数字图像处理英语课件chapter2
For a pixel p at (x, y) , there are 4 horizontal and vertical neighbors: (x+1,y), (x-1,y), (x,y+1), (x,y-1), and 4 diagonal neighbors of p (x+1,y+1),(x+1,y-1),(x-1,y+1,(x-1,y-1), (ND(p)) This set of pixels , N8(p), is called the 8-neighbors of p.
– If (x0,y0) =(xn, yn) , the path is a closed path.
• Define 4-, 8-, or m-paths (see example above)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Region
Let R be a subset of pixels in an image. • Region: if R is a connected set. • Two regions, Ri and Rj are said to be adjacent if their union forms a connected set. • Regions that are not adjacent are said to be disjoint • 4- and 8-adjacency are considered when referring to regions
Connect
• Let S represent a subset of pixels in an image. • Two pixels p and q are said to be connected in S
– if there exists a path between them consisting entirely of pixels in S.
0 0 0 0 0 0
0 1 1 1 1 0
0 1 1 1 1 0
0 0 0 1 1 0
0 0 0 0 0 0
Boundary(cont’)
• The preceding definition is referred to as the inner border of the region to distinguish it from its outer border, which is the corresponding border in the background. • Such algorithm usually are formulated to follow the outer boundary in order to guarantee that the result will form a closed path.
Example of adjacency
Suppose V={1}
Ambiguity
(a)
(c)
Figure 2. (a) Arrangement of pixels; (b) pixels that are 8-adjacent (shown as dashed) to the center pixel; (c ) m-adjacency.
Adjacency
Let V be the set of gray-level values • 4-adjacency. – Two pixels p and q with gray-level values from V are 4-adjacency if q is in the set N4(p) • 8-adjacency. – Two pixels p and q with values from V are 8adjacency if q is in the set N8(p)
p p
Figure1. diagram of N4(p) and N8(p)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Connectivity
• Connectivity between pixels:
– To simplify concept (regions and boundaries)
• Two pixels may be 4-neighbors • But they are said to be connected only if they have the same value.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
0
1 1 1
0 0
0 0
Digital Image Processing, 2nd ed.
Region (cont.)
• Suppose that an image contains K disjoint regions, Rk, k=1,2,…,K, none of which touches the image border. • Let Ru denote the union of all the K regions and let (Ru)c denote its complement.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Example of region
1 1 1
1 1
1
1 1
© 2009 Lianxi Yuan
1 1
Figure 3. Two regions (of 1s) that are adjacent if 8-adjecency is used
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Example 1 of boundary
The point circled is not a member of border of the 1valued region if 4-connectivity is used between the region and its background.
– Be a sequence of distinct pixels with coordinates
• (x0,y0), (x1, y1), … (xn,yn) – where (x0, y0)= (x, y) ; (xn, yn)=(s, t) – Pixel (xi, yi) and (xi-1,yi-1) are adjacent for i in [1,n] – n is the length of the path
– All the points in Ru is called the foreground of the image – All the points in (Ru)c are called the background of the image
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Figure 4. The circled point is part of the boundary of the 1valued pixels only if 8-adjecency between the region and background is used
© 2009 Lianxi Yuan
Boundary
• The boundary (border, contour) of a region R is the set of points that are adjacent to points in the complement of R. • The border of a region is the set of pixels in the region that have at least one background neighbor (another definition).
Digital Image Processing, 2nd ed.
Chapter Two
Some Basic Relationship Between Pixels
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Path (curve)
• A path from pixel p with coordinate (x, y) to pixel q with coordinate (s, t)
• to Eliminate the ambiguities that arise when 8-adjacency is used
A intersection B (A∩B)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Neighbors of a el
Suppose an image f(x,y), and use lowercase letters to represent a pixel. For any pixel p at (x, y) , there are 4 horizontal and vertical neighbors: ( x, y 1),( x, y 1),( x 1, y),( x 1, y) This set of pixels denoted by N4(p) , is called the 4neighbors of p. The four diagonal neighbors of p , ND(p), have coordinates:
– If (x0,y0) =(xn, yn) , the path is a closed path.
• Define 4-, 8-, or m-paths (see example above)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Region
Let R be a subset of pixels in an image. • Region: if R is a connected set. • Two regions, Ri and Rj are said to be adjacent if their union forms a connected set. • Regions that are not adjacent are said to be disjoint • 4- and 8-adjacency are considered when referring to regions
Connect
• Let S represent a subset of pixels in an image. • Two pixels p and q are said to be connected in S
– if there exists a path between them consisting entirely of pixels in S.
