'Interest point' diaporamas de présentation

Interest Point Descriptors and Matching

948 views • 79 slides

Local feature extraction

Local feature extraction. Slide credits: James Tompkin , Juan Carlos Niebles and Ranjay Krishna. General Approach. 1. Find a set of distinctive key- points. 2. Define a region around each keypoint. A 1. B 3.

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Interest Point Detectors (see CS485/685 notes for more details)

Interest Point Detectors (see CS485/685 notes for more details). CS491Y/691Y Topics in Computer Vision Dr. George Bebis. What is an Interest Point?. A point in an image which has a well-defined position and can be robustly detected.

770 views • 76 slides

Motion illusion, rotating snakes

Motion illusion, rotating snakes. Local features: main components. Detection: Find a set of distinctive key points. Description : Extract feature descriptor around each interest point as vector. Matching: Compute distance between feature vectors to find correspondence.

738 views • 73 slides

Machine recognition of human activities : a survey

Pavan Turaga , Student Member, IEEE, Rama Chellappa , Fellow, IEEE, V. S. Subrahmanian , and Octavian Udrea. Machine recognition of human activities : a survey. Presented by Hakan Boyraz. Outline. Actions vs. Activities Applications of Activity Recognition Activity Recognition System

727 views • 59 slides

Interest Point Descriptors

750 views • 51 slides

Interest Point Descriptors

519 views • 51 slides

Interest Operator Lectures

Interest Operator Lectures. Harris Corner Detector: the first and most basic interest operator Kadir Entropy Detector and its use in object recognition SIFT interest point detector and region descriptor MSER region detector and Harris Affine in region matching

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Interest Operator Lectures

417 views • 41 slides

Interest Operator Lectures

Interest Operator Lectures. Harris Corner Detector: the first and most basic interest operator Kadir Entropy Detector and its use in object recognition SIFT interest point detector and region descriptor MSER region detector and Harris Affine in region matching Additional applications.

412 views • 41 slides

Learning to Efficiently Detect Repeatable Interest Points in Depth Data

Learning to Efficiently Detect Repeatable Interest Points in Depth Data. Stefan Holzer , Jamie Shotton , and Pushmeet Kohli Department of Computer Science, CAMP, Technische University at Munchen (TUM). Microsoft Research Cambridge. Presented by Rimma Shulman.

413 views • 35 slides

SURF: Speeded Up Robust Features

SURF: Speeded Up Robust Features. ???? : ??? ?? Group number: 20 Advisor: Tzuu-Hseng S. Li Group members: E24956552 ??? E24951099 ??? N26984224 ??? aiRobots Laboratory, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. Outline.

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Compact Signatures for High-speed Interest Point Description and Matching

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SURF : Speeded-Up Robust Features

CPSC 643, Presentation 2. SURF : Speeded-Up Robust Features. Herbert Bay a , Andreas Ess a , Tinne Tuytellares b , Luc Van Gool a,b a ETH Zurich b K. U. Leuven, ESAT-PSI Sternwartstrasse 7 Kasteel Arenberg 10

636 views • 19 slides

img1 img3 img5

img1 img3 img5. Your task. Write Harris detector ComputeHarris() in features.cpp Write Feature descriptor ExtractDescriptor() in features.cpp Run your program to get required numerical and image results

190 views • 18 slides

Real Time Object Recognition Using the SURF Algorithm

Real Time Object Recognition Using the SURF Algorithm. Frank Bergschneider February 21, 2014 Presented to National Instruments. Introduction. Object Recognition Real Time, Online Applications Applications : Security, Shopping, Augmented Reality Speeded Up Robust Features Algorithm

734 views • 15 slides

A Thousand Words in a Scene

A Thousand Words in a Scene. P. Quelhas, F. Monay, J. Odobez, D. Gatica-Perez and T. Tuytelaars PAMI, Sept. 2006. Outline. Introduction Image Representation Bag-of-Visterms (BOV) Representation Probabilistic Latent Semantic Analysis (PLSA) Scene Classification Experiments

238 views • 13 slides