里昂是一个文档图像处理。文章的内容似乎罚款。里昂是一个文档图像处理。文章的内容似乎罚款。
2022-03-01 19:46:39
8.66MB
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Sample Project Solutions for
Digital Image Processing Using MATLAB(2nd edition)Rafael C. Gonzalez Richard E. Woods Steven L. Eddins
2022-02-26 20:12:49
4.48MB
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冈萨雷斯的经典书籍。内有两个文档：一个书籍电子版，一个课程答案，均为原版电子文档。自己从中受益颇多，低积分分享给大家。
2022-02-26 15:08:20
18.72MB
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叶识别
一个用于从叶子图像识别物种的python桌面应用程序。 使用图像处理和机器学习的概念。 它分为以下7种
槭树
雪松杜达拉
紫荆
柑桔
银杏叶
鹅掌
夹竹桃夹竹桃
要运行项目，请运行Executioner.py
有关更多详细信息，请参阅Project Details.pdf
2022-02-18 16:27:16
189.98MB
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基于FPGA的嵌入式图像处理系统设计，英文版，非扫描版，内容清晰。
作者简介
Donald G Bailey is Associate Professor in the Institute of Information Sciences and Technology at Massey University, where he leads the Image and Signal Processing Research Group. His research interests include most aspects of image analysis, but in particular the algorithm development process, and training. Bailey has developed a Vision Image Processing System package which has been used in a wide range of image analysis applications. Current and recent projects include: image processing using FPGAs, real time produce grading using machine vision, super-resolution, and sub pixel measurement techniques, camera calibration, and coastal monitoring using automated video analysis. He has been working as an electronics and computer systems engineer in the field of image analysis and machine vision for over 25 years. He began applying FPGA technology to image processing in 2002, and since then has published about 25 papers on issues and applications of FPGAs to image processing.
目录
Preface.
Acknowledgements.
1 Image Processing.
1.1 Basic Definitions.
1.2 Image Formation.
1.3 Image Processing Operations.
1.4 Example Application.
1.5 Real-Time Image Processing.
1.6 Embedded Image Processing.
1.7 Serial Processing.
1.8 Parallelism.
1.9 Hardware Image Processing Systems.
2 Field Programmable Gate Arrays.
2.1 Programmable Logic.
2.2 FPGAs and Image Processing.
2.3 Inside an FPGA.
2.4 FPGA Families and Features.
2.5 Choosing an FPGA or Development Board.
3 Languages.
3.1 Hardware Description Languages.
3.2 Software-Based Languages.
3.3 Visual Languages.
3.4 Summary.
4 Design Process.
4.1 Problem Specification.
4.2 Algorithm Development.
4.3 Architecture Selection.
4.4 System Implementation.
4.5 Designing for Tuning and Debugging.
5 Mapping Techniques.
5.1 Timing Constraints.
5.2 Memory Bandwidth Constraints.
5.3 Resource Constraints.
5.4 Computational Techniques.
5.5 Summary.
6 Point Operations.
6.1 Point Operations on a Single Image.
6.2 Point Operations on Multiple Images.
6.3 Colour Image Processing.
6.4 Summary.
7 Histogram Operations.
7.1 Greyscale Histogram.
7.2 Multidimensional Histograms.
8 Local Filters.
8.1 Caching.
8.2 Linear Filters.
8.3 Nonlinear Filters.
8.4 Rank Filters.
8.5 Colour Filters.
8.6 Morphological Filters.
8.7 Adaptive Thresholding.
8.8 Summary.
9 Geometric Transformations.
9.1 Forward Mapping.
9.2 Reverse Mapping.
9.3 Interpolation.
9.4 Mapping Optimisations.
9.5 Image Registration.
10 Linear Transforms.
10.1 Fourier Transform.
10.2 Discrete Cosine Transform.
10.3 Wavelet Transform.
10.4 Image and Video Coding.
11 Blob Detection and Labelling.
11.1 Bounding Box.
11.2 Run-Length Coding.
11.3 Chain Coding.
11.4 Connected Component Labelling.
11.5 Distance Transform.
11.6 Watershed Transform.
11.7 Hough Transform.
11.8 Summary.
12 Interfacing.
12.1 Camera Input.
12.2 Display Output.
12.3 Serial Communication.
12.4 Memory.
12.5 Summary.
13 Testing, Tuning and Debugging.
13.1 Design.
13.2 Implementation.
13.3 Tuning.
13.4 Timing Closure.
14 Example Applications.
14.1 Coloured Region Tracking.
14.2 Lens Distortion Correction.
14.3 Foveal Sensor.
14.4 Range Imaging.
14.5 Real-Time Produce Grading.
14.6 Summary.
References.
Index.
2022-02-16 21:38:40
27.41MB
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本文为一篇英文文献，主要讲数字图像处理的基本原理和处理方法
2022-02-15 15:56:50
44.37MB
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简单简历
快速链接：
[Docker]（#docker）
关于
使用SimpleCV（计算机视觉的开源框架）使计算机具有视觉效果
SimpleCV是使用OpenCV和Python编程语言的开放源代码机器视觉的框架。 它为相机，图像处理，特征提取和格式转换提供了简洁易读的界面。 我们的使命是为休闲用户提供用于基本机器视觉功能的全面界面，以及为高级用户提供优雅的编程界面。
我们之所以喜欢SimpleCV，是因为：
即使是初学者，也可以编写简单的机器视觉测试
摄像机，视频文件，图像和视频流均可互操作
可以轻松提取，分类和过滤有关图像特征的信息
操作速度快，名字容易记住
线性代数是严格可选的
这
2022-02-10 15:05:00
51.2MB
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这是一个用于自动驾驶的开源高清地图项目。
精确地图制作过程分为四个部分：地图收集，地图制作，地图标签和地图保存。
OpenHDMap这是一个用于自动驾驶的开源高清地图项目。
精确地图制作过程分为四个部分：地图收集，地图制作，地图标签和地图保存。
该项目主要使用激光雷达作为收集传感器来提供地图制作过程。
目标是为自动驾驶系统和仿真提供完整的映射过程。
如果您有任何建议，请随时与我们联系。
简介让我们从制作高清地图开始。
H
2022-01-28 18:07:46
739KB
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NTIRE2021-IQA-MACS（tensorflow 2）
评估
从此处下载预：[1] （〜135 MB）
提取models.zip文件并将模型放在./models/中。
对单个图像的评估
运行
python3 evaluation_single_image.py --ref ./test_images/ref.bmp --distorted ./test_images/dist.bmp
输出为：
-------------------------------------
Image Quality Score: 1381.0543870192307
对NTIRE图像的评估
在设置验证参考图像和失真图像的目录
运行
python3 evaluation_ntire.py
输出分数将记录在output.txt中。
从头开始培训网络
准备数据集
下载数据集： [1
2022-01-27 13:54:04
6.06MB
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