'Feature point' presentation slideshows

Sparse Point-Sets Matching with underlying non-rigidity

ICPR August 23-26, 2004 University of Cambridge, UK. Sparse Point-Sets Matching with underlying non-rigidity. Baihua Li, Qinggiang Meng, Horst Holstein (speaker) bal@aber.ac.uk, qqm@aber.ac.uk, hoh@aber.ac.uk. University of Wales, Aberystwyth, UK. Where we are.

882 views • 78 slides

Feature Point Matching

Feature Point Matching. Detection, Extraction & Matching. Contents. Feature point matching What is it? What is it for? Feature point detection Moravec feature point detector Harris corner detector Scale space detection Feature point extraction & matching Matching using templates

829 views • 55 slides

Software Engineering

Software Engineering. SM ?. Outline of this presentation. What is SM The Need for SM Type of SM Size Oriented Metric Function Oriented Metric. Software Metric. A metric is a measurement of some aspect of the software product or the software process. Software Metric. What is SM.

612 views • 35 slides

Software Engineering

Software Engineering. SM ?. Outline of this presentation. What is SM The Need for SM Type of SM Size Oriented Metric Function Oriented Metric. Software Metric. A metric is a measurement of some aspect of the software product or the software process. Software Metric. What is SM.

478 views • 35 slides

Time Series Sequence Matching

471 views • 30 slides

Feature Extraction and Matching Feature Tracking Sudipta N Sinha Sep 19, 2006

Feature Extraction and Matching Feature Tracking Sudipta N Sinha Sep 19, 2006. Outline. Feature Extraction and Matching (for Larger Motion) What are features ? Tasks Detection: finding the feature locations Representation: computing a compact descriptor

608 views • 29 slides

Probabilistic Framework for Feature-point Matching

Probabilistic Framework for Feature-point Matching. Ron Tal, Minas E. Spetsakis York University. Outline. Introduction Probabilistic Framework Theoretical Validation Our Algorithm Results Discussion. Introduction: The Problem.

348 views • 25 slides

Rodrigo Braga Pinheiro

Rodrigo Braga Pinheiro. What is it?. It is a point-based rendering, i.e., the 3D models are represented by points (point cloud) and not by triangles, as they are commonly represented.

342 views • 22 slides

Wei-Ta Chu , Che -Cheng Lin ,Jen-Yu Yu

Using Cross-Media Correlation for Scene Detection in Travel Videos. Wei-Ta Chu , Che -Cheng Lin ,Jen-Yu Yu. Outline. Introduction Approach Experiments Conclusion. Introduction. Why Use Cross Media Correlation for Scene Detection in Travel Video??

304 views • 20 slides

Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion

Real-Time Efficient Parallel Thermal and Visual Face Recognition Fusion. 2009/12/24 ???. Outline. I ntroduction G abor F iltering F or F ace Recognition -Feature point calculation ? selection ? Feature vector generation -Similarity calculations

350 views • 19 slides

Three-dimensional Motion Capture, Modelling and Analysis of Ski Jumpers

Three-dimensional Motion Capture, Modelling and Analysis of Ski Jumpers. Atle Nes CSGSC 2005 Trondheim, April 28th. Overview. Project description What kind of data are we interested in? Capturing data: Image acquisition, Camera system

320 views • 18 slides

Outline

VISION-AUGMENTED INERTIAL NAVIGATION BY SENSOR FUSION FOR AN AUTONOMOUS ROTORCRAFT VEHICLE C.L. Bottasso , D. Leonello Politecnico di Milano AHS International Specialists' Meeting on Unmanned Rotorcraft Phoenix, AZ, January 20-22, 2009. Outline. Introduction and motivation

308 views • 17 slides

Representation

Representation. A farmer wants to move himself, a hunrgry silver fox, a hungry, fat goose, and a sack of tasty grain across a river. Unfortunately, his boat is so tiny he can move only one of his possessions across on any trip. What is he to do?. Fox, Farmer, Goose, Grain.

667 views • 16 slides

Determining Moving Objects from Forward Motion Video

Determining Moving Objects from Forward Motion Video. Nick Hirsch: Progress Report. Overview of the Technique. Track features from frame to frame using KLT Calculate their optical flow. Determine Focus of Expansion The center that all other points seem to expand from when moving forward.

321 views • 13 slides

Automated Rule of Thirds Compliancy

246 views • 11 slides