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Image processing is a book published by Science Press in 2009 by sun Jixiang. Expand full text
Image processing is a book published by Science Press in 2009 by sun Jixiang.
information
ISBN
nine trillion and seven hundred and eighty-seven billion thirty million two hundred and forty thousand eight hundred and seventy-three   [1]
do      person
Sun Jixiang [1]
set      price
RMB 35.00
book      name
image processing
Publication time
November 2009
open      book
16 open
press
Science Press
Introduction to image processing
Introduction to image processing: image information processing is a multi-stage, multi-channel and multi-objective information processing process. This book deeply and systematically expounds and demonstrates the common and basic knowledge in image information processing, as well as the front-end processing theories, methods and technologies《 Image processing involves an overview of image information processing, relevant mathematical knowledge, visual knowledge, mathematical description of image, image digitization, image transformation, image enhancement, image restoration, etc. The technical content introduced in some chapters can be used as an independent technology to produce the output required by users to meet the needs of users, or it can be the preprocessing of some subsequent information processing《 The contents and depth of discussion involved in image processing are suitable for postgraduates and senior undergraduate students of Electronic Science and engineering, control theory and engineering, computer science and technology, instrument science and technology and other relevant majors and research directions as teaching materials or teaching reference books, as well as scientific researchers of relevant majors.
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• ## Python + opencv real timeimage processing

10000 times of reading Many people like it 2020-01-04 23:09:35
Beginner opencvimage processingMy friends must have a headache about Gaussian function, filtering, threshold binarization and other features. Here is a small project for you to share, which can be dynamically viewed in real time through the cameraimage processingIt can also be helpful for you to adjust parameters and test.
catalogue
1. Import library file
2. Design gui
3. Call camera
4. Real time image processing
4.1 threshold binarization

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• This series of articles is about Python OpenCVimage processingKnowledge. In the early stage, it mainly explains the introduction to images, the basic usage of OpenCV, and in the middle stageimage processingVarious algorithms, including image sharpening operator, image enhancement technology, image segmentation, etc. in the later stage, combined with deep learning, research on image recognition and image classification application
This series of articles is to explain Python OpenCV image processing knowledge. In the early stage, it mainly explains the introduction of image and the basic usage of OpenCV. In the middle stage, it explains various algorithms of image processing, including image sharpening operator, image enhancement technology, image segmentation, etc. in the later stage, it studies image recognition and image classification application in combination with in-depth learning. I hope the article is helpful to you. If there are deficiencies, please forgive me~
As the first article, this article will explain the basic knowledge of image processing and the introduction function of OpenCV. The knowledge points are as follows:

1. Basic knowledge of image
2. Opencv reading and writing images
3. Opencv pixel processing

PS: the article also learned the knowledge of Netease cloud Gordon education. I recommend you to study.
All source code of this series in GitHub:

https://github.com/eastmountyxz/ImageProcessing-Python

PS: ask for help to order a star. Haha, I will share more codes in the future when I use GitHub for the first time. Come on.
At the same time, the author's C + + image series knowledge is recommended:

[digital image processing] i. MFC detailed display of BMP format pictures
[digital image processing] II. MFC single document segmentation window display picture


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• ## image processingMATLAB image reading

10000 times of reading Many people like it 2017-05-29 12:50:45
Speaking ofimage processing, the first step is image reading. Matlab is the simplest imread function. This section introduces the usage of imread and its error prone places
When it comes to image processing, the first step is image reading. Matlab is the simplest imread function. This section introduces the usage of imread and its error prone places As shown in the figure above, imread includes the above usages in MATLAB documents, but it is not required to master them all. I think I can use one or two, and I can understand the syntax of others.Let's introduce the most commonly used statement a = imread (filename)Let's read a picture
>>A = imread ('stare. JPG ');
>> imtool(a)

