Digital Image Processing Lecture 1: Introduction

Digital Image Processing Lecture 1: Introduction Prof. Charlene Tsai tsaic@cs.ccu.edu.tw http://www.cs.ccu.edu.tw/~tsaic/teaching/spring2006_dip/main.html

Why digital image processing? • Image is better than any other information form for human being to perceive. • Humans are primarily visual creatures – above 90% of the information about the world (a picture is better than a thousand words) • However, vision is not intuitive for machines • projection of 3D world to 2D images => loss of information • interpretation of dynamic scenes, such as a moving camera and moving objects

What is digital image processing? • Image understanding, image analysis, and computer vision aim to imitate the process of human vision electronically • Image acquisition • Preprocessing • Segmentation • Representation and description • Recognition and interpretation

General procedures • Goal: to obtain similar effect provided by biological systems • Two-level approaches • Low level image processing. Very little knowledge about the content or semantics of images • High level image understanding. Imitating human cognition and ability to infer information contained in the image.

Low level image processing • Very little knowledge about the content of the images. • Data are the original images, represented as matrices of intensity values, i.e. sampling of a continuous field using a discrete grid. • Focus of this course.

Low level image processing 3x3 neighborhood Origin (Ox,Oy) Pixel Value Pixel Region Spacing (Sy) Spacing (Sx)

Low level image processing • Image compression • Noise reduction • Edge extraction • Contrast enhancement • Segmentation • Thresholding • Morphology • Image restoration

Low level image processing • Image compression • Noise reduction • Edge extraction • Contrast enhancement • Segmentation • Thresholding • Morphology • Image restoration Dilation Erosion

Low level image processing • Image compression • Noise reduction • Edge extraction • Contrast enhancement • Segmentation • Thresholding • Morphology • Image restoration

High level image understanding • To imitate human cognition according to the information contained in the image. • Data represent knowledge about the image content, and are often in symbolic form. • Data representation is specific to the high-level goal.

High level image understanding • What are the high-level components? • What tasks can be achieved? Landmarks (bifurcation/crossover) Traces (vessel centerlines)

Applications • Medicine • Defense • Meteorology • Environmental science • Manufacture • Surveillance • Crime investigation

Applications: Medicine CT (computed Tomography) PET (Positron Emission Tomography PET/CT

Applications: Meteorology

Applications: Environmental Science

Applications: Manufacture

Application: Surveillance Car Tracking Project from CMU: Tracking cars in the surrounding road scene and then generating a "bird's eye view" of the road. Courtesy of Simon Baker: http://www.ri.cmu.edu/projects/project_526.html

Applications: Crime Investigation Fingerprintenhancement

What are the difficulties? • Poor understanding of the human vision system Do you see a young or an old lady?

What are the difficulties? • Human vision system tends to group related regions together, not odd mixture of the two alternatives. • Attending to different regions or contours initiate a change of perception • This illustrates once more that vision is an active process that attempts to make sense of incoming information.

What are the difficulties? • The interpretation is based heavily on prior knowledge.

Just some fun visual perception games Can you count the dots?

More … Do you see squares? More at http://scientificpsychic.com/graphics/index.html

Example: Detection of ozone layer hole Over the Antarctic, normal value around 300 DU

Class Format – Efficiency of Learning • What we read 10% • What we hear 20% • What we see 30% • What we hear + see 50% • What we say ourselves 70% • What we do ourselves 90%

Class Format – Efficiency of Learning • This leads to in-class discussion and quizzes. • 50-minute lecture • Remaining for group discussion & in-class quiz

Course requirements • In-class quizzes 10% • 5 Homework assignments 30% • Final project 20% • Midterm exam 20% • Final exam 20% • Peer learning is encouraged • BUT, NO PLAGIARISM!!! (20% deduction if caught)

Textbooks • Problems in picking a good textbook: • Hard to find a textbook of the right level --- too easy or too hard. • Hard to find a textbook of the right price --- good books tend to be too expensive • Prescribed: • Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing. Prentice Hall; 2nd edition, 2002 • Other references (used in 2005): • Alasdair McAndrew: Introduction to Digital Image Processing with Matlab, 2004.

Programming Tools • Matlab with Image Processing Toolbox for homework exercises • MATLAB Tutorial: http://www.mathworks.com/products/matlab/matlab_tutorial.html • MATLAB documentation: http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.shtml • User-contributed MATLAB IP functions: http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do?objectType=category&objectId=26

More on Matlab • University of Colorado Matlab Tutorials: • A decent collection of Matlab tutorials, including one focusing on image processing • http://amath.colorado.edu/computing/Matlab/tutorials.html • http://amath.colorado.edu/courses/4720/2000Spr/Labs/Worksheets/Matlab_tutorial/matlabimpr.html

Term project • Group project of 2~3 people • I decide the format of the term project • You decide your own topic that interests you • So, starting thinking about it!!! • You may implement your project with any programming language of your preference.

In-class quiz • Goal: to enhance learning • Open-book/open-notes format • Group effort of 2~3 people to encourage discussion and peer learning

Looking ahead: lecture2 • Image types • File format • Matlab programming.

Digital Image Processing Lecture 1: Introduction