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gdiexample
- 使用GDI+实现画线，基数样条，可伸缩区域，图像与文本等功能，以及使用GDI实现画线功能。-Use GDI+ Realize painting line, spline base, scalable region, features such as image and text, as well as realize the use of GDI drawing a line function.
LBF_v0_v0.1
- 图像分割，Minimization of Region-Scalable Fitting Energy for Image Segmentation-LBF_v0.1: This code implements an improved algorithm slightly modified from the original LBF model in the above paper. A desirable advant
Image_Segmentation_Active_Coutour_Local_Binary_Fit
- 李纯明最新实现的局部活动轮廓模型的图像分割，比CV模型方法好很多。包含所实现的论文Minimization of Region-Scalable Fitting Energy for Image Segmentation-Li Chunming latest realization of the local active contour model for image segmentation, much better than the
RSF_v0
- This code demomstrates an improved algorithm based on the local binary fitting (LBF) model in Chunming Li et al s paper: "Minimization of Region-Scalable Fitting Energy for Image Segmentation", IEEE Trans. Image
RSF_v0
- 李春明Minimization of Region-Scalable Fitting Energy for Image Segmentation代码-li Minimization of Region-Scalable Fitting Energy for Image Segmentation code
RSF-Active-contour-model.
- 实现论文 Minimization of region-scalable fitting energy for image segmentation 中提出的RSF模型，也适用于LBF模型，用于图像分割。-RSF model of the papers Minimization of region-scalable fitting energy for image The a segmentation , LBF model for
cvpr12_mfc
- cvpr2012_oral On Multiple Foreground Cosegmentation-In this paper, we address a challenging image segmentation problem called multiple foreground cosegmentation (MFC), which concerns a realistic scenario in general
COSFIRE
- Minimization of Region-Scalable Fitting Energy for Image Segmentation
2008__RSF-LiChunMing
- 李春明 2008年文章“Minimization of Region-Scalable Fitting Energy for Image Segmentation”的文章源码-Li Chunming 2008 article " Minimization of Region-Scalable Fitting Energy for Image Segmentation" article source
RSF_v0_v0.1
- Minimization of Region-Scalable Fitting Energy for Image Segmentation(RSF Minimization of Region-Scalable Fitting Energy for Image Segmentation)
SVAC标准介绍
- 支持高精度视频数据，在高动态范围场景提供更多图像细节 支持先进编码工具，在获得更好图像质量的同时获得更高编码效率 支持感兴趣区域（ROI）变质量编码，在网络带宽或存储空间有限的情况下，提供更符 合监控需要的高质量视频编码 支持可伸缩视频编码（SVC），满足不同传输网络带宽和数据存储环境的需求 u 支持代数码书激励线性预测（ACELP）和变换音频编码（TAC）切换的双核音频编码， 保证对语音和环境（背景）声音均有较好的编码
pytorch-a2c-ppo-acktr-master
- 改代码为ACKTR代码，该算法比传统的TRPO和DQN在运行速度和计算量都有很大的提升(scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation)