计算机视觉算法与应用

raywenderlich 的 Data Structures and Algorithms in Swift pdf 格式。 更新到最新的 swift 4 版本。

最大流/最小割算法的简介，理解常用最大流最小割概念的文献，值得学习。 minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style “push-relabel” methods and algorithms based on Ford- Fulkerson style “augmenting paths.” We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.

Evolutionary Optimization Algorithms Wiley Evolutionary Optimization Algorithms Wiley Evolutionary Optimization Algorithms Wiley

Python版的数据结构和算法，是一本极优的python数据结构入门教材，同时提供英文版的官方网站，可进行在线的程序演示。www.interactivepython.org/runestone/static/pythonds/index.html

Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms

[算法导论].[Introduction.to.Algorithms].Thomas.H.Cormen.Ronald.L.Rivest.Charles.E.Leiserson.Clifford.Stein

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management―considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing―addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms―explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy―focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications―illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data

该教材为MIT大学教授编写的introduction to algorithms（此版本为第三版），上课用的ppt（以pdf格式打包在里面），还有每次课的习题，以及习题的答案，内容详细丰富，很经典，激励推荐

Digital Signal Processing: Principles, Algorithms and Applications