Edge detection based on kernel density estimation

O. Pereira; E. Torres; Y. Garcés; R. Rodríguez

Edge detection based on kernel density estimation

O. Pereira, E. Torres, Y. Garcés, R. Rodríguez

Artificial Intelligence and Facial Recognition Research Group (GIIAR)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the probability density function. In our algorithm, pixels in the image with minimum value of density function are labeled as edges. The boundary between two homogeneous regions is defined in two domains: the spatial/lattice domain and the range/color domain. Extensive experimental evaluations proved that our edge detection method is significantly a competitive algorithm.

Original language	English
Title of host publication	Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017
Editors	Hamid R. Arabnia, Leonidas Deligiannidis, Fernando G. Tinetti
Publisher	CSREA Press
Pages	123-128
Number of pages	6
ISBN (Electronic)	1601324642, 9781601324641
State	Published - 2017
Event	2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 - Las Vegas, United States Duration: 17 Jul 2017 → 20 Jul 2017

Publication series

Name	Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017

Conference

Conference	2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017
Country/Territory	United States
City	Las Vegas
Period	17/07/17 → 20/07/17

Bibliographical note

Publisher Copyright:
CSREA Press ©

Keywords

Edge Detection
Kernel Density Estimation
Probability Density Function

Cite this

Pereira, O., Torres, E., Garcés, Y., & Rodríguez, R. (2017). Edge detection based on kernel density estimation. In H. R. Arabnia, L. Deligiannidis, & F. G. Tinetti (Eds.), Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 (pp. 123-128). (Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017). CSREA Press.

Pereira, O. ; Torres, E. ; Garcés, Y. et al. / Edge detection based on kernel density estimation. Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017. editor / Hamid R. Arabnia ; Leonidas Deligiannidis ; Fernando G. Tinetti. CSREA Press, 2017. pp. 123-128 (Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017).

@inproceedings{fc01608c78cd4acd8057c474ce45da32,

title = "Edge detection based on kernel density estimation",

abstract = "Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the probability density function. In our algorithm, pixels in the image with minimum value of density function are labeled as edges. The boundary between two homogeneous regions is defined in two domains: the spatial/lattice domain and the range/color domain. Extensive experimental evaluations proved that our edge detection method is significantly a competitive algorithm.",

keywords = "Edge Detection, Kernel Density Estimation, Probability Density Function",

author = "O. Pereira and E. Torres and Y. Garc{\'e}s and R. Rodr{\'i}guez",

note = "Publisher Copyright: CSREA Press {\textcopyright}; 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017 ; Conference date: 17-07-2017 Through 20-07-2017",

year = "2017",

language = "English",

series = "Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017",

publisher = "CSREA Press",

pages = "123--128",

editor = "Arabnia, {Hamid R.} and Leonidas Deligiannidis and Tinetti, {Fernando G.}",

booktitle = "Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017",

}

Pereira, O, Torres, E, Garcés, Y & Rodríguez, R 2017, Edge detection based on kernel density estimation. in HR Arabnia, L Deligiannidis & FG Tinetti (eds), Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017. Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017, CSREA Press, pp. 123-128, 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017, Las Vegas, United States, 17/07/17.

Edge detection based on kernel density estimation. / Pereira, O.; Torres, E.; Garcés, Y. et al.
Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017. ed. / Hamid R. Arabnia; Leonidas Deligiannidis; Fernando G. Tinetti. CSREA Press, 2017. p. 123-128 (Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Edge detection based on kernel density estimation

AU - Pereira, O.

AU - Torres, E.

AU - Garcés, Y.

AU - Rodríguez, R.

N1 - Publisher Copyright: CSREA Press ©

PY - 2017

Y1 - 2017

N2 - Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the probability density function. In our algorithm, pixels in the image with minimum value of density function are labeled as edges. The boundary between two homogeneous regions is defined in two domains: the spatial/lattice domain and the range/color domain. Extensive experimental evaluations proved that our edge detection method is significantly a competitive algorithm.

AB - Edges of an image are considered a crucial type of information. These can be extracted by applying edge detectors with different methodology. Edge detection is a vital step in computer vision tasks, because it is an essential issue for pattern recognition and visual interpretation. In this paper, we propose a new method for edge detection in images, based on the estimation by kernel of the probability density function. In our algorithm, pixels in the image with minimum value of density function are labeled as edges. The boundary between two homogeneous regions is defined in two domains: the spatial/lattice domain and the range/color domain. Extensive experimental evaluations proved that our edge detection method is significantly a competitive algorithm.

KW - Edge Detection

KW - Kernel Density Estimation

KW - Probability Density Function

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M3 - Conference contribution

AN - SCOPUS:85034084184

T3 - Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017

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BT - Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017

A2 - Arabnia, Hamid R.

A2 - Deligiannidis, Leonidas

A2 - Tinetti, Fernando G.

PB - CSREA Press

T2 - 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017

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ER -

Pereira O, Torres E, Garcés Y, Rodríguez R. Edge detection based on kernel density estimation. In Arabnia HR, Deligiannidis L, Tinetti FG, editors, Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017. CSREA Press. 2017. p. 123-128. (Proceedings of the 2017 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2017).

Edge detection based on kernel density estimation

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Keywords

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