As shown in the figure, first note that the syntax is correct, a = imread ('stare. JPG '); 1、 Correct demonstration.He means to read the image data named "gaze. JPG" in the current path into a and save it. Then we can see that there are some data in the workspace area on the far right of the image. This is the data of A. We can see that the image is 340five hundred and ninety-three3 size, which means 340 rows, 593 columns, 3-channel (RGB) pictures. Uint8 on the right represents an 8-bit unsigned integer type（ The following imtool statements are used to display images, which will be discussed in detail later)Add a little knowledge:To clear the command window, enter the command CLCTo clear the workspace, enter the command clearTo close all open windows, enter the command close allTo view image information, use whos
2、 Stepping pitWell, now that we know the correct way to write, let's try what pit there is（ Daring to try and make mistakes is an excellent quality in this business)1. Why semicolon?Because matlab compiles line by line, line by line. If you don't write well, you will directly get the compilation results. Just show you an example. An A and B matrix are created above. The a matrix does not end with a semicolon. The window directly displays the content, while the B matrix uses a semicolon and does not display the content. However, we can see that after compilation, two array matrices have been created in workspace. We can also see that there are specific data at the top by clicking the variable name. Similarly, if we read the picture a = imread ('staring. JPG ') without writing a semicolon, a large wave of data will appear in the window and jump out. It's sour and cool. Those data are the pixels saved in the array.Tips:If you don't write the variable name, such as > > imread ('staring. JPG '); It gives you a name by default: ANSWhen you want to rewrite a sentence that is the same or similar to the above, you can press the up arrow button on the keyboard This shortcut can help you quickly modify the sentence, easy to use.
2. English half angle symbolEveryone who has learned programming should know it. Don't use the whole Chinese. ", Don't use the English full angle symbol "." as for why, I don't know. Matlab will appear: file "stare. JPG" does not exist
3. PathThis is a common mistake. You remember that you have the picture and the name of the picture, but you don't put it in the current path. How do you ask others to find it? MATLAB is not so powerful that you can search all the picture files in your computer. Similarly, the compiler will appear: does not exist. But there are remedies. You can indicate where you can find them, for example: I put the picture "gazing. JPG" under the build file on disk D, and the results kept making mistakes, as shown in the figure. At first glance, I found that gazing was written as gazing. Then, I wrote build as bulid. Ha ha, I'm just a beginner. I make mistakes when I'm careless. This also shows that I really have to concentrate on writing code. This bug is easy to change, But if you do a big project, write hundreds of thousands of lines of code, and spend hours because of spelling mistakes, it's a big loss.
Well, the reading and writing of this document is written here. If you have any questions, you can comment on your discussion and study together. Maybe we will have a spark of thought. It may be trivial and simple. As long as it can give you a little harvest, this blog will be valuable. The next section continues with other functions. Thanks for watching
Welcome to my official account [CV], learning and communicating.
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• ## Do it in Pythonimage processing

10000 times of reading Many people like it 2007-10-28 23:45:00
Do it in Pythonimage processingRecently, I'm doing something more evil - verification code recognition to learn some new skills. Because I'm a beginner, rightimage processingI don't know much about it. If you want to benefit me, you must benefit my tools first. Since it's just an experiment, use Python for prototype development, and then

Image processing with Python

Recently, I'm doing something more evil - verification code recognition to learn some new skills. Because I'm a beginner, I don't know much about image processing. If I want to benefit my things, I must first benefit my tools. Since I'm just doing an experiment, it's better to use Python for prototype development. In Python, the commonly used image processing library is PIL (Python Image Library). The current version is 1.1.6, which is very convenient to use. You can

Here, I mainly introduce some functions provided by PIL that may be used in image recognition, such as image enhancement and filtering. Finally, the advantages and disadvantages of using Python for image processing and recognition are given.

Basic image processing

Import image module is required before using PIL:

import Image

Then you can use image. Open ('xx. BMP ') to open a bitmap file for processing. When you open a file, you don't have to worry about the format or understand the format. No matter what format, just throw the file name to image.open. It's really called BMP, JPG, PNG, gif... None of them can be missing.

img = Image.open(‘origin.png’)     #  Get the instance object img of an image

Figure 1 original

In image processing, the most basic is the conversion of color space. Generally speaking, our images are in RGB color space, but in image recognition, we may need to convert the image to different color spaces such as gray image and binary image. PIL also provides very complete support in this regard. We can:

new_ img = img.convert(‘L’)

Convert img to 256 gray level image. Convert () is a method of image instance object. It accepts a mode parameter to specify a color mode. The values of mode can be as follows:

