彩色影像系統中色彩分類方法之研究

Abstract

人類的眼睛能根據色彩的不同而分類出不同的物體。本研究即在於產生一色彩分類系統以應用於自行設計之即時彩色影像系統中。希望能依據色彩的不同而將目標與背景加以分類。本研究所完成之系統能針對多色彩目標加以分類。此一系統的完成將可彌補傳統灰階影像不足之處。 本論文配合各種色彩模型，提出以模糊理論與倒傳遞類神經網路為色彩分類的方法。在模糊色彩分類系統中，我們提出動態模糊空間分割的觀念。在倒傳擄類神經網路色彩分類系統中，我們則設計了單、雙隱藏層網路，並使用適應性學習速率。本研究成功地在32位元作業系統下開發一套色彩分類系統。論文並針對影響系統效能與分類結果的各項參數進行許多實驗測試，所有實驗皆在我們研製之系統上完成。 本論文提出以色彩做影像分類，提供了影像處理上一個新的思考方向。我們仍須強調這僅是一個開端，新的色彩分類法則仍有待開發以強化分類效能和精確度。

Human eye can classify different objects by way of their colors. The purpose of this research is to develop a color classification system in order to use in a self-made real time color image system. We hope to classify the target and background using their colors. The final system we made can process any object coated with multiple colors. Success of this research can also improve the defects or insufficiency of traditional gray image system. Fuzzy theory and back propagation neural network in conjunction with several typical color models to classify different colors were proposed. We proposed dynamic fuzzy input variable space partitioning in the fuzzy color classification system. We used single also double hidden layer neural network with adaptive learning rate in the back propagation neural network color classification system. A complete color classification system working under 32 bit operating system have been developed successfully. Parameters that will affect the performance of color classification were examined in this article. All experiments were finished under our self-made system. This research proposed image classification using colors. This is a new direction in color image processing. We still emphasized that this is just a beginning and new classification alogrithms should be found out to improve the performance or accuracy of color classification.