Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic (SpringerBriefs in Applied Sciences and Technology)
暫譯: 基於廣義型二 fuzzy 邏輯的邊緣檢測方法 (SpringerBriefs in Applied Sciences and Technology)

Claudia I. I. Gonzalez

  • 出版商: Springer
  • 出版日期: 2017-03-14
  • 售價: $2,420
  • 貴賓價: 9.5$2,299
  • 語言: 英文
  • 頁數: 100
  • 裝訂: Paperback
  • ISBN: 3319539930
  • ISBN-13: 9783319539935
  • 海外代購書籍(需單獨結帳)

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商品描述

In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format.

Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preprocessing phase of a face rec-ognition system; where the recognition system is based on a monolithic neural network. The aim of this part of the book is to show the advantage of using a generalized type-2 fuzzy edge detector in pattern recognition applications.

The main goal of using generalized type-2 fuzzy logic in edge detec-tion applications is to provide them with the ability to handle uncertainty in processing real world images; otherwise, to demonstrate that a GT2 FS has a better performance than the edge detection methods based on type-1 and type-2 fuzzy logic systems.

商品描述(中文翻譯)

在本書中提出了四種新方法。第一種方法將廣義型2模糊邏輯與形態梯度技術結合。第二種方法結合了廣義型2模糊系統(GT2 FSs)和索貝爾運算子;第三種方法改進了基於索貝爾運算子和GT2 FSs的方法,以應用於彩色圖像。在第四種方法中,我們提出了一種新穎的邊緣檢測方法,將數位圖像轉換為廣義型2模糊圖像。本書中還包括了一項比較研究,探討型1、區間型2和廣義型2模糊系統作為工具,在與形態梯度和索貝爾運算子結合使用時,如何增強數位圖像的邊緣檢測。所提出的廣義型2模糊邊緣檢測方法在基準圖像和合成圖像上進行了測試,涵蓋了灰階和彩色格式。

本書的另一項貢獻是將廣義型2模糊邊緣檢測方法應用於人臉識別系統的預處理階段;該識別系統基於單體神經網絡。本書這部分的目的是展示在模式識別應用中使用廣義型2模糊邊緣檢測器的優勢。

使用廣義型2模糊邏輯於邊緣檢測應用的主要目標是提供處理現實世界圖像時應對不確定性的能力;否則,展示GT2 FS在性能上優於基於型1和型2模糊邏輯系統的邊緣檢測方法。