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FACE-RECOGNITION
- 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列 为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabce
worddetectinvideo
- 基于稀疏表达的视频文字检测方法 ：针对传统视频文字检测方法存在效率较低、计算复杂、精确度不高的不足，提出一种视频文字检测方法，通过基于边缘密度和连 通域分析的文字粗检测得到候选文本行，利用稀疏表达分类产生的过完备词典进行文字行验证。实验结果表明，该方法具有较好的检测性 能，可以应用于视频检索系统。-Expression based on sparse video text detection method: video text detection method for the exis
face_detection
- 本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-The purpose of this paper is threefold: firstly, the local Successive Mean Quantization Transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a s
SR
- 自己写的代码，是关于稀疏表示用来红外小目标检测的论文实现，但是只是一个初步实现，很多功能还没用实现-Write your own code, is on the sparse representation for infrared small target detection paper to achieve, but only a preliminary implementation, not with a lot of features to achieve
ICPR
- 1_On the Dimensionality Reduction for Sparse Representation based Face Recognition 2_Fingerprint Pore Matching based on Sparse Representation 3_Microaneurysm (MA) Detection via Sparse Representation Classifier with MA and Non-MA Dictionary
dense-stereo
- 基于VC++的双目立体视觉检测 采用SIFT特征点计算稀疏匹配 和稠密匹配-VC++ based binocular stereo vision detection using SIFT feature points to calculate sparse matching and dense matching
HSC
- 进行稀疏编码的梯度检测，判定输入的图片的类别-Gradient detection sparse coding, decision input category
HSCcvpr2013
- This paper is a report CMU CVPR2013 article, proposed a sparse encoding based on contour features, referred to as HSC (Histogram of Sparse Code), and in target detection beyond the HOG (Histogram of Gradient) this paper introduces ideas and calculati