## CS/ECE 545 Class 1

Introduction

This was an introductory class in which we discussed the course mechanics and key concepts of Image Processing.

Course Mechanics

• 14 Classes: 6 lectures, midterm exam, 6 lectures, final exam, project presentations. See the Syllabus.
• Text book is Gonzalez and Woods, Digital Image Processing, 3rd ed, 2008, Pearson Education, Inc..
• Group projects will be described in Class 8.
• Grading is 20% for project (everyone in group gets the same), 20% for each of two exams, 20% for class participation, 20% for homework (10 HW assignments at 2% each).

Philosophical Stuff

We discussed what is and what is not part of the course material. And we reserved the right to modify the syllabus once we get a better idea of the backgrounds and needs of the participants.

Digital Images

A digital image is just a two-dimensional matrix of numbers, each of which represents a color - which can include shades of gray. To be perceived as an image by a human, uniform, regular patches of the corresponding colors are projected on a screen or printed. These patches are called picture elments or pixels. An inherent resolution limit is imposed by the number of pixels in a digital image. If the image is enlarged without altering the number of pixels, the uniform patches just become larger.

Digital Images

In this course, an "image" is just a matrix of numbers (usually bytes) which represent the brightness of a corresponding picture element, pixel. The animated image at the top of the course home page is the same image with resolutions in factors of two from 4x1 pixels to 256x64 pixels. We showed examples of the difference between images created using other media (such as oil paints) and digital images. We also discussed how color images are represented - as three digital images that represent, respectively, the red, green, and blue content of the color image. We described the course image format.

Human Vision System

The human vision system is capable of observing, in a scene, objects whose brighnesses vary by as much as a factor of 256 (rounded to the nearest factor of 2) and people can see up to 256 distinct gray levels (again, rounded to a factor of two). However, common objects with dull surface finishes only exhibit reflectivities which vary by only a factor of 32. Thus, a person can see more gray levels in a projected image or an image on a computer screen than in a printed image. We demonstrated this using the following image. It is presented in pdf format so that you can either print it or observe it on-screen.

We also showed that the maximum number of gray levels that are significant in digital images is about 256 so for most purposes, our digital images can be arrays of type `unsigned char`.

Images are usually designed to match the characteristics of the output device. We will discuss this more throughout the course. Since the maximum number of gray levels that are significant in digital images is about 256, pixels values are usually represented by bytes and digital images are usually arrays of type `unsigned char`.

We also showed Mach bands which demonstrate that the human vision system has a differentiator, and showed directly that the human vision system samples the image. That will be discussed in greater detail in class 2.

Linear Systems

We discussed the reasons that imaging systems, whenever possible, are described as linear systems and began the discussion of sampling theory.

CS/ECE 545 Staff
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