English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51946/87076 (60%)
Visitors : 8499148      Online Users : 173
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94520

    Title: 基於PSO模糊分類器於多目標色彩模型的設計
    Other Titles: Pso-based fuzzy classifier in multi-object color models design
    Authors: 林怡女勻;Lin, Yi-Chun
    Contributors: 淡江大學電機工程學系碩士班
    李世安;Li, Shih-An
    Keywords: 色彩分割;模糊系統;粒子群最佳化演算法;全方位視覺;color segmentation;fuzzy system;PSO;Omnidirectional Vision
    Date: 2013
    Issue Date: 2014-01-23 14:44:05 (UTC+8)
    Abstract: 本論文係以符合FIRA (Federation of International Robot-soccer Association) RoboSot規則之中型足球機器人全方位視覺系統為發展平台,研究基於PSO模糊分類器之多目標色彩模型的設計,取代傳統以人工建立多目標色彩模型的方法,提升建立色彩模型之效率,並且改善一般以HSV色彩空間中色相、飽和度與亮度之上下界建立色彩模型容易包含過多不屬於目標色彩範圍的問題,減少機器人誤判目標物的可能。本論文提出解耦合模糊分類器的方法簡化PSO訓練參數之複雜度,一個模糊分類器建立一種目標色彩模型,單一模糊分類器之輸入為像素之三分量-色相、飽和度、亮度,輸出為像素對應模糊規則庫之最大歸屬度與其所屬規則類別,綜合模糊分類器獲得歸屬度最大之所屬類別即為輸出。PSO模糊分類器為監督式學習方法,一般樣本空間以事先在多種環境下取得之多目標樣本組成,而本論文以固定位置作為基本定位,定義感興趣之多目標物範圍後就地取樣,建立適合當時環境之色彩模型。實驗結果呈現本論文提出之建立多目標色彩模型方法較使用六個閥值建立之色彩模型貼近多目標物之色彩模型,且提升人工建立色彩模型之效率。
    The construction of multi-object color models based on PSO-based fuzzy classifier is proposed. This study is developed on omnidirectional vision system of middle-size robots with the competition of FIRA (Federation of International Robot-soccer Association) RoboSot. It replace the old method in order to promote the efficient of construct multi-object color models by manual and improve the problem which misjudge the object because of using six thresholding in HSV color space may include too many colors not belong objective color model. This paper proposed a method to reduce the complexity of training parameter in PSO algorithm with a decouple fuzzy classifier. One fuzzy classifier will construct an objective color model. The input for fuzzy classifier is the consistence in one pixel, hue, saturation, value. The maximum membership value with the color class is output. PSO fuzzy classifier is a supervised learning method. Generally, the patterns of multi-object were obtained in multi-environment beforehand. This paper use fixed location to define the region of interest then get the patterns of multi-object pixels right there. And construct the multi-object models suit of the environment. The experiments show the multi-object color models construct by the proposed method between by manual use six thresholding is much close to the real multi-object color models and much efficiently.
    Appears in Collections:[電機工程學系暨研究所] 學位論文

    Files in This Item:

    There are no files associated with this item.

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback