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1100万基础地理数据库系列_2017县级行政区shp文件

《OpenCV算法精解：基于Python与C++》是以OpenCV 为工具学习数字图像处理的入门书。内容由浅入深，每一章都采用阐述基本概念、数学原理、C++ 实现、Python 实现相结合的方法，使初学者循序渐进地掌握数字图像处理技术。本书既注重基本的概念理论及数学原理，也注重其代码实现及实际应用，力求帮助读者全面系统地掌握图像算法的基本技术，同时为掌握OpenCV 打下良好的基础。 《OpenCV算法精解：基于Python与C++》适合入门图像处理和计算机视觉领域的初学者阅读，要求读者具备一定的C++ 或Python 编程基础。

Tensorflow Machine Learning Cookbook英文版书籍，加代码，随便用。

This fourth edition has been developed to reflect the changes that have occurred in techniques for the analysis of digital image data in remote sensing over the past five years or so. Its focus is on those procedures that seem now to have become part of the set of tools regularly used to perform thematic mapping. As with previous revisions, the fundamental material has been preserved in its original form because of its tutorial value; its style has been revised in places and it has been supplemented if newer aspects have emerged in the time since the third edition appeared. The theme of the book remains, however, on the needs of the senior student and practitioner. The earlier editions have contained extensive material in Chapter 1 on satellite programs and sensor characteristics. Although that material is important in the context of understanding the application of image analysis procedures, the rapid development of quasi- and fully operational programs in the past decade has meant that expanding the material of Chap. 1, as required, would have distracted from the role of that chapter to introduce the reader to the nature and properties of digital image data in remote sensing. Accordingly, all of the material on satellite programs and sensor specifications has been moved to a newAppendix A. Chapter 1 has then been completely rewritten as a stand-alone introduction to sensors in general and the data properties of importance in image analysis. Many changes have been made throughout the book to meet the increasing emphasis on hyperspectral data and its analysis. Although much of that is contained in Chap. 13, techniques required for hyperspectral data processing are developed in the context of previous chapters, particularly new material on feature extraction tools that work well on hyperspectral data sets; they are covered in Chap. 10. Chapter 10 has been re-named. It was felt that there is too much confusion in the term Data Fusion to retain it as the title for material that is fundamentally concerned with thematic mapping from multiple data sources and multiple sensors. Chapter 8, dealing with supervised classification methods, has been substantially supplemented. Sections have been incorporated on k nearest neighbour classification, Markov random fields and support vector classifiers. Other modifications relate to the noise adjusted principal components transformation, the definition of texture, a re-working of the contrast modification material and the inclusion of several further illustrations and problems. The authors continue to enjoy the very strong support and understanding of their families, so important in the undertaking of this work, for which they express and record their sincere gratitude.

在数字图像处理领域，《数字图像处理(第三版)》作为主要教材已有30多年。这一版本是作者在前两版的基础上修订而成的，是前两版的发展与延续。除保留了前两版的大部分内容外，根据读者的反馈，作者在13个方面对《数字图像处理(第三版)》进行了修订，新增了400多幅图像、200多幅图表及80多道习题，融入了近年来数字图像处理领域的重要进展，因而《数字图像处理(第三版)》特色鲜明且与时俱进。《数字图像处理(第三版)》仍分为12章，即绪论、数字图像基础、灰度变换与空间滤波、频率域滤波、图像复原与重建、彩色图像处理、小波和多分辨率处理、图像压缩、形态学图像处理、图像分割、表示与描述、目标识别。

在数字图像处理领域，《数字图像处理(第三版)》作为主要教材已有30多年。这一版本是作者在前两版的基础上修订而成的，是前两版的发展与延续。除保留了前两版的大部分内容外，根据读者的反馈，作者在13个方面对《数字图像处理(第三版)》进行了修订，新增了400多幅图像、200多幅图表及80多道习题，融入了近年来数字图像处理领域的重要进展，因而《数字图像处理(第三版)》特色鲜明且与时俱进。《数字图像处理(第三版)》仍分为12章，即绪论、数字图像基础、灰度变换与空间滤波、频率域滤波、图像复原与重建、彩色图像处理、小波和多分辨率处理、图像压缩、形态学图像处理、图像分割、表示与描述、目标识别。

很好，特别好的单片机入门，希望能够帮助大家！