搜索资源列表
DSPSS10-Seminar-3
- Locally Adaptive Sparse Representation for Detection, Classification, and Recognition. Lectuures given by Prof Trac Tran from john Hopkins university
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
duomubiaodeshequjiegoujiece
- 本文用于揭示复杂网络中社区结构的多目标遗传算法。该算法可检测具有组间稀疏连接且组内紧密联结的节点并定义了两个目标函数对检测方法进行了优化。通过仿真及现实中的实验验证，该算法可以成功的检测社区结构。-This paper presents a method for revealing the community structure of complex networks in multi-objective genetic algorithm. The algorithm can detect w
uwbdete6
- 该方法将宽带信号的检测转化为稀疏信号的表示问题。利用分数阶傅里叶变化实现宽带LFM信号的稀疏表示，然后利用OMP算法实现LFM信号的检测。-The method to the detection of broadband signals into sparse signal representation problem. Wideband LFM signal changes in the fractional Fourier sparse representation, and then ta
Pudn
- A filter bank structure that can deal effectively with piecewise smooth images with smooth contours, was proposed by Minh N Do and Martin Vetterli. The resulting image expansion is a directional multiresolution analysis framework composed of contour
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
Fusion-based-Sensor-Placement
- 论文 在使用无线传感器网络进行目标检测时,如何布置尽可能少的传感器节点而同时实现高的正确检测概率和 低的误警率,是关键问题之一。采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度。提 出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布 置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域。仿真结果表明,算法在保证检测精度的同时能有 效减少所使用的传感器节点数目。 - Sensor placeme
PCA-Faces-and-examples
- 稀疏主成分分析用于脸部检测和识别的基础知识介绍。初学者很专业的入门材料。-Sparse principal component analysis for face detection and identification of the basics of introduction. Introductory material for beginners very professional.
HaarPSRC=Vehicle-detection
- 运用harr特征+SRC（稀疏表示）分类实现的一种车辆检测方法，文件中提供了训练和测试车辆图片。由于时间原因，所用haar特征没有优化，维度过高，导致滑窗框图过慢，本代码只输出效果统计数据，以供大家参考学习稀疏表示在车辆检测中的应用。-Using harr feature+SRC (sparse representation) classification to achieve a vehicle detection method, the paper provides a training a
N-sparse
- 创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。-Cr
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
Sparse-Coding-for-Face-detection
- 基于sparse coding的人脸检测，很好的学习代码-face detection based on sparse coding!
Histograms-of-Sparse
- Histograms of Sparse Codes for Object Detection,cvpr文章-Histograms of Sparse Codes for Object Detection
sparse-representation-pdf
- This project describes the problem of facial expression recognition in the field of computer vision. Firstly, the psychological background of the problem is presented. Then, the idea of facial expression recognition system (FERS) is outlined and the
group-anomaly-detection
- 基于光流直方图和稀疏表示的群体异常事件检测-Detecting abnormal events based group optical flow histogram and sparse representation
A-Fast-Convergence-Multiuser-Detection-Scheme-for
- good paper about sparse code multiple access (SCMA)
PolSAR-ship-detection
- 基于稀疏表示的极化SAR舰船目标检测，有益于学者学习-Polarization SAR ship target detection based on sparse representation, is conducive to academic study