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A Multi-scale Feature Likelihood Map for Direct Evaluation of Object Hypotheses⋆

  • Ivan Laptev
  • Tony Lindeberg
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)

Abstract

This paper develops and investigates a new approach for evaluating feature based object hypotheses in a direct way. The idea is to compute a feature likelihood map (FLM), which is a function normalized to the interval [0; 1], and which approximates the likelihood of image features at all points in scale-space. In our case, the FLM is defined from Gaussian derivative operators and in such a way that it assumes its strongest responses near the centers of symmetric blob-like or elongated ridge-like structures and at scales that reflect the size of these structures in the image domain. While the FLM inherits several advantages of feature based image representations, it also (i) avoids the need for explicit search when matching features in object models to image data, and (ii) eliminates the need for thresholds present in most traditional feature based approaches. In an application presented in this paper, the FLM is applied to simultaneous tracking and recognition of hand models based on particle filtering. The experiments demonstrate the feasibility of the approach, and that real time performance can be obtained by a pyramid implementation of the proposed concept.

Keywords

Image Structure Scale Selection Object Hypothesis Simultaneous Tracking Local Image Structure 
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 2001

Authors and Affiliations

  • Ivan Laptev
    • 1
  • Tony Lindeberg
    • 1
  1. 1.Dept. of Numerical Analysis and Computing ScienceComputational Vision and Active Perception Laboratory (CVAP)StockholmSweden

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