· 1 (1-bit pixels, black and white, stored with one pixel per byte)

· L (8-bit pixels, black and white)

· P (8-bit pixels, mapped to any other mode using a colour palette)

· RGB (3x8-bit pixels, true colour)

· RGBA (4x8-bit pixels, true colour with transparency mask)

· CMYK (4x8-bit pixels, colour separation)

· YCbCr (3x8-bit pixels, colour video format)

· I (32-bit signed integer pixels)

· F (32-bit floating point pixels)

How's it going? Isn't it rich enough? In fact, PIL also supports the following rare color modes: La (L with alpha), rgbx (true colour with padding) and RGBA (true colour with premultiplied alpha).

Let's take a look at the image converted when the mode is' 1 ',' l 'and' p ':

Figure 2 mode = '1'

Figure 3 mode = 'l'

Figure 4 mode = 'p'

The convert () function also accepts another implicit parameter matrix, which is a tuple with a length of 4 or 16. The following example is an example of converting RGB space to CIE XYZ space:

rgb2xyz = (

0.412453, 0.357580, 0.180423, 0,

0.212671, 0.715160, 0.072169, 0,

0.019334, 0.119193, 0.950227, 0 )

out = im.convert("RGB", rgb2xyz)

In addition to the complete color space conversion capability, PIL also provides functions such as resize() and rotate() to obtain geometric transformation capabilities such as changing size and rotating pictures. In terms of image recognition, image examples provide a histogram() method to calculate histograms, which is very convenient and practical.

image enhancement

Image enhancement is usually used for preprocessing before image recognition. Appropriate image enhancement can make the recognition process get twice the result with half the effort. In this regard, PIL provides a module called imageenhance, which provides several common image enhancement schemes:

import ImageEnhance

enhancer = ImageEnhance.Sharpness(image)

for i in range(8):

factor = i / 4.0

enhancer.enhance(factor).show("Sharpness %f" % factor)

The above code is a typical example of using the imageenhance module. Sharpness is a class of the imageenhance module to sharpen images. The first mock exam module includes the following categories: Color, Brightness, Contrast and Sharpness. They all have a common interface. Enhance (factor), which accepts a floating-point parameter factor to indicate the enhanced proportion. Let's take a look at the effects of these four classes under different factors

In Figure 5, color is used for color enhancement, the factor value is [0,4], and the step is 0.5

Fig. 6 enhance the brightness with birghtness, factor value [0,4], step 0.5

Fig. 7 contrast is enhanced with contrast, factor value [0,4], step 0.5

Figure 8 sharpen the image with sharpness, factor value [0,4], step 0.5

Image filter

PIL's support for filter is very complete. In addition to common blur, relief, contour, edge enhancement and smoothing, as well as median filter and modefilter, it's so convenient that you can make your own Photoshop. These filters are placed in the imagefilter module. Imagefilter mainly includes two parts: one is the built-in filter, such as blur and detail, and the other is the filter function, which can specify different parameters to obtain different effects. Examples are as follows:

import ImageFilter

im1 = im.filter(ImageFilter.BLUR)

im2 = im.filter(ImageFilter.MinFilter(3))

im3 = im.filter(ImageFilter.MinFilter()) # same as MinFilter(3)

You can see that the use of the imagefilter module is very simple. Each filter only needs one line of code to call, and the development efficiency is very high.

Figure 9 using blur

Figure 10 using contour

Figure 11 using detail

Figure 12 use   EMBOSS

Figure 13 using edge_ ENHANCE

Figure 14 using edge_ ENHANCE_ MORE

Figure 15 using find_ EDGES

Figure 16 using SharePoint

Figure 17 using smooth

Figure 18 using smooth_ MORE

The above are the effects of several built-in filters. In addition, imagefilter also provides some filter functions. Let's take a look at the effects of these filters that can change their behavior through parameters:

Figure 19 using kernel (), parameters: size = (3, 3), kernel = (0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)

