Instance level recognition part

Josef Sivic

Description

Instance-level recognition – part 2 Josef Sivic INRIA, WILLOW, ENS/INRIA/CNRS UMR 8548 Laboratoire d'Informatique, Ecole Normale Supérieure, Paris With slides from: O. Chum, K. Grauman, I. Laptev, S. Lazebnik, B. Leibe, D. Lowe, J. Philbin, J. Ponce, D. Nister, C. Schmid, N. Snavely, A. Zisserman Visual Recognition and Machine Learning Summer School Paris 2011

• image matching


• target image


• visual indexing


• wide baseline matching


• camera calibration


• harris-laplace


• ecole normale

Sujets

• Target
• Camera calibration
• École normale

Informations

• Publié par: pefav
• Nombre de lectures: 13
• Langue: English
• Poids de l'ouvrage: 9 Mo

Extrait

Visual Recognition and Machine Learning Summer Schoo l Paris 2011 Instance-level recognition – part 2 Josef Sivic http://www.di.ens.fr/~jose f INRIA, WILLOW, ENS/INRIA/CNRS UMR 8548 Laboratoire d’Informatique, Ecole Normale Supérieure, Paris Wit L h  o s w li e d , e  J s .     fr P o hi m l : b   i O n, .     J C .  h P u o m n , c   e K , .     D G . r   a N u is m te a r n ,  ,  C I. .     L S a c p h t m e i v d ,  ,  S N .  . L  S az n e a b v n e i l k y , ,     B A . .     L Zi e s i s b e e r ,  m D a .  n 

Outline 1.   Local invariant features (45 mins, C. Schmid) 2.   Matching and recognition with local features (45 mins, J. Sivic) 3.   Efficient visual search (45 mins, J. Sivic) 4.   Very large scale visual indexing – recent work (45 mins, C. Schmid) Practical session – Panorama stitching (60 mins) Download: http://www.di.ens.fr/willow/events/cvml2011/mosaic.zip

Image matching and recognition with local features The goal: establish correspondence between two or more images x X 'x Image points x and x’ ar € e    in correspondence if they are projections € of    the same 3D scene p € o   int X. Images courtesy A. Zisserman

Example I: Wide baseline matching Establish correspondence between two (or more) images. Useful in visual geometry: Camera calibration, 3D reconstruction, Structure and motion estimation, … Scale/affine – invariant regions: SIFT, Harris-Laplace, etc.

Example II: Object recognition Establish correspondence between the target image and (multiple) images in the model database. Model database i Ta m r a g g e e t     [D. Lowe, 1999]

Example III: Visual search Given a query image, find images depicting the same place / object in a large unordered image collection. Find these landmarks i...  erom M1 dna segami eseht n