w褶积矩阵、二值化旧图经核矩阵得到新图。

https://docs.gimp.org/en/plug-in-convmatrix.html

8.2. Convolution Matrix

8.2.1. Overview

Here is a mathematician's domain. Most of filters are using convolution matrix. With the Convolution Matrix filter, if the fancy takes you, you can build a custom filter.

What is a convolution matrix? It's possible to get a rough idea of it without using mathematical tools that only a few ones know. Convolution is the treatment of a matrix by another one which is called “kernel”.

The Convolution Matrix filter uses a first matrix which is the Image to be treated. The image is a bi-dimensional collection of pixels in rectangular coordinates. The used kernel depends on the effect you want.

GIMP uses 5x5 or 3x3 matrices. We will consider only 3x3 matrices, they are the most used and they are enough for all effects you want. If all border values of a kernel are set to zero, then system will consider it as a 3x3 matrix.

The filter studies successively every pixel of the image. For each of them, which we will call the “initial pixel”, it multiplies the value of this pixel and values of the 8 surrounding pixels by the kernel corresponding value. Then it adds the results, and the initial pixel is set to this final result value.

A simple example:

On the left is the image matrix: each pixel is marked with its value. The initial pixel has a red border. The kernel action area has a green border. In the middle is the kernel and, on the right is the convolution result.

Here is what happened: the filter read successively, from left to right and from top to bottom, all the pixels of the kernel action area. It multiplied the value of each of them by the kernel corresponding value and added results. The initial pixel has become 42: (40*0)+(42*1)+(46*0) + (46*0)+(50*0)+(55*0) + (52*0)+(56*0)+(58*0) = 42. (the filter doesn't work on the image but on a copy). As a graphical result, the initial pixel moved a pixel downwards.

Convolution Matrix的更多相关文章

  1. 卷积、卷积矩阵(Convolution matrix)与核(Kernel)

    在图像处理领域,Kernel = convolution matrix = mask,它们一般都为一个较小的矩阵: 用于:Sharpen,Blur, Edge enhance,Edge detect, ...

  2. 2D image convolution

    在学习cnn的过程中,对convolution的概念真的很是模糊,本来在学习图像处理的过程中,已对convolution有所了解,它与correlation是有不同的,因为convolution = ...

  3. 数字图像处理- 3.4 空间滤波 and 3.5 平滑空间滤波器

    3.4 空间滤波基础 • Images are often corrupted by random variations in intensity, illumination, or have poo ...

  4. [Matlab] Galois Field arrays

    Operations supported for Galois Field arrays: + - - Addition and subtraction of Galois arrays. * / \ ...

  5. Deep Learning 10_深度学习UFLDL教程:Convolution and Pooling_exercise(斯坦福大学深度学习教程)

    前言 理论知识:UFLDL教程和http://www.cnblogs.com/tornadomeet/archive/2013/04/09/3009830.html 实验环境:win7, matlab ...

  6. 【ufldl tutorial】Convolution and Pooling

    卷积的实现: 对于每幅图像,每个filter,首先从W中取出对应的filter: filter = squeeze(W(:,:,filterNum)); 接下来startercode里面将filter ...

  7. Understanding Convolution in Deep Learning

    Understanding Convolution in Deep Learning Convolution is probably the most important concept in dee ...

  8. Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1

    3.Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1 http://blog.csdn.net/sunbow0 ...

  9. Deep Learning 学习随记(七)Convolution and Pooling --卷积和池化

    图像大小与参数个数: 前面几章都是针对小图像块处理的,这一章则是针对大图像进行处理的.两者在这的区别还是很明显的,小图像(如8*8,MINIST的28*28)可以采用全连接的方式(即输入层和隐含层直接 ...

随机推荐

  1. Vmware linux 无法上网

    流程如下: 1)点击 VM->Settings Hardware选项卡下面 2)点击Network Adapter 设置如下图所示,首先我们在虚拟机中将网络配置设置成NAT, 3.进入Windo ...

  2. CodeForces 558D

     Guess Your Way Out! II Time Limit:2000MS     Memory Limit:262144KB     64bit IO Format:%I64d & ...

  3. ldap temp

    #http://www.openldap.org/software/man.cgi?query=slapcat&apropos=0&sektion=0&manpath=Open ...

  4. JSON 文件格式解析

    JSON 文件大致说明 JSON 文件你可以理解为就是一个字典文件. 格式为 { 索引:数据, 索引:{ 索引:数据, 索引:{ 索引:数据, 索引:数据 } } } 自己写一个 my.json { ...

  5. [开机启动]Linux开机自启和运行级别

    嵌入式系统中程序自启动方法 在很多嵌入式系统中,由于可用资源较少,常常在系统启动后就直接让应用程序自动启动,以减少用户操作和节省资源.如何让自己的应用程序自动启动呢?    在Linux系统中,配置应 ...

  6. Sql Server 语句集合

    -- 判断数据库表是否存在 select count(*) from sysobjects where id=OBJECT_ID('tableName'); -- 返回 1存在,0不存在 -- 判断表 ...

  7. Apache ab使用POST参数进行压力测试 (服务端为Django)

    2016年07月07日 15:04:51 常城 阅读数:13774更多 个人分类: PythonLinux架构   版权声明:本文为博主原创文章,未经博主允许不得转载. https://blog.cs ...

  8. 003Maven_Maven核心概念

    Maven核心概念 Maven插件 Maven的核心仅仅定义了抽象的生命周期,具体的任务都是交由插件完成的每个插件都能实现多个功能,每个功能就是一个插件目标 Maven的生命周期与插件目标相互绑定,以 ...

  9. digitalocean --- How To Install Apache Tomcat 8 on Ubuntu 16.04

    https://www.digitalocean.com/community/tutorials/how-to-install-apache-tomcat-8-on-ubuntu-16-04 Intr ...

  10. 《linux系统及其编程》实验课记录(四)

    实验4:组织目录和文件 实验目标: 熟悉几个基本的操作系统文件和目录的命令的功能.语法和用法, 整理出一个更有条理的主目录,每个文件都位于恰当的子目录. 实验背景: 你的主目录中已经积压了一些文件,你 ...