Figure 20 using maxfilter, default parameters

Figure 21 using minfilter, default parameters

Figure 22 using medianfilter, default parameters

Figure 23 using modefilter, parameter size = 3

Figure 24 uses rankfilter with parameters size = 3 and rank = 3

Summary

So far, the introduction to PIL has come to an end. In general, PIL has built-in strong support for image processing and recognition. From various enhancement algorithms to filter, people can't doubt the feasibility of using python. The only disadvantage of Python is that the execution time is too slow, especially when implementing some algorithms with large amount of computation, it requires great patience. I once used Hough transform to find straight lines in an image. It takes a few seconds for a pure Python implementation to process a 340 * 100 image (P4 3.0g + 1G memory). However, using PIL does not need to pay attention to image format, built-in image enhancement algorithm and filter algorithm. These advantages make Python suitable for constructing prototypes and experiments. In these two aspects, Python is more convenient than MATLAB. For the development of commercial image recognition products, the open source c + + library Gil from adobe, which has been boost accepted, can be considered to give consideration to both execution performance and development efficiency.

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• ## image processingMatlab feature extraction and expression

10000 times of reading Many people like it 2017-08-07 17:36:14
introduceimage processingFor feature extraction and expression, bwboundaries function is used to obtain the boundary, and regionprops function is used to count the features
• ## 【image processingHistogram equalization

10000 times of reading Many people like it 2019-03-26 20:24:54
numberimage processing(Third Edition) Fly leftimage processingIntroduction to the concept of histogram, the theoretical basis of histogram equalization, manual histogram equalization, the disadvantage of histogram equalization on MATLAB, histogram equalization
• This article isimage processingIn the last article, we will enter a new chapter later.image processingThe article mainly explainsimage processingThe methods include image geometric operation, image quantitative sampling, image point operation, image morphological processing, image enhancement, image smoothing, image sharpening, image special effects, image segmentation
• This paper mainly explains the relevant contents of image Fourier transform, in digital image processingimage processingIn, two classical transforms are widely used - Fourier transform and Hough transform. Among them, Fourier transform is mainly used to transform the signal in time domain into the signal in frequency domain for image denoising, image enhancement and other processing
• ## MATLABimage processing

Thousands of people learn 2017-06-27 19:37:11
MATLABimage processingcurriculum
• ## Pythonimage processingDetailed introduction of PIL modules

10000 times of reading Many people like it 2018-01-21 22:01:16
The image module is in Python PILimage processingThe functions of basic operation on images are basically included in this module. Such as open, save, convert, show... And other functions. Open class image. Open (file) ⇒ image. Open (file, mode) ⇒ image
• ## numberimage processingMatlab

10000 times of reading Many people like it 2018-06-18 20:25:13
(Note: most of the codes in this article can be found in the digitalimage processing(found in the Third Edition) using software: MATLAB r2018a learning premise: understand each button of the GUI interface of MATLAB reference: "digital"image processingThe third edition, CSDN blog uses the first tone picture, P station painter uid: 1589657
• ## Gonzalez figuresimage processingThird Edition

Gonzalez figuresimage processing, Third Edition
• ## image processingGetting Started tutorial

10000 times of reading Many people like it 2015-12-29 11:21:24
Someone asked me recentlyimage processingHow to study, how to get started, how to apply, I was speechless for a moment. Think about it carefully. I have also done two years of image research, done two innovative projects and issued two papers. It is also a little experience, so I summarize and share with you, hoping to be helpful to you. In
• ## How to learn wellimage processing——From Xiaobai to great God?

10000 times of reading Many people like it 2016-02-26 17:48:13
What are numbersimage processing？ History, and what it studies. Speaking ofimage processing, what do you think? Do you really understand what you are studying in this field. Vertically, numbersimage processingThe history of research is quite long; Horizontally, numbersimage processingThe topic of research is quite wide
• ## numberimage processingReview summary

