搜索资源列表
original
- these are the original publications of Haar-like features and Adaboost framework, which form the basis of the newest object detections technology
Haar_boost
- 用Haar特征构建级联分类器的方法，在opencv环境下快速实现级联的adboost-Features with the Haar classifier cascade method of construction, environment, fast implementation in opencv cascade adboost
OpenCV_ObjectDetection_HowTo
- How-to build a cascade of boosted classifiers based on Haar-like features
HWTCoding
- Haar Wavelete Transformation Coding
Create-haarcascade
- create Haar cascade databas for detection
haar-feature
- haar特征的word版本的介绍，可以直接复制到论文中，方便快捷-haar features the introduction of the word version can be directly copied to the paper, convenient
OpenCV-FaceDetect
- 文章描述了如何基于OpenCV在嵌入式平台上利用类Haar特征和Adaboost算法实现人脸检测，并讨论了如何在嵌入式平台上优化算法。-The article describes how the class Haar features and the Adaboost algorithm embedded platform based on OpenCV face detection, and discussed how optimization algorithms on embedded p
Based-onSVM-target-tracking
- 计算Haar小波特征，用AdBaoost提取部分有代表性的特征共三种特征选择方法与SVM相结合进行目标跟踪的算法。 -The calculated Haar wavelet features to extract some of the typical characteristics of three feature selection method combined with SVM algorithm for target tracking AdBaoost.
Daubechies-y-Haar
- proyecto en qt que realiza la tranasformada Haar y daubechies
image_denoising
- denoising packages for wavelet haar based image filtering
DWT
- DWT Based Watermarking Algorithm using Haar Wavelet
Kofsky-Code-Orange
- 人脸检测，matlab源码，适合初学者使用，包括Adaboost及Haar-like特征提取-face detectiom
plate-detection
- 车牌识别的算法描述：Haar特征以及Adaboost分类器-License Plate Recognition Algorithm Descr iption: Haar features and Adaboost classifiers
Haar-wavelet
- 与标准的傅里叶变换相比，小波分析中使用到的小波函数具有不唯一性，即小波函数 具有多样性。小波分析在工程应用中，一个十分重要的问题就是最优小波基的选择问题，因为用不同的小波基分析同一个问题会产生不同的结果。目前我们主要是通过用小波分析方法处理信号的结果与理论结果的误差来判定小波基的好坏，由此决定小波基。常用小波基有Haar小波、Daubechies(dbN)小波、Mexican Hat(mexh)小波、Morlet小波、Meyer小波等5种。-Compared with the standard
corrected-journal
- haar wavelet compression and encryption
demodulation-of-MFSK-signals
- 提出了一种多迸制频移键控(M娲K)信号调制分类及解调方法，选取截获接收机输出的MFSK信 号的时频脊线作为分类特征，利用无监督聚类算法求取最佳聚类数M．利用时频脊线的Ham：小波变换 估计码元宽度，并且利用对应最佳聚类数的聚类中心确定抽判门限，通过对时频脊线抽样判决，实现了 MFSK信号的解调．理论分析和对实际信号的处理结果证明了此算法的可行性．-new algorithm is proposed for elassillcation and demodulation of MFSK
haarcascade_upperbody.xml.tar
- haar cascade upper body classifier
AdaBoostFace
- 基于adaboost的正面人脸检测 通过对haar特征的提取来进行训练与识别-face detection by adaboost
Haar-transform
- the code here is used for image compression using Haar transform.