目录 Joint Tracking of Features and Edges Joint Tracking of Features and Edges 1. LK光流 基本LK光流运动假设: \[ I(x+u,y+v,t+1) = I(x,y,t) \] 一阶近似得到: \[ f(u,v,I) = I_xu+I_yv+I_t =0 \] 由于Aperture problem,需要假设领域像素运动相同,并作为约束,便可以求解 \[ E_{LK}(u,v) = K_{\rho}*(f(u,v;I)…

In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks. That's unfortunate, since we have good reason to believe that if we could train deep nets they'd be much more powerful than shallow…

Adit Deshpande CS Undergrad at UCLA ('19) Blog About A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do reali…

Participate in Reproducible Research General Image Processing OpenCV (C/C++ code, BSD lic) Image manipulation, matrix manipulation, transforms Torch3Vision (C/C++ code, BSD lic) Basic image processing, matrix manipulation and feature extraction algor…

this blog from: https://opendatascience.com/blog/notes-on-representation-learning-1/   Notes on Representation Learning By Zac Kriegman, Senior Data Scientist in the Thomson Reuters Data Innovation Lab | 02/07/2017 Tags: Deep Learning , Neural Networ…

A successful Git branching model In this post I present the development model that I’ve introduced for some of my projects (both at work and private) about a year ago, and which has turned out to be very successful. I’ve been meaning to write about i…

artifacts 瑕疵 伪影(Artifacts) 伪影(Artifacts)-CT-基础术语 - 影像园 http://www.xctmr.com/baike/ct/c34b5413e305b453bf0d91bfaaf058ed.html imatest的画质因素分析页面 Image Quality Factors | imatest http://www.imatest.com/solutions/iqfactors/ Artifacts | imatest http://www.ima…

为了吸引大家的注意力,先给大家看一张动图: 相信这种效果大家都见过吧?我第一次见到这样的效果时,心里也痒痒的,急于想实现这种功能,后来因为拖延症的问题,就一直没有去弄这件事.现在这段时间,工作比较轻闲,所以对自己几年 Android 生涯所运用的技术做一些总结与思考.拖拽这种功能正好可以形成一个主题.如题目所示,今天博文的目标就是介绍与分析 ViewDragHelper 这个类. 读者阅读本文后将会有如下收获: 1. 不借助于 ViewDragHelper 实现基本的拖拽效果. 2. 借助于 V…

Convolution Fundamental I Foundations of CNNs Learning to implement the foundational layers of CNN's (pooling,convolutions) and to stack them properly in a deep network to solve multi-class image classification problems. Computer vision Computer visi…

About this Course This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applica…