10000 times of reading Many people like it 2019-01-03 22:19:26
I can't remember when I review, so with this blog, if I also choose numbersimage processingCourse partners can refer to a ha! Pure hand code... Every exam must pass! Concept sampling and quantization of scenes with slow gray transformation: coarse sampling and fine quantization of images with a large number of detail changes:
• 2. Simulationimage processing3. Figuresimage processing4. What is image 5. Relationship between digital image and signal 6. How to form digital image 7. Application of machine / computer vision, computer graphics, artificial intelligence signal processing 1. Introduction to digital imageimage processing(Digital Image ...
• This series of articles is about Python OpenCVimage processingKnowledge. In the early stage, it mainly explains the introduction to images, the basic usage of OpenCV, and in the middle stageimage processingVarious algorithms, including image sharpening operator, image enhancement technology, image segmentation, etc. in the later stage, combined with deep learning, research on image recognition and image classification application
• This series of articles is about Python OpenCVimage processingKnowledge. In the early stage, it mainly explains the introduction to images, the basic usage of OpenCV, and in the middle stageimage processingVarious algorithms, including image sharpening operator, image enhancement technology, image segmentation, etc. in the later stage, combined with deep learning, research on image recognition and image classification application
• Based on MATLABimage processingImplementation and comparison of median filter, mean filter and Gaussian filter 1. Background knowledge median filter method is a nonlinear smoothing technology. It sets the gray value of each pixel as the median of the gray values of all pixels in a neighborhood window of the point. Median filter is based on ranking
• ## Matlab06: Digitalimage processing

10000 times of reading Many people like it 2019-11-18 13:15:09
Table of contents matlab06: numbersimage processingImage reading and display image storage format in MATLAB reading and display image point operation of image statistical distribution of four operation pixels of image matlab06: Digitalimage processingImage reading and display image storage format in matlab matlab can
• ## numberimage processing

image processingnumberimage processingThe processing simulation of mathematical operation and processing of image information by using computer technology or other digital technologyimage processingAlso known as opticsimage processing, the simulated image is processed by optical lens or optical photography method, and photoelectric combination processing (simulationimage processingWith
• ## numberimage processingChapter 9 - morphologyimage processing

A thousand readings Many people like it 2019-05-11 08:31:12
numberimage processingChapter IX figuresimage processing---Morphologyimage processing（1） Preliminary knowledge 1.1 preliminary knowledge 1.1.1 basic concepts in set theory 1.2 binary images, sets and logical operators (II) expansion and corrosion 2.1 expansion 2.2 decomposition of structural elements 2.3 Strel function 2.4 corrosion (III) expansion
• numberimage processingThe development of technology has high requirements for the basis of mathematics. In some emerging new methods, the dazzling mathematical derivation discourages many people who expect in-depth research. A regular science and engineering student has roughly had the mathematical foundation including calculus, linear algebra and probability theory. But in
• ## numberimage processingChapter VI - colorimage processing(bottom)

A thousand readings Many people like it 2019-05-04 12:28:03
numberimage processingChapter VI figuresimage processing---Colourimage processing（5） Spatial filtering of color image 5.1 smoothing of color image 5.2 sharpening of color image (VI) processing directly in RGB vector space 6.1 color edge detection using gradient 6.2 segmentation in RGB vector space
• ## numberimage processingKnowledge points

A thousand readings Many people like it 2020-02-03 16:11:34
numberimage processingDirectory number of knowledge pointsimage processingKnowledge points Chapter 1 overview 1.1 numbersimage processingRelated concepts 1.2 figuresimage processingSystem flow chart: 1.3 digitalimage processingThe main research content is the second chapterimage processingBasic 2.1 image digitization and expression 2.2 image
• ## image processingTechnology (I)image processingBasic knowledge

10000 times of reading Many people like it 2018-08-30 15:52:26
image processingThe concept of image processing is to process image information to meet the needs of human visual psychology and practical application. Analog image: continuous image, which adopts digital (discrete) representation and digital technology to process the previous image. Digital image: an image obtained by continuous analog image sampling and quantization
• ## numberimage processingChapter VI - colorimage processing(upper)

10000 times of reading Many people like it 2019-04-23 22:24:29
numberimage processingChapter VI figuresimage processing---Colourimage processing（1） Representation of color image in MATLAB (II) affine transformation (III) projection transformation (IV) geometric transformation applied to image (V) image coordinate system in MATLAB 5.1 output image position 5.2 control output grid (VI)
• Image Processing Toolbox image processingThe toolbox contains functions: image reading and saving, image display, GUI image creation, geometric transformation, image filter design and linear filter morphologyimage processingImage domain transformation image enhancement image analysis image synthesis image registration image segmentation

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