Motion-based Detection and Tracking in 3D LiDAR Scans Ayush Dewan Tim Caselitz Gian Diego Tipaldi

Source URL: europa2.informatik.uni-freiburg.de

• Statistics
• Signal processing
• Artificial intelligence
• Video processing
• Lidar
• Robotic sensing
• Random sample consensus
• Motion detection
• Computer vision
• Kalman filter
• Motion estimation
• Rigid motion segmentation

ICP Stereo Visual Odometry for Wheeled Vehicles based on a 1DOF Motion Prior Yanhua Jiang1,2 , Huiyan Chen1 , Guangming Xiong1 , Davide Scaramuzza2 Abstract— In this paper, we propose a novel, efficient stereo visual-o

Source URL: rpg.ifi.uzh.ch

• Computer vision
• Artificial intelligence
• Vision
• Robust statistics
• Random sample consensus
• SRI International
• Robotic sensing
• Motion estimation
• Visual odometry
• Structure from motion
• Iterative closest point
• Pose

1-Point-based Monocular Motion Estimation for Computationally-Limited Micro Aerial Vehicles Chiara Troiani1 , Agostino Martinelli1 , Christian Laugier1 and Davide Scaramuzza2 TABLE I: Number of iterations of RANSAC Abst

Source URL: rpg.ifi.uzh.ch

• Statistics
• Random sample consensus
• SRI International
• Vision
• Artificial intelligence
• Motion estimation
• Structure from motion
• Visual odometry
• Reprojection error
• Motion capture
• Outlier

Robust Structural Metric Learning Daryl K. H. Lim Department of Electrical and Computer Engineering, University of California, San Diego, CAUSA Brian McFee

Source URL: bmcfee.github.io

• Statistics
• Information science
• Information retrieval
• Machine learning
• Learning to rank
• Monotone likelihood ratio
• K-nearest neighbors algorithm
• Random sample consensus

Pseudo-code for the RAndom SAmple Consensus (RANSAC) Algorithm RANSAC is an iterative algorithm which can be used to estimate parameters of a statistical model from a set of observed data which contains outliers. Below w

Source URL: learning.eng.cam.ac.uk

2. Effective Approximations of Matching Priors 1. Summary How can we achieve truly robust dense, real-time matching? The strong priors generally available on image correspondence problems, are often used partially and p

Source URL: www.margaritachli.com

• Statistics
• Mathematics
• Random sample consensus
• SRI International
• Matching

Online Robust Dictionary Learning Cewu Lu Jianping Shi Jiaya Jia The Chinese University of Hong Kong {cwlu,jpshi,leojia}@cse.cuhk.edu.hk

Source URL: www.cse.cuhk.edu.hk

• Statistics
• Robust statistics
• Machine learning
• Statistical theory
• Regression analysis
• Estimation theory
• Sparse dictionary learning
• Outlier
• Random sample consensus
• Iteratively reweighted least squares
• K-SVD
• Sparse approximation

Eurographics Symposium on Geometry ProcessingM. Desbrun, H. Pottmann (Editors) Robust Global Registration Natasha Gelfand † ‡

Source URL: geometry.stanford.edu

• Feature detection
• Computer vision
• Vision
• Artificial intelligence
• Image processing
• Shape context
• Speeded up robust features
• Feature
• Outline of object recognition
• Image registration
• Random sample consensus
• Generalised Hough transform

Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
Avrim Blum

Source URL: hunch.net

• Statistics
• Machine learning
• Learning
• Artificial intelligence
• Design of experiments
• Hypothesis testing
• Nonparametric statistics
• Stability
• Generalization error
• Cross-validation
• Random sample consensus
• Statistical hypothesis testing

Scalable Active Matching Ankur Handa, Margarita Chli, Hauke Strasdat and Andrew J. Davison Department of Computing, Imperial College London, UK {ahanda, mchli, strasdat, ajd}@doc.ic.ac.uk Abstract

Source URL: www.margaritachli.com

• Artificial intelligence
• Statistics
• Mathematics
• Machine learning
• Graphical models
• Probability theory
• Combinatorial optimization
• Matching
• Pattern recognition
• Random sample consensus
• Mixture model
• Simultaneous localization and mapping