'Feature descriptors' diaporamas de présentation

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.

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Image alignment

Image alignment. Image from http://graphics.cs.cmu.edu/courses/15-463/2010_fall/. A look into the past. http://blog.flickr.net/en/2010/01/27/a-look-into-the-past/. A look into the past. Leningrad during the blockade. http://komen-dant.livejournal.com/345684.html. Bing streetside images.

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Image alignment

Image alignment. Image source. Alignment applications. Panorama stitching. http:// matthewalunbrown.com / autostitch / autostitch.html. Alignment applications. A look into the past. Alignment applications. A look into the past. Alignment applications. Cool video. Alignment applications.

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PostgreSQL-IE: An Image-handling Extension for PostgreSQL

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Lecture 5: Feature descriptors and matching

CS5670: Computer Vision. Noah Snavely. Lecture 5: Feature descriptors and matching. Reading. Szeliski: 4.1. Announcements. Project 2 is now released, due Monday, March 4, at 11:59pm To be done in groups of 2 Please create a group on CMS once you have found a partner

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Robust and large-scale alignment

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SIFT

SIFT. CSE 455, Winter 2010 February 3 , 2010. Announcements. Project 1b grades out https://catalysttools.washington.edu/gradebook/iansimon/17677 Statistics Mean 88.95 Median 100 Std. Dev. 20.97. Review From Last Time. Image Matching. NASA Mars Rover images

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SURF Feature Compression

SURF Feature Compression. EE398 Winter 2008 Ivan Janatra June Zhang. Outline. SURF Features Transform coding Quantization Encoding Results. SURF Features. Given a set of SURF feature descriptors. Explore how compression affect feature descriptors and their performance. Query image.

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COS 429 PS3: Stitching a Panorama

COS 429 PS3: Stitching a Panorama. Due November 4 th. Goal. Find key features in images and correspondences between images Use RANSAC to find the best correspondences Map one image plane to another to create a panoramic image. Problem 1: Preprocessing.

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Comparison of Image Feature Descriptors for Mobile Visual Search

Comparison of Image Feature Descriptors for Mobile Visual Search. Vijay Chandrasekhar David Chen Andy Lin Gabriel Takacs Sam Tsai Ngai-man Cheung Bernd Girod Stanford University. Outline. Feature-level experiments Winder & Brown data set Experiment 1: Evaluation of MPEG-7 descriptor

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Non-linear Distribution of Descriptors Descriptors are non-linearly distributed.

MVSV. Descriptor-level. X. X. M-PCCA. SVM. Fusion. GMM. Fusion. SVM. Score. Score. Y. Y. GMM. (linear) Kernel-level. X. Fusion. SVM. GMM. Score. Y. GMM. SVM. Score-level. X. Score. Fusion. Score. SVM. GMM. Score. Y. Multi-View Super Vector for Action Recognition

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