Home » A Multiscale Particle Filter and Multi Feature Based Contour Detection

A Multiscale Particle Filter and Multi Feature Based Contour Detection

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Devshri Swami

Department of Electronics and Communication Engineering,  Scope College of Engineering, Bhopal, India
email: devshri.swami@gmail.com

Bharti Chaurasia

Department of Electronics and Communication Engineering,  Scope College of Engineering, Bhopal, India
email: bharti.chourasia27@gmail.com

Yashwant Kurmi

Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology Bhopal, India
yashwantkurmi18@gmail.com

Abstract

In this paper, a multi-scale particle filter (MSPF) based algorithm and multi-feature-based surround inhibition (MFBSI) algorithm are proposed for contour detection in natural images. The contour detection algorithms are jointly tracks at two edgelets as edge elements. This multi-scale edgelet structure gathers the semi-local information as Bayesian modeling. The model is further approximate by using Monte Carlo algorithm. The individual surround inhibition weights of the feature as, including luminance, luminance contrast, and orientation. The scale-guided strategy is used for features together. The final surround inhibition is modulated using the combined weights in the neuron. The luminance contrast and luminance are proven as an excellent contour extraction capability. The multiscale particle filter (MSPF) based approach provides a better contour for the object segmentation.

Keywords

Classical receptive field,
Multiscale contour detection,
Multiscale filtering,
Statistical model,
Surround inhibition.

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Cited as

Devshri Swami, Bharti Chaurasia and Yashwant Kurmi,  “A Multiscale Particle Filter and Multi-Feature Based Contour Detection,” International Journal of Advanced Engineering and Management, Vol. 2, No. 6, pp. 127-130,  2017.  DOI: https://doi.org/10.24999/IJOAEM/02060031                                    

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