent homes, smart spaces

Topics

  • Image Formation, Representation of Camera

Motion, Camera Calibration

  • Image Features – filtering, edge detection, point

feature detection

  • Image alignment, 3D structure and motion

recovery, stereo

  • Analysis of dynamic scenes, detection, tracking
  • Object detection and object recognition

Rigid body motion Image Formation Perspective Projection

z = 1

o

x

y x

q

z x z = q 3

y

Image plane

Basic Ingredients – Course Overview

Feature Detection and Correspodence

Examples

Example - Euclidean multi-view reconstruction

Euclidean Reconstruction Texture mapping, hole filling

Texture mapping

Modeling choices And Model recovery

How to use prior scene knowledge