'Projection error' diaporamas de présentation

Machine Learning for Signal Processing Eigenfaces and Eigen Representations

Machine Learning for Signal Processing Eigenfaces and Eigen Representations. Class 6. 12 Feb 2015 Instructor: Bhiksha Raj. Recall: Representing images. The most common element in the image: background Or rather large regions of relatively featureless shading Uniform sequences of numbers.

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Structure from motion Class 12

Structure from motion Class 12. Read Chapter 5. Assignment 2. Assignment 3. Collect potential matches from all algorithms for all pairs Matlab ASCII format, exchange data Implement RANSAC that uses combined match dataset Compute consistent set of matches and epipolar geometry

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Structure from motion Class 9

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Structure from motion Class 9

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Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration

Improving and Filtering Laser Data for Extrinsic Laser Range Finder/Camera Calibration. Sukhum Sattaratnamai Advisor: Dr.Nattee Niparnan. Outline. Introduction Objective Calibration Process Our Work Improving Laser Data Automate Data Collection Conclusion. LRF-Camera System. ?.

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The Heavy Flavor Tracker (HFT) The Silicon Vertex Upgrade of STAR @ RHIC Spiros Margetis

The Heavy Flavor Tracker (HFT) The Silicon Vertex Upgrade of STAR @ RHIC Spiros Margetis Kent State University, USA. Excited QCD 2010, Slovakia . Outline. The Physics The Challenges The HFT. The Bottom Line.

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Eigenfaces (2)

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Multiple View Reconstruction Class 23

Multiple View Reconstruction Class 23. Multiple View Geometry Comp 290-089 Marc Pollefeys. Administrivia. No papers to be presented Probably take home exam. Content. Background : Projective geometry (2D, 3D), Parameter estimation, Algorithm evaluation.

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Motivation I: Data Compression

Motivation I: Data Compression. Dimensionality Reduction. Machine Learning. Data Compression. Reduce data from 2D to 1D. (inches). (cm). Data Compression. Reduce data from 2D to 1D. (inches). (cm). Data Compression. Reduce data from 3D to 2D. Motivation II: Data Visualization.

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Presenter: Van-Dung Hoang dungvanhoang@islab.ulsan.ac.kr June 15, 2013

Real-Time 6D Stereo Visual Odometry with Non-Overlapping Fields of View Tim Kazik , Laurent Kneip , Janosch Nikolic , Marc Pollefeys and Roland Siegwart IEEE Conference Computer Vision and Pattern Recognition, 2012 . Presenter: Van-Dung Hoang dungvanhoang@islab.ulsan.ac.kr

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A principal components analysis self-organizing map

A principal components analysis self-organizing map. Ezequiel Lopez-Rubio, Jose Munoz-Perez, Jose Antonio Gomez-Ruiz. Advisor : Dr. Hsu Student : Sheng-Hsuan Wang Department of Information Management. Neural Network 17 (2004) 261-270. Outline. Motivation Objective

446 views • 26 slides