One of the most common questions we get is whether to estimate in time or points. It seems like points are used only “to avoid thinking about time” and they are essentially the same. Wrong. Let us give you the travel metaphor to give you an idea abou…

这个算法是汪悦在 Lane detection and tracking using B-spline中提出来的.他在这篇论文中主要用的是B-spline模型,这个模型的主要优点是鲁棒性好,可以针对不同的情景进行处理,而且他将检测道路两边的边缘的问题转化成求解道路中间线的问题. 下面主要描述一下CHEVP算法: 边缘像素提取 我们使用Canny边缘检测来获得边缘映射和边缘定位映射.选择方差σ = 1 并且模板的尺寸是9*1在X方向和Y方向上进行高斯卷积.边缘映射是通过一个合适的阈值处理得到的结果…

文章文件夹: x264 编码器选项分析 (x264 Codec Strong and Weak Points) 1 x264 编码器选项分析 (x264 Codec Strong and Weak Points) 2 ====================== 本文简单翻译了MSU实验室做的X264的Option(即编码选项,后文称其英文名)分析报告<x264 Codec Strong and Weak Points>.看了之后感觉分析得十分透彻.并且其採用的方法也非常有參考价值,因此记录一…

Imagination is an outcome of what you learned. If you can imagine the world, that means you have learned what the world is about. Actually we don't know how we see, at lease it's really hard to know, so we can't program to tell a machine to see. One…

Depth estimation/stereo matching/optical flow @CVPR 2017 Unsupervised Learning of Depth and Ego-Motion from Video https://people.eecs.berkeley.edu/%7Etinghuiz/projects/SfMLearner/ https://www.reddit.com/r/MachineLearning/comments/6u06y8/p_selfsupervi…

论文原址:https://arxiv.org/abs/1901.08043 github: https://github.com/xingyizhou/ExtremeNet 摘要 本文利用一个关键点检测网络来检测目标物的最左边,最右边,顶部,底部及目标物中心五个点.如果这几个点在几何空间上对齐,则生成一个边界框.目标检测进而演变为基于外形的关键点检测问题,不需要进行区域分类及复杂的特征学习. 介绍 Top-Down方法占据目标检测中的主要地位,一些流行的目标检测算法通过直接裁剪区域或者特征,或者…

Awesome Works  !!!! Table of Contents Conference Papers 2017 ICCV 2017 CVPR 2017 Others 2016 ECCV 2016 CVPR 2016 Others 2015 ICCV 2015 CVPR 2015 Others 2014 CVPR 2014 Others & Before Journal Papers Theses Datasets Challenges Other Related Papers Eval…

论文来自Mikolov等人的<Efficient Estimation of Word Representations in Vector Space> 论文地址: 66666 论文介绍了2个方法,原理不解释... skim code and comment : # -*- coding: utf-8 -*- # @time : 2019/11/9 12:53 import numpy as np import torch import torch.nn as nn import torch.…

最近在菜鸟教程上自学redis.看到Redis HyperLogLog的时候,对"基数"以及其它一些没接触过(或者是忘了)的东西产生了好奇. 于是就去搜了"HyperLogLog",从而引出了Cardinality Estimation算法,以及学习它时参考的一些文章: http://blog.codinglabs.org/articles/algorithms-for-cardinality-estimation-part-i.html 从文章上看来,基数是指一个…

每一个实数都能用有理数去逼近到任意精确的程度,这就是有理数的稠密性.The rational points are dense on the number axis.…