oleh tretiak © 20051 computer vision lecture 1: introduction

Oleh Tretiak © 2005 1

Computer Vision

Lecture 1: Introduction


Introduction: Administrative

• Instructor:– Oleh Tretiak– [email protected]– Course web site:

www.ece.drexel.edu/faculty/tretiak/Lviv/CV.html– Office: Lviv Polytechnic, Building 5, room 801– Office hours: Thursdays 12-2

• Textbook: Дэвид Форсайт, Жан Понс, (David Forsythe, Jean Ponce) Компьютерное зрение – современний подход, Вильямс (Москва, Санкт-Петербург, Киев), 2004

• Textbook web site: www.cs.berkley/~daf/book.html

mailto:[email protected]

http://www.cs.berkley/~daf/book.html


Syllabus (see course web site for more details)

1. Introduction, camera model

2. Linear Filters

3. Edge detection and texture

4. Multi-image and stereo

5. Segmentation and structural operations

6. Segmentation and probabilistic methods

7. Recognition through template matching

8. Classification and evaluation


Artificial Intelligence and Computer Vision

• Computer Vision: production of information about the physical world from optical sensors

• Type of information – Non-contact sensing– Interpreting symbol, e. g. optical character

recognition– Information about three-dimensional objects

(distance, obstacles)

• Computer vision is part of the functioning of autonomous agents


Computer Vision and Related Areas

• Image Processing: Formation and enhancement of images. For example, Computer Tomography

• Machine Vision: Automated sensing and classification in manufacturing

• Robot Vision: Control of vehicles and manufacturing devices

• Computer Graphics: Many computer and mathematical tools are shared with Computer Vision


Classes of Vision Tasks

• Reflexive– Full task consists of sensing and response.

• Sensor that actuates a supermarket checkout belt drive

• Multi-level– Reflexive task guided by dynamical process

• Optical character recognition• The dynamical process may be guided by an explicit

model of the object being analyzed


Conceptual Structure of Computer Vision

• Image-object relation– Physics and optics of cameras– Photometry– Color

• Early vision (first layer) – One image

• Edge detection• Texture

– Multiple images• Stereo vision for depth information • Shape from motion


Conceptual Structure

• Mid-level vision (second layer)– Segmentation

• Find objects in image by grouping similar areas• Find objects in sequence of images by finding

regions that move together


Structure of Vision

• High level vision (third layer)– Geometry:

• Model used to find known objects• Model used to find change of shape due to

motion

– Probability:• Classifiers to find objects• Templates


Lecture Outline

• Cameras and perspective projection (Section 1.1 in the textbook)


Pinhole Camera


Distant Objects Have Smaller Images


Parallel Lines Meet at Infinity


Equations of Projection

x’ = fx/zy’ = fy/zz’ = f


Common Approximations

• Projection equations are nonlinear• Weak perspective:

– Magnification is constant over a ‘thin’ object

• Orthographic:– x’ = x, y’ = y

• Affine– x’ = ax + by + cz + d– y’ = ex + fy + gz + h

• Accounts for object rotation, shift• Valid for small z changes (locally affine)


Real Cameras

• Lenses are used to collect more light– Pinhole camera admits very little light

• Lenses introduce distortions (geometric distortion, defocusing)

• Images are recorded with electronic sensors– Obtain rectangular arrays of numbers

oleh tretiak © 20051 computer vision lecture 1: introduction

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