郑爱华《数字图像处理双语》教案-2017

安徽大学本科教学课程教案

课程代码：ZX36225

课程名称：数字图像处理（双语）

授课专业：计算机科学与技术/网络工程

授课教师：郑爱华

职称/学位：讲师/博士

开课时间：二○一六至二○一七学年第2学期

第4次课程教学方案

第4次教学活动设计

教学

环节

内容设计与手段

导

入

新

课

上节课主要介绍了视觉感知要素，包括：人眼的构造，眼睛中图像的形成和亮度适应和鉴别，

同时介绍了光和电磁波谱的相关概念。

本节课：主要介绍图像感知和获取的过程，包括：单个传感器、带状传感器、传感器阵列以

及简单的图像形成模型，接着，介绍了图像取样和量化的过程，讲述了相关概念和表示方法。

讲

授

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2.3 Image Sensing and Acquisition

2.3.1 Image Acquisition Using a Single Sensor

?Single sensor

?Inexpensive but slow

?Rotation

?

1.3.2Image Acquisition Using Sensor Strips

?≥ 4000 sensors

?CAT, MRI

?Flat bed

?Linear motion

?Sensor ring, 3D

2.3.3 Image Acquisition Using Sensor Arrays

?≥4000*4000 sensors

?no motion

?typical example: CCD (Charge Coupled Device)

2.3.4 A Simple Image Formation Model

Most of the images in which we are interested in this book are monochromatic images, whose values are said to span the gray scale, as discussed in Section 2.2. When an image is generated from a physical process, its values are proportional to energy radiated by a physical

source(e.g., electromagnetic waves).As a consequence, f(x, y) must be nonzero and finite; that is,

Appropriately, these are called the illumination and reflectance components and are denoted by i(x, y) and r(x, y), respectively. The two functions combine as a product to form f(x, y):

2.4 Image Sampling and Quantization 2.4.1Basic Concepts in Sampling and Quantization

Our objective in all: to generate digital images from sensed data. To create a digital image, we need to convert the continuous sensed data into digital form. This involves two processes: sampling：Digitizing the coordinate values

quantization：Digitizing the amplitude values

2.4.2 Representing Digital Images

The result of sampling and quantization is a matrix of real numbers. Assume that an image f(x, y) is sampled so that the resulting digital image has M rows and N columns.

2.4.3 Spatial and Gray-Level Resolution

Sampling is the principal factor determining the spatial resolution of an image. Spatial

resolution is the smallest discernible detail in an image.Gray-level resolution similarly refers

to the smallest discernible change in gray level. the number of gray levels is usually an integer power of 2, eg.: 8 bits, 16 bits it is not uncommon to refer to an L-level digital image of size

M*N as having a spatial resolution of M*N pixels and a gray-level resolution of L levels. We

will use this terminology from time to time in subsequent discussions.

归

纳

总

结

本次课主要介绍图像感知和获取的过程，包括：单个传感器、带状传感器、传感器阵列以及简单的图像形成模型，接着，介绍了图像取样和量化的过程，讲述了相关概念和表示方法。

第5次课程教学方案

第5次教学活动设计

with values from V are 8-adjacent if q is in the set N8(p). m-adjacency: Two pixels p and q with values from V are m-adjacent if, q is in N4(p), or q is in ND(p) and the set is empty(has no pixels whose values are from V).

Path：A (digital) path (or curve) from pixel p with coordinates (x, y) to pixel q with coordinates (s, t) is a sequence of distinct pixels with coordinates : ( x0 , y0), (x1 , y1), ... , (xn , yn).

Region and Boundary: R is a subset of pixels in an image. We call R a region of the image if R is a connected set.

The boundary (also called border or contour) of a region R is the set of pixels in the region that have one or more neighbors that are not in R.

Edge and Boundary：The boundary of a finite region forms a closed path and is thus a “global” concept.

Edges are formed from pixels with derivative values that exceed a preset threshold. Thus is a “local” concept that is based on a measure of gray-level discontinuity at a point.

2.5.3 Distance Measures

For pixels p, q, and z, with coordinates (x, y), (s, t), and (v, w), respectively, D is a distance function or metric if

The Euclidean distance between p and q is defined as

D4 distance (city-block distance)

D8 distance (chessboard distance)

2.5.4 Image Operations on a Pixel Basis

Arithmetic and logic operations are similarly defined between corresponding pixels in the images involved.

“Dividing one image by another”: specifically the division is carried out between corresponding pixels in the two images.

2.6 Linear and Nonlinear Operations