0 0 0 0 0 0
0 1 1 1 1 0
0 1 1 1 1 0
0 0 0 1 1 0
0 0 0 0 0 0
Boundary(cont’)
• The preceding definition is referred to as the inner border of the region to distinguish it from its outer border, which is the corresponding border in the background. • Such algorithm usually are formulated to follow the outer boundary in order to guarantee that the result will form a closed path.
Example of adjacency
Suppose V={1}
Ambiguity
(a)
(c)
Figure 2. (a) Arrangement of pixels; (b) pixels that are 8-adjacent (shown as dashed) to the center pixel; (c ) m-adjacency.
Adjacency
Let V be the set of gray-level values • 4-adjacency. – Two pixels p and q with gray-level values from V are 4-adjacency if q is in the set N4(p) • 8-adjacency. – Two pixels p and q with values from V are 8adjacency if q is in the set N8(p)
p p
Figure1. diagram of N4(p) and N8(p)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Connectivity
• Connectivity between pixels:
– To simplify concept (regions and boundaries)
• Two pixels may be 4-neighbors • But they are said to be connected only if they have the same value.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
0
1 1 1
0 0
0 0
Digital Image Processing, 2nd ed.
Region (cont.)
• Suppose that an image contains K disjoint regions, Rk, k=1,2,…,K, none of which touches the image border. • Let Ru denote the union of all the K regions and let (Ru)c denote its complement.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Example of region
1 1 1
1 1
1
1 1
© 2009 Lianxi Yuan
1 1
Figure 3. Two regions (of 1s) that are adjacent if 8-adjecency is used
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Example 1 of boundary
The point circled is not a member of border of the 1valued region if 4-connectivity is used between the region and its background.
– Be a sequence of distinct pixels with coordinates
• (x0,y0), (x1, y1), … (xn,yn) – where (x0, y0)= (x, y) ; (xn, yn)=(s, t) – Pixel (xi, yi) and (xi-1,yi-1) are adjacent for i in [1,n] – n is the length of the path
– All the points in Ru is called the foreground of the image – All the points in (Ru)c are called the background of the image
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Figure 4. The circled point is part of the boundary of the 1valued pixels only if 8-adjecency between the region and background is used
© 2009 Lianxi Yuan
Boundary
• The boundary (border, contour) of a region R is the set of points that are adjacent to points in the complement of R. • The border of a region is the set of pixels in the region that have at least one background neighbor (another definition).
Digital Image Processing, 2nd ed.
Chapter Two
Some Basic Relationship Between Pixels
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Path (curve)
• A path from pixel p with coordinate (x, y) to pixel q with coordinate (s, t)
• to Eliminate the ambiguities that arise when 8-adjacency is used
A intersection B (A∩B)
© 2009 Lianxi Yuan
Digital Image Processing, 2nd ed.
Neighbors of a el
Suppose an image f(x,y), and use lowercase letters to represent a pixel. For any pixel p at (x, y) , there are 4 horizontal and vertical neighbors: ( x, y 1),( x, y 1),( x 1, y),( x 1, y) This set of pixels denoted by N4(p) , is called the 4neighbors of p. The four diagonal neighbors of p , ND(p), have coordinates:
合集下载
数字图像处理 第2章
2020/4/12
Digital Image Processing
第12页
2.4 保存图像
• imwirte函数可以将图像写到磁盘上，语法： • imwrite(f,’filename’)，filename中包含的字符串必须是
一种可识别的文件格式扩展名（见表2.1）。下面的命 令可将图像h写为TIFF格式，且名为xray。 >>imwrite(f,’xray’,’tif’)或者
>> [M,N]=size(I);
>> whos I
Name Size
Bytes Class
I 256x256
65536 uint8 array
Grand total is 65536 elements using 65536 bytes
2020/4/12
Digital Image Processing
图像取样: 图像空 间离散化, 确定图 像空间分辨率
图像量化:图像幅 度离散化, 确定图 像幅度分辨率
图像编码: 用较少 的bit表示量化后的 图像(JPEG)
Digital Image Processing
第3页
图像取样和量化
数字图像的质量很大程度上取决于取样和量化过程中所用 的取样数和灰度级, 图像内容是确定这两个参数的重要因素
2020/4/12
Digital Image Processing
第4页
数字图像用MN维矩阵表示 M和N分别表示图像行、列的取样点数(确定空间分辨率) 灰度级总数为L(确定灰度分辨率) 起始位置为f(0,0), 灰度级范围为[0, L-1] M、N和L通常取2的整数次幂, 如果L=2k, 则该图是k比特图
数字图像处理冈萨雷斯英文ChapterEng数字图像基础
第2页/共56页
10 10 16 28 13952536212425165539865576295081244057368976549650053326765972859436447663972399679278
Visual Perception: Human Eye
(Picture from Microsoft Encarta 2000)
第3页/共56页
Cross Section of the Human Eye
第4页/共56页
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Visual Perception: Human Eye (cont.)
