From images to shape models for object detection - Archive ouverte HAL Access content directly
Reports (Research Report) Year : 2008

From images to shape models for object detection

(1) , (1) , (1)
1
Vittorio Ferrari
  • Function : Author
  • PersonId : 852592
Frédéric Jurie
Cordelia Schmid
  • Function : Author
  • PersonId : 831154

Abstract

We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is made possible by a novel technique for learning a shape model of an object class given {\em images} of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately, while needing no segmented examples for training (only bounding-boxes)
Vignette du fichier
icon.jpg (5.83 Ko) Télécharger le fichier Fichier principal
Vignette du fichier
RR-6600.pdf (2.3 Mo) Télécharger le fichier
Format : Figure, Image
Origin : Files produced by the author(s)
Loading...

Dates and versions

inria-00308388 , version 1 (30-07-2008)

Identifiers

  • HAL Id : inria-00308388 , version 1

Cite

Vittorio Ferrari, Frédéric Jurie, Cordelia Schmid. From images to shape models for object detection. [Research Report] RR-6600, INRIA. 2008. ⟨inria-00308388⟩
239 View
905 Download

Share

Gmail Facebook Twitter LinkedIn More