'Interest points' diaporamas de présentation

Top-points in Image Matching

Top-points in Image Matching. Bram Platel Evgenya Balmashnova Luc Florack Bart ter Haar Romeny. Introduction. Question: can top-points be used for object-retrieval tasks?. Introduction to Scale Space and Deep Structure. Importance of Scale in Image Analysis.

944 views • 75 slides

Local spatio-temporal image features for motion interpretation

Computational Vision and Active Perception Laboratory (CVAP) Dept of Numerical Analysis and Computer Science KTH (Royal Institute of Technology) SE-100 44 Stockholm, Sweden. Local spatio-temporal image features for motion interpretation. Ivan Laptev. Motivation. Goal:

867 views • 73 slides

IRISA / INRIA Rennes ilaptev@irisa.fr

IRISA / INRIA Rennes ilaptev@irisa.fr. Local Motion Features for Video interpretation. Ivan Laptev. Computational Vision and Active Perception Laboratory (CVAP) KTH (Royal Institute of Technology) http://www.nada.kth.se/~laptev. Common methods:. Camera stabilization. ?. Segmentation.

850 views • 69 slides

Vision-based SLAM

1.15k views • 66 slides

10/29/15

Object Recognition and Augmented Reality. 10/29/15. Dali, Swans Reflecting Elephants. Computational Photography Derek Hoiem, University of Illinois. Last class: Image Stitching. Detect keypoints Match keypoints Use RANSAC to estimate homography Project onto a surface and blend.

1.73k views • 64 slides

Multimedia Storage Techniques

853 views • 62 slides

Project 3 questions?

Project 3 questions?. 10/21/11. Interest Points and Instance Recognition. Computer Vision CS 143, Brown James Hays. Many slides from Kristen Grauman and Derek Hoiem. Last time. Detecting corner-like points in an image. Today. Local invariant features Detection of interest points

699 views • 61 slides

10/21/10

Object Recognition and Augmented Reality. 10/21/10. Dali, Swans Reflecting Elephants. Computational Photography Derek Hoiem, University of Illinois. Last class: Image Stitching. Detect keypoints Match keypoints Use RANSAC to estimate homography Project onto a surface and blend.

675 views • 57 slides

Interest Points

647 views • 54 slides

Overview

Overview. Introduction to local features Harris interest points + SSD, ZNCC, SIFT Scale & affine invariant interest point detectors Evaluation and comparison of different detectors Region descriptors and their performance. Scale invariance - motivation.

650 views • 45 slides

More on Features

More on Features. Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/3/25. with slides by Trevor Darrell Cordelia Schmid , David Lowe, Darya Frolova, Denis Simakov , Robert Collins, Brad Osgood, W W L Chen, and Jiwon Kim. Announcements.

538 views • 44 slides

Space-time interest points

Space-time interest points. Ivan Laptev and Tony Lindeberg. Computational Vision and Active Perception Laboratory (CVAP) Dept of Numerical Analysis and Computer Science KTH (Royal Institute of Technology) SE-100 44 Stockholm, Sweden. General motivation.

603 views • 39 slides

Features, Feature descriptors, Matching

593 views • 38 slides

Recognition and Matching based on local invariant features

Recognition and Matching based on local invariant features. Cordelia Schmid INRIA, Grenoble. David Lowe Univ. of British Columbia. ( ). Introduction. Local invariant photometric descriptors. local descriptor. Local : robust to occlusion/clutter + no segmentation

594 views • 37 slides

Vision and SLAM

587 views • 35 slides

APECE-505 Intelligent System Engineering Low-level Processing Md. Atiqur Rahman Ahad

335 views • 23 slides

OfficeTrack

348 views • 16 slides

Part 4: combined segmentation and recognition

Part 4: combined segmentation and recognition. Li Fei-Fei. Aim. Given an image and object category, to segment the object. Object Category Model. Segmentation. Cow Image. Segmented Cow. Segmentation should (ideally) be shaped like the object e.g. cow-like

255 views • 15 slides

CSSE463: Image Recognition Day 30

273 views • 15 slides

CSSE463: Image Recognition Day 30

398 views • 13 slides