Standard

Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks. / Kugaevskikh, A. V.; Sogreshilin, A. A.

In: Optoelectronics, Instrumentation and Data Processing, Vol. 55, No. 4, 01.07.2019, p. 414-422.

Research output: Contribution to journal › Article › peer-review

Harvard

Kugaevskikh, AV & Sogreshilin, AA 2019, 'Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks', Optoelectronics, Instrumentation and Data Processing, vol. 55, no. 4, pp. 414-422. https://doi.org/10.3103/S8756699019040137

APA

Kugaevskikh, A. V., & Sogreshilin, A. A. (2019). Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks. Optoelectronics, Instrumentation and Data Processing, 55(4), 414-422. https://doi.org/10.3103/S8756699019040137

Vancouver

Kugaevskikh AV, Sogreshilin AA. Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks. Optoelectronics, Instrumentation and Data Processing. 2019 Jul 1;55(4):414-422. doi: 10.3103/S8756699019040137

Author

Kugaevskikh, A. V. ; Sogreshilin, A. A. / Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks. In: Optoelectronics, Instrumentation and Data Processing. 2019 ; Vol. 55, No. 4. pp. 414-422.

BibTeX

@article{e3a2d16b7e9945fabb78cc06dc0158c8,
title = "Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks",
abstract = "This paper describes the architecture of a neural network for edge detection. Different filters for first-layer neurons are compared. Neural network learning based on a cosine measure algorithm shows much worse results than an error backpropagation algorithm. Optimal parameters for the first-layer neuron operation are given. The proposed architecture fulfills the stated tasks on edge selection.",
keywords = "cosine measure, edge selection, Gabor filter, hyperbolic tangent, Mexican hat wavelet, neural networks",
author = "Kugaevskikh, {A. V.} and Sogreshilin, {A. A.}",
year = "2019",
month = jul,
day = "1",
doi = "10.3103/S8756699019040137",
language = "English",
volume = "55",
pages = "414--422",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "4",

}

RIS

TY - JOUR

T1 - Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks

AU - Kugaevskikh, A. V.

AU - Sogreshilin, A. A.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - This paper describes the architecture of a neural network for edge detection. Different filters for first-layer neurons are compared. Neural network learning based on a cosine measure algorithm shows much worse results than an error backpropagation algorithm. Optimal parameters for the first-layer neuron operation are given. The proposed architecture fulfills the stated tasks on edge selection.

AB - This paper describes the architecture of a neural network for edge detection. Different filters for first-layer neurons are compared. Neural network learning based on a cosine measure algorithm shows much worse results than an error backpropagation algorithm. Optimal parameters for the first-layer neuron operation are given. The proposed architecture fulfills the stated tasks on edge selection.

KW - cosine measure

KW - edge selection

KW - Gabor filter

KW - hyperbolic tangent

KW - Mexican hat wavelet

KW - neural networks

UR - http://www.scopus.com/inward/record.url?scp=85073225128&partnerID=8YFLogxK

U2 - 10.3103/S8756699019040137

DO - 10.3103/S8756699019040137

M3 - Article

AN - SCOPUS:85073225128

VL - 55

SP - 414

EP - 422

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 4

ER -

ID: 21861318