【正文】 河北工程大學(xué)畢業(yè)設(shè)計（論文） 1 摘要信息論是人們在長期通信實踐活動中，由通信技術(shù)與概率論、隨機過程、數(shù)理統(tǒng)計等學(xué)科相結(jié)合而逐步發(fā)展起來的一門新興交叉學(xué)科。而熵是信息論中事件出現(xiàn)概率的不確定性的量度，能有效反映事件包含的信息。隨著科學(xué)技術(shù)，特別是信息技術(shù)的迅猛發(fā)展，信息理論在通信領(lǐng)域中發(fā)揮了越來越重要的作用，由于信息理論解決問題的思路和方法獨特、新穎和有效，信息論已滲透到其他科學(xué)領(lǐng)域。隨著計算機技術(shù)和數(shù)學(xué)理論的不斷發(fā)展，人工智能、神經(jīng)網(wǎng)絡(luò)、遺傳算法、模糊理論的不斷完善，信息理論的應(yīng)用越來越廣泛。在圖像處理研究中，信息熵也越來越受到關(guān)注。為了尋找快速有效的圖像處理方法，信息理論越來越多地滲透到圖像處理技術(shù)中。本文通過進一步探討概論率中熵的概念，分析其在圖像處理中的應(yīng)用，通過概念的分析理解，詳細(xì)討論其在圖像處理的各個方面：如圖像分割、圖像配準(zhǔn)、人臉識別，特征檢測等的應(yīng)用。 本文介紹了信息熵在圖像處理中的應(yīng)用，總結(jié)了一些基于熵的基本概念，互信息的定義。并給出了信息熵在圖像處理特別是圖像分割和圖像配準(zhǔn)中的應(yīng)用，最后實現(xiàn)了信息熵在圖像配準(zhǔn)中的方法。關(guān)鍵詞：信息熵，互信息，圖像分割，圖像配準(zhǔn)河北工程大學(xué)畢業(yè)設(shè)計（論文） 2 Abstract Information theory is a new interdisciplinary subject developed in people longterm munication practice, bining with munication technology, theory of probability, stochastic processes, and mathematical statistics. Entropy is a measure of the uncertainty the probability of the occurrence of the event in the information theory, it can effectively reflect the information event contains. With the development of science and technology, especially the rapid development of information technology, information theory has played a more and more important role in the munication field, because the ideas and methods to solve the problem of information theory is unique, novel and effective, information theory has perated into other areas of science. With the development of puter technology and mathematical theory, continuous improvement of artificial intelligence, neural work, geic algorithm, fuzzy theory, there are more and more extensive applications of information theory. In the research of image processing, the information entropy has attracted more and more attention. In order to find the fast and effective image processing method, information theory is used more and more frequently in the image processing technology. In this paper, through the further discussion on concept of entropy, analyzes its application in image processing, such as image segmentation, image registration, face recognition, feature detection etc.This paper introduces the application of information entropy in image processing, summarizes some basic concepts based on the definition of entropy, mutual information. And the information entropy of image processing especially for image segmentation and image registration. Finally realize the information entropy in image registration.Keywords： Information entropy, Mutual information, Image segmentation,Image registration河北工程大學(xué)畢業(yè)設(shè)計（論文） 3 目 錄摘 要......................................................................................................................................1ABSTRACT...............................................................................................................................2目 錄..................................................................................................................................31 引言........................................................................................................................................5 信息熵的概念 .................................................................................................................5 信息熵的基本性質(zhì)及證明 .............................................................................................6 單峰性.......................................................................................................................6 對稱性.......................................................................................................................7 漸化性.......................................................................................................................7 展開性.......................................................................................................................7 確定性.......................................................................................................................82 基于熵的互信息理論 ............................................................................................................9 互信息的概述.................................................................................................................9 互信息的定義.................................................................................................................9 熵與互信息的關(guān)系.........................................................................................................93 信息熵在圖像分割中的應(yīng)用..............................................................................................11 圖像分割的基本概念 ..................................................................................................11 圖像分割的研究現(xiàn)狀 .............................................................................................11 圖像分割的方法.....................................................................................................11 基于改進粒子群優(yōu)化的模糊熵煤塵圖像分割 ............................................................12 基本粒子群算法.....................................................................................................12 改進粒子群優(yōu)化算法.............................................................................................13 Morlet 變異 ..............................................................................................................13 改建粒子群優(yōu)化的圖像分割方法..........................................................................14 實驗結(jié)果及分析.....................................................................................................16 一種新信息熵的定義及其在圖像分割中的應(yīng)用 .......................................................19 香農(nóng)熵的概念及性質(zhì).............................................................................................19 一種信息熵的定義及證明.....................................................................................19 信息熵計算復(fù)雜性分析.........................................................................................21 二維信息熵閾值法.................................................................................................22