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A Neural Scheme for Robust Detection of Transparent Logos in TV Programs

  • Stefan Duffner
  • Christophe Garcia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

In this paper, we present a connectionist approach for detecting and precisely localizing transparent logos in TV programs. Our system automatically synthesizes simple problem-specific feature extractors from a training set of logo images, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the logo pattern to analyze. We present in detail the design of our architecture, our learning strategy and the resulting process of logo detection. We also provide experimental results to illustrate the robustness of our approach, that does not require any local preprocessing and leads to a straightforward real time implementation.

Keywords

False Alarm Convolutional Neural Network Generalize Regression Neural Network Robust Detection Convolutional Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefan Duffner
    • 1
  • Christophe Garcia
    • 1
  1. 1.France Telecom Division Research & DevelopmentCesson-SévignéFrance

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