Analyzing the Efficiency of Segment Boundary Detection Using Neural Networks

被引：0
|
作者：
A. V. Kugaevskikh
A. A. Sogreshilin
机构：
[1] Novosibirsk State Technical University,Institute of Automation and Electrometry, Siberian Branch
[2] Novosibirsk State University,undefined
[3] Russian Academy of Sciences,undefined
来源：
Optoelectronics, Instrumentation and Data Processing | 2019年 / 55卷
关键词：
edge selection; Gabor filter; cosine measure; neural networks; Mexican hat wavelet; hyperbolic tangent;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
—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.
引用
收藏
页码：414 / 422
页数：8
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