I = {(x,a(x)): x X, a(x) F} where X and F are a point set and value set, respectively. w An element of the image, (x,a(x)) is called a pixel where
- x is called the pixel location and - a(x) is the pixel value at the location x
x
Origin
y
Image “After snow storm” f(x,y)
w An image: a multidimensional function of spatial coordinates. w Spatial coordinate: (x,y) for 2D case such as photograph,
10 10 16 28 13952536212425165539865576295081244057368976549650053326765972859436447663972399679278
Visual Perception: Human Eye
(Picture from Microsoft Encarta 2000)
第3页/共56页
Cross Section of the Human Eye
第4页/共56页
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Visual Perception: Human Eye (cont.)
I = {(x,a(x)): x X, a(x) F} where X and F are a point set and value set, respectively. w An element of the image, (x,a(x)) is called a pixel where
- x is called the pixel location and - a(x) is the pixel value at the location x
x
Origin
y
Image “After snow storm” f(x,y)
w An image: a multidimensional function of spatial coordinates. w Spatial coordinate: (x,y) for 2D case such as photograph,
数字信号处理课件 Chapter 2
➢In some applications, a discrete-time sequence {x[n]} may be generated by periodically sampling a continuous-time signal xa(t) at uniform(统一的) intervals(间隔) of time
➢Reciprocal(倒数) of sampling interval T,
denoted as FT , is called the sampling frequency(抽样频率):
FT=1/T
6
§2.1.1 Time-Domain Representation
➢Unit of sampling frequency is cycles per second, or hertz (Hz), if T is in seconds
3
etc.
§2.1.1 Time-Domain Representation
➢Graphical(图形的) representation of a discrete-time signal with real-valued samples is as shown below:
4
§2.1.1 Time-Domain Representation
8
§2.1.1 Time-Domain Representation
➢Example - {x[n]}={cos0.25n} is a real sequence {y[n]}={ej0.3n} is a
complex sequence
➢We can write
{y[n]}={cos0.3n + jsin0.3n}
➢Reciprocal(倒数) of sampling interval T,
denoted as FT , is called the sampling frequency(抽样频率):
FT=1/T
6
§2.1.1 Time-Domain Representation
➢Unit of sampling frequency is cycles per second, or hertz (Hz), if T is in seconds
3
etc.
§2.1.1 Time-Domain Representation
➢Graphical(图形的) representation of a discrete-time signal with real-valued samples is as shown below:
4
§2.1.1 Time-Domain Representation
8
§2.1.1 Time-Domain Representation
➢Example - {x[n]}={cos0.25n} is a real sequence {y[n]}={ej0.3n} is a
complex sequence
➢We can write
{y[n]}={cos0.3n + jsin0.3n}
数字图像处理课件 ch2
n Why do we need to understand visual perception?
n Human intuition plays an important role in the choice of processing technique
4
Simple questions
n What intensity differences can we distinguish?
n Retina Innermost membrane of the eye which lines inside of the wall’s entire posterior portion. When the eye is properly focused,light from an object outside the eye is imaged on the retina.
scene
reflection
18
Image sensors
n Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material
(continuous)
n usually content of the signal changes over some set of spatiotemporal dimensions.
n Vocabulary: Spatiotemporal: existing in both space and
time having both spatial extension and temporal duration
n Human intuition plays an important role in the choice of processing technique
4
Simple questions
n What intensity differences can we distinguish?
n Retina Innermost membrane of the eye which lines inside of the wall’s entire posterior portion. When the eye is properly focused,light from an object outside the eye is imaged on the retina.
scene
reflection
18
Image sensors
n Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material
(continuous)
n usually content of the signal changes over some set of spatiotemporal dimensions.
n Vocabulary: Spatiotemporal: existing in both space and
time having both spatial extension and temporal duration
数字图像处理冈萨雷斯英文Chapter空域图像增强专项文档
Image Enhancement Example
Original image
Enhanced image using Gamma correction
(Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition.
Image Negative
L-1 White
sL1r
Original digital mammogram
Outpack Input intensity White
L = the number of gray levels
Negative digital
We can written as g(x,y)Tf(x,y)
where f(x,y) is an original image, g(x,y) is an output and T[ ] is a function defined in the area around (x,y)
Note: T[ ] may have one input as a pixel value at (x,y) only or multiple inputs as pixels in neighbors of (x,y) depending in each function. Ex. Contrast enhancement uses a pixel value at (x,y) only for an input while smoothing filte use several pixels around (x,y) as inputs.
