1. Kirk Borne He is the principal data scientist and executive advisor at the Booz Allen Hamilton since 2015. He worked as an astrophysicist in NASA, thereafter he was also called upon to analyze the big data, by Gorge Bush post the 9/11 attack on the WTC, in order to prevent further attacks. 2. Bernard Marr Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to…
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1. Ronald van Loon @Ronald_vanLoon New #algorithm can generate highly realistic animated heads from still images | #AI #ArtificialIntelligence #Innovation #SmartTech #IoT #EmergingTech #FutureofWork InternetofThings Automation Videos RT Cc: @mclynd @jblefevre60 @mikequindazzi @diioannid pic.twitter.com/ag5Z900NzH pic.twitter.com/tugRnQ75m2 — Ronald van Loon @HPE #AnalystSummit (@Ronald_vanLoon) September 27, 2019 2. Kirk Borne @KirkDBorne 25 Best Free Datasets for #MachineLearning : https://t.co/GY2kUzUvs7 by @edurekaIN HT @ipfconline1 ——————#AI #DeepLearning #DataScience #BigData #OpenData pic.twitter.com/V4Pl2P5CTP — Kirk Borne (@KirkDBorne) September 27, 2019 3.…
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Highlights: In this post we will learn about fundamental matrix and we will continue our series about stereo vision. In the last post we concluded that if we have enough points we should be able to figure out the constraints for the epipolar line. So for this, we will need to calculate a fundamental matrix. Tutorial Overview: Weak calibration Uncalibrated Case Matrix Multiplication Properties of the Fundamental Matrix Intro In previous posts we have developed…
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Current trends in the automotive industry show that the world’s largest manufacturers are heavily preparing for a future in which car driving will be decided by computers, not by the people. The roads that autonomous vehicles will drive are no longer science fiction but a fact that could become reality in just a few years. But what about a self-driving trucks? Will they also drive the roads alongside autonomous cars in near future? Difference between…
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Highlights: In this post we continue our talk about stereo geometry. We will show the relation between two calibrated cameras. The goal is to transform geometry into algebra, so then the computer can understand it. Tutorial Overview: This post covers the following topics: Intro Stereo Correspondence Stereo Geometry Cross Product 1 From Geometry to Algebra Cross Product 2 Essential Matrix 1. Intro Before we start, we have to remind ourself some things. So first thing…
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