Tsai相机标定两步法-原论文

原Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs 深度学习应用于眼底图眼科糖尿病视网膜病变预测

2015年新发布的蚁狮优化算法ALO，以及原作者的发布论文，可用于神经网络权值阈值的优化

ALPHA GO 原论文转载

Abstract—Clustering face images according to their latent identity has two important applications: (i) grouping a collection of face images when no external labels are associated with images, and (ii) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the partition algorithm. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarities between face images. This allows a dynamic selection of number of clusters and retains pairwise similarities between faces. ConPaC formulates the clustering problem as a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as k-means, spectral clustering and approximate Rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to work in a semi-supervised way that leads to improved clustering performance. We also propose an k-NN variant of ConPaC, which has a linear time complexity given a k-NN graph, suitable for large datasets. Index Terms—face clustering, face representation, Conditional Random Fields, pairwise constraints, semi-supervised clustering.

关于信道分解方面的经典论文，里面有完整的算法描述以及仿真结果，非常适于学习

两阶段鲁棒优化CCG列于约束生成和Benders代码，可扩展改编，复现自原论文。文件中附源代码以及论文。使用matlab-yalmip编

原始论文的简单 DBSCAN 实现：“A Density-Based Algorithm for Discovery Spatial Databases in Large Spatial Databases with Noise”——Martin Ester 等人。 DBSCAN 能够对带有噪声的任意形状进行聚类。 由于没有实现空间访问方法，运行时间复杂度将是 N^2 而不是 N*logN。 ****************************************************** **************************** 包含带有螺旋合成数据集的附加演示 (demo.m)。 并且还提供了聚类的逐步动画（demo_stepwise）。 ****************************************************** ******

读者学习过程中，发现网上的翻译版的格式混乱，然后使用ieee的latex的论文格式按照原论文格式进行了翻译，希望读者可以比对原论文阅读，及节约时间又可以快读理解raft论文。如果觉得翻译的存在问题，可以到https://github.com/brandonwang001/raft_translation提建议或改进。如果有侵权，请联系作者进行删除。

heft算法的原论文，2002年发布，