Image Enhancement in the Spatial Domain
数字图像处理ENGLISH
基于DCT的图像压缩技术研究与仿真实现题目：基于DCT的图像压缩技术研究与仿真实现院系名称：信息工程学院专业班级：集成电路工程专业学生姓名：刘家明学号： 62011年12月15号AbstractDiscrete Cosine Transform (Discrete Cosine Transform, referred to as DCT) is often considered to be the voice and image signals as the best way of transforming. In order to achieve the required engineering, many scholars at home and abroad to spend a lot of energy to find or improve fast DCT algorithms. with the development of DSP in recent years, coupling with the advantages of ASIC design, DCT firmly established an important position in the current image coding algorithm,as to be an important part of the coding of , JPEG, MPEG and other international standards on the public . MATLAB is by the American Math-Works introduced for the numerical computation and graphics processing for scientific computing software, which combines numerical analysis, matrix computation, signal processing and graphics display functions in one and constitutes a convenient user-friendly environment. The Image Processing Toolboxs in MATLAB, is the set of packages of many MATLAB technical computing environment .This paper discusses the DCT transform methods, and discusses applied functions of the image processing toolbox in MATLAB and implement related to the use of C language to implement the discrete cosine transform image compression algorithm simulation.KEYWORD:Discrete Cosine Transform(DCT);MATLAB;,DCT Transformation method;Image Processing;Image Compression;CATALOGABSTRACT ............................................ 错误!未定义书签。
数字图像处理
—图像的数字处理
digital processing of images
b
43 哈工大计算机系姚鸿勋
第一章 绪论 Chapter 1 Introduction
1-1 数字图像的概况及应用
一. 图像处理技术发展简介
—1. 该技术诞生的重要标志 —2. 该技术发展的主要因素 —3. 该学科研究的主要内容 —4. 该学科研究的主要方法
Examples: Ultrasound imaging
30 哈工大计算机系姚鸿勋
Examples: Transmission electron microscope
31 哈工大计算机系姚鸿勋
Examples:
Computer-generated images
32 哈工大计算机系姚鸿勋
IP vs. Computer Vision
图象处理与分析(图像工程上册)
—章毓晋，清华大学出版社，1999
6 哈工大计算机系姚鸿勋
主要参考书
数字图象处理学
—[美] W.K.普拉特，科学出版社，1978 ， 1984译
计算机图象处理技术基础
—张远鹏，北京大学出版社， 1996
数字图象分析
—吴健康（中科大），人民邮电出版社， 1987
计算机图象识别
提取）
39 哈工大计算机系姚鸿勋
第一章 绪论 Chapter 1 Introduction
1-1 数字图像的概况及应用
一. 图像处理技术发展简介
—1. 该技术诞生的重要标志
1964年美国的喷气推进实验室JDL处理了太空船“徘徊 者7号”发回的月球照片
—2. 该技术发展的主要因素
航天业，微电子技术，VLSI技术，70％的视觉信息
digital processing of images
b
43 哈工大计算机系姚鸿勋
第一章 绪论 Chapter 1 Introduction
1-1 数字图像的概况及应用
一. 图像处理技术发展简介
—1. 该技术诞生的重要标志 —2. 该技术发展的主要因素 —3. 该学科研究的主要内容 —4. 该学科研究的主要方法
Examples: Ultrasound imaging
30 哈工大计算机系姚鸿勋
Examples: Transmission electron microscope
31 哈工大计算机系姚鸿勋
Examples:
Computer-generated images
32 哈工大计算机系姚鸿勋
IP vs. Computer Vision
图象处理与分析(图像工程上册)
—章毓晋，清华大学出版社，1999
6 哈工大计算机系姚鸿勋
主要参考书
数字图象处理学
—[美] W.K.普拉特，科学出版社，1978 ， 1984译
计算机图象处理技术基础
—张远鹏，北京大学出版社， 1996
数字图象分析
—吴健康（中科大），人民邮电出版社， 1987
计算机图象识别
提取）
39 哈工大计算机系姚鸿勋
第一章 绪论 Chapter 1 Introduction
1-1 数字图像的概况及应用
一. 图像处理技术发展简介
—1. 该技术诞生的重要标志
1964年美国的喷气推进实验室JDL处理了太空船“徘徊 者7号”发回的月球照片
—2. 该技术发展的主要因素
航天业，微电子技术，VLSI技术，70％的视觉信息