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dshowtest.zip
- 一個播放avi檔的filter graph.Source Filters主要負責取得資料，資料來源可以是文件、網路、或者電腦裡的介面卡、Webcam等，然後將資料往下傳輸；Transform Fitlers主要負責資料的格式轉換、傳輸；Rendering Filtes主要負責資料的最終去向，我們可以將資料送給音效卡、顯示卡進行多媒體的展示，也可以輸出到文件進行存儲。三個部分並不是都只 有一個Filter去完成功能。恰恰相反，每個部分往往是有幾個Fitler協同工作的。 ,Avi files a
BM3D
- BM3D去噪算法的实现和相关文档，很好的去噪算法-Image and video denoising by sparse 3D transform-domain collaborative filtering Block-matching and 3D filtering (BM3D) algorithm
AFDF
- 文章全面描述了协作通信中DF和AF协议的符号信噪比性能，以及两者的比较-Article a comprehensive descr iption of the collaborative communication agreement DF and AF symbol SNR performance, as well as the comparison between the two
2009Based_on_Co-Training
- Co-Training的协同目标跟踪内容，感兴趣的看看吧-Co-Training for collaborative target tracking content, of interest to see it
Sparsity-Collaborative-Track
- 基于稀疏表示的目标跟踪,对于稀疏表示应用于图像处理的同志可是一个借鉴。-Robust Object Tracking via Sparsity-based Collaborative Model
CF_Java
- 协同过滤算法的java实现版本，包括从数据文件中读取文件-Collaborative filtering algorithm to achieve the java version, including file read from the data file
Block-TVNLR
- Image Compressive Sensing Recovery via Collaborative Sparsity
colaborative-demo
- 基于协同稀疏表示的解混方法，因为每个端元的相似性，本文采用协同稀疏表示来约束每个像素采用相同位置系数不同的原子-Collaborative Sparse Regression for Hyperspectral Unmixing
3
- 采用多假设跟踪器实现对人体身体部位的跟踪的文献，具有较强的鲁棒性-Robust tracking of human body parts for collaborative human computer interaction
VapourSynth-BM3D-master
- VapourSynth-BM3D-master BM3D算法的原创代码，运行速度超快，参考文献《Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering》-VapourSynth-BM3D-master BM3D original algorithm code, run super-fast, Reference " Image Denoising by Sparse 3-D Transform-Doma
machine-learning-ex8
- Andrew Ng Cousera 机器学习 异常检测勇于服务器故障分析以及用于电影推荐的推荐系统的源代码和说明文档。(Andrew Ng Cousera's machine learning implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering t
code
- 水平集进行协同分割的代码，用两幅图像的前景背景分别比较，能够获得很好的效果，相对单图(The level set for collaborative segmentation of the code)
BM3D
- This is BM3D algorithm implemented according to the paper: K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3D transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August