GCPR 2014: Pattern Recognition pp 488-498 | Cite as

Distance-Based Descriptors and Their Application in the Task of Object Detection

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

In this paper, we propose an efficient and interesting way how to encode the shape of the objects. A lot of state-of-the art descriptors (e.g. HOG, Haar, LBP) are based on the fact that the shape of the objects can be described by brightness differences inside the image. It means that the descriptors encode the gradient or intensity differences inside the image (i.e. edges). In the cases that the edges are very thin, the edge information can be difficult to obtain and the dimensionally of feature vector (without the method for reduction) is typically large and contains redundant information. These ills are motivation for the proposed method in that the edges need not be hit directly; the input brightness function is transformed using the appropriate image distance function. After this transformation, the values of distance function inside objects and backgrounds are different and the values can be used for description of object appearance. We demonstrate the properties of the method for the case of solving the problem of face detection using the classical sliding window technique.

Keywords

Feature Vector Distance Function Local Binary Pattern Face Detection Geodesic Distance 
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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Notes

Acknowledgments

This work was supported by the SGS in VSB Technical University of Ostrava, Czech Republic, under the grant No. SP2014/170.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceTechnical University of Ostrava, FEECSOstrava-PorubaCzech Republic

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