Object Recognition in Indoor Video Sequences by Classifying Image Segmentation Regions Using Neural Networks

  • Nicolás Amezquita Gómez
  • René Alquézar
Conference paper

DOI: 10.1007/11578079_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)
Cite this paper as:
Gómez N.A., Alquézar R. (2005) Object Recognition in Indoor Video Sequences by Classifying Image Segmentation Regions Using Neural Networks. In: Sanfeliu A., Cortés M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg

Abstract

This paper presents the results obtained in a real experiment for object recognition in a sequence of images captured by a mobile robot in an indoor environment. The purpose is that the robot learns to identify and locate objects of interest in its environment from samples of different views of the objects taken from video sequences. In this work, objects are simply represented as an unstructured set of spots (image regions) for each frame, which are obtained from the result of an image segmentation algorithm applied on the whole sequence. Each spot is semi-automatically assigned to a class (one of the objects or the background) and different features (color, size and invariant moments) are computed for it. These labeled data are given to a feed-forward neural network which is trained to classify the spots. The results obtained with all the features, several feature subsets and a backward selection method show the feasibility of the approach and point to color as the fundamental feature for discriminative ability.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nicolás Amezquita Gómez
    • 1
  • René Alquézar
    • 1
  1. 1.Dept. Llenguatges i Sistemes InformàticsUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations