The reprojection error is a geometric error corresponding to the image distance between a projected point and a measured one. It is used to quantify how closely an estimate of a 3D point recreates the point's true projection . More precisely, let be the projection matrix of a camera and be the image projection of , i.e. . The reprojection error of is given by , where denotes the Euclidean distance between the image points represented by vectors and . Minimizing the reprojection error can be used for estimating the error from point correspondences between two images. Suppose we are given 2D to 2D point imperfect correspondences . We wish to find a homography and pairs of perfectly matched points and , i.e. points that satisfy that minimize the reprojection error function given by So the correspondences can be interpreted as imperfect images of a world point and the reprojection error quantifies their deviation from the true image projections (Wikipedia).
Why do some space launches fail?
Any space aficionado will tell you that sometimes space launches can go horribly wrong. Indeed, we have all watched a launch online only to see it delayed. What happens in these cases and what can be done to prevent such problem? Watch the video to find out more. Find out more infor
From playlist Theory to Reality
I guess this is what happens when you don't put anything up to keep the shopping carts from falling out of the truck.
From playlist Inertia
GCSE Science Revision "Systematic Errors"
In this video, we look at systematic errors. First we explore what is meant by a systematic error. We then look at what can cause a systematic error, including a zero error. Image Credits Thermometer https://commons.wikimedia.org/wiki/File:Laboratory_thermometer-03.jpg Lilly_M, CC BY-SA
From playlist GCSE Working Scientifically
Yes. I make mistakes ... rarely. http://www.flippingphysics.com
From playlist Miscellaneous
Marc Levoy - Lectures on Digital Photography - Lecture 18 (01jun16).mp4
This is one of 18 videos representing lectures on digital photography, from a version of my Stanford course CS 178 that was recorded at Google in Spring 2016. A web site that includes all 18 videos, my slides, and the course schedule, applets, and assignments is http://sites.google.com/sit
From playlist Stanford: Digital Photography with Marc Levoy | CosmoLearning Computer Science
Stereo Vision | Student Competition: Computer Vision Training
In this video, you will learn about stereo vision and calibrating stereo cameras. We will use an example of reconstructing a scene using stereo vision. Get files: https://bit.ly/2ZBy0q2 Explore the MATLAB and Simulink Robotics Arena: https://bit.ly/2yIgwfS ---------------------------------
From playlist Student Competition: Computer Vision Training
Learn how to find and classify the discontinuity of the function
👉 Learn how to classify the discontinuity of a function. A function is said to be discontinuous if there is a gap in the graph of the function. Some discontinuities are removable while others are non-removable. There is also jump discontinuity. A discontinuity is removable when the denomi
From playlist Holes and Asymptotes of Rational Functions
Determine the discontinuity of the function
👉 Learn how to classify the discontinuity of a function. A function is said to be discontinuos if there is a gap in the graph of the function. Some discontinuities are removable while others are non-removable. There is also jump discontinuity. A discontinuity is removable when the denomin
From playlist Holes and Asymptotes of Rational Functions
How To Identify Type I and Type II Errors In Statistics
This statistics video tutorial provides a basic introduction into Type I errors and Type II errors. A type I error occurs when a true null hypothesis is rejected. A type II error occurs when a false null hypothesis is not rejected. This video contains a few examples and practice problem
From playlist Statistics
Matteo Gori - 2nd-Quantization of Many-Body Dispersion Formalism: Modeling of Million Atom Systems
Recorded 01 April 2022. Matteo Gori of the University of Luxembourg Department of Science and Materials presents "Second-Quantization of Many-Body Dispersion Formalism: Towards Modeling of Million Atom Systems in Arbitrary Environments" at IPAM's Multiscale Approaches in Quantum Mechanics
From playlist 2022 Multiscale Approaches in Quantum Mechanics Workshop
Stanford Seminar - Deploying Autonomous Service Mobile Robots, And Keeping Them Autonomous
Joydeep Biswas - https://www.joydeepb.com/ UT Austin April 15, 2022 Why is it so hard to deploy autonomous service mobile robots in unstructured human environments, and to keep them autonomous? In this talk, I will explain three key challenges, and our recent research in overcoming them:
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
Which Incapacitating Agent is the Most Effective?
This time, we're ranking incapacitating agents! The term incapacitating agent is defined by the United States Department of Defense as: "An agent that produces temporary physiological or mental effects, or both, which will render individuals incapable of concerted effort in the performance
From playlist Chemistry Tierlists
Standard Deviation vs Standard Error, Clearly Explained!!!
People often confuse the standard deviation and the standard error. This StatQuest clears it all up! For more information on the standard error, see the StatQuest on The Standard Error: https://youtu.be/XNgt7F6FqDU And the StatQuest on p-value pitfalls and power calculations: https://yout
From playlist StatQuest
NeX: Real-time View Synthesis with Neural Basis Expansion + NERF [Paper explaned]
The proposed approach uses a modification of Multiplane Image (MPI), where it models view-dependent effects by parameterizing each pixel as a linear combination of basis functions learned by a neural network. The pixel representation (i.e., the coordinates in the set of bases defined by th
From playlist Computer Vision
Stanford Seminar - Self-Supervised Pseudo-Lidar Networks
Adrien Gaidon Toyota Research Institute October 11, 2019 Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception, especially in safety critical contexts like Automated Driving. Nonetheless, recent progress in combining deep l
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar
Discovering Relationships between Object Categories via Universal Canonical Maps [CVPR 2021]
Authors: Facebook AI Research Natalia Neverova*, Artsiom Sanakoyeu*, Patrick Labatut, David Novotny, Andrea Vedaldi Project page: https://gdude.de/discovering-3d-obj-rel/ Abstract: We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Rece
From playlist Computer Vision
What is an Injective Function? Definition and Explanation
An explanation to help understand what it means for a function to be injective, also known as one-to-one. The definition of an injection leads us to some important properties of injective functions! Subscribe to see more new math videos! Music: OcularNebula - The Lopez
From playlist Functions
JunoCam, Perijove 10, Animated Cloud Motion
JunoCam, Perijove 10, Animated Cloud Motion Caution: This movie might not be suitable for people with epilepsy. This movie shows the short-term dynamics Jupiter's southern storms derived from raw JunoCam images of Juno's Perijove-10 flyby on Dec 16, 2017. You might also notice the effect
From playlist Space Photography Playlist