Detection and Localization of Random Signals
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Detection and Localization of Random Signals. / Sporring, Jon; Olsen, Niels Holm; Nielsen, Mads.
Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings. 2003. p. 785-797 (Lecture notes in computer science, Vol. 2695/2003).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Detection and Localization of Random Signals
AU - Sporring, Jon
AU - Olsen, Niels Holm
AU - Nielsen, Mads
N1 - Conference code: 4
PY - 2003
Y1 - 2003
N2 - Object detection and localization are common tasks in image analysis. Correlation based detection algorithms are known to work well, when dealing with objects with known geometry in Gaussianly distributed additive noise. In the Bayes’ view, correlation is linearly related to the logarithm of the probability density, and optimal object detection is obtained by the integral of the exponentiated squared correlation under appropriate normalization. Correlation with a model is linear in the input image, and can be computed effectively for all possible positions of the model using Fourier based linear filtering techniques. It is therefore interesting to extend the application to objects with many but small degrees of freedom in their geometry. These geometric variations deteriorate the linear correlation signal, both regarding its strength and localization with multiple peaks from a single object. Localization is typically preferred over detection, and Bayesian localization may be obtained as local integration of the probability density. In this work, Gaussian kernels of the exponentiated correlation are studied, and the use of Linear Scale-Space allows us to extend the Bayes detection with a well-posed localization, to extend the usage of correlation to a larger class of shapes, and to argue for the use of mathematical morphology with quadratic structuring elements on correlation images. This project is supported in part by the Danish Research Agency, project “Computing Natural Shape”, no. 2051-01-0008 and in part by the DSSCV project under the IST Programme of the European Union (IST-2001-35443)
AB - Object detection and localization are common tasks in image analysis. Correlation based detection algorithms are known to work well, when dealing with objects with known geometry in Gaussianly distributed additive noise. In the Bayes’ view, correlation is linearly related to the logarithm of the probability density, and optimal object detection is obtained by the integral of the exponentiated squared correlation under appropriate normalization. Correlation with a model is linear in the input image, and can be computed effectively for all possible positions of the model using Fourier based linear filtering techniques. It is therefore interesting to extend the application to objects with many but small degrees of freedom in their geometry. These geometric variations deteriorate the linear correlation signal, both regarding its strength and localization with multiple peaks from a single object. Localization is typically preferred over detection, and Bayesian localization may be obtained as local integration of the probability density. In this work, Gaussian kernels of the exponentiated correlation are studied, and the use of Linear Scale-Space allows us to extend the Bayes detection with a well-posed localization, to extend the usage of correlation to a larger class of shapes, and to argue for the use of mathematical morphology with quadratic structuring elements on correlation images. This project is supported in part by the Danish Research Agency, project “Computing Natural Shape”, no. 2051-01-0008 and in part by the DSSCV project under the IST Programme of the European Union (IST-2001-35443)
U2 - 10.1007/3-540-44935-3_55
DO - 10.1007/3-540-44935-3_55
M3 - Article in proceedings
T3 - Lecture notes in computer science
SP - 785
EP - 797
BT - Scale Space Methods in Computer Vision
Y2 - 29 November 2010
ER -
ID: 5581838