本文關(guān)鍵詞：基于面象特征的中醫(yī)體質(zhì)自動辨識系統(tǒng)研究　出處：《北京工業(yè)大學》2016年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 中醫(yī)面診 體質(zhì)辨識 特征點定位 支持向量機

【摘要】：中醫(yī)學認為人體是一個有機整體,需要根據(jù)個體體質(zhì)進行養(yǎng)生保健和疾病防治。所以,面部形態(tài)、神色的變化與身體體質(zhì)有著某種程度上的聯(lián)系。目前的面診研究主要集中于面色與臟腑病癥或其他具體病癥之間的關(guān)系研究。本論文通過拍攝受檢者的面部圖像,利用數(shù)字圖像處理、模式識別等技術(shù)初步完成了中醫(yī)面診的自動分析研究,并在此基礎上,完成了一個體質(zhì)辨識的輔助系統(tǒng)。該系統(tǒng)為體質(zhì)辨識提供更多的理論依據(jù),對中醫(yī)四診客觀化的發(fā)展和中醫(yī)體質(zhì)辨識有重要的現(xiàn)實意義。具體來說,本論文主要完成以下幾方面的工作:1.完成了對采集到的人體面部圖像的預處理工作。在面象采集過程中,由于人體姿勢或拍攝角度等因素的影響,我們采集到的人體面部圖像不統(tǒng)一。為了排除其他不穩(wěn)定因素的干擾,減小誤差,得到統(tǒng)一的人體面部圖像,有必要對采集的人體面部圖像進行預處理工作。本論文利用基于YCb Cr顏色空間的橢圓模型進行膚色檢測,并對檢測結(jié)果進行去噪處理,進而利用基于面部矩形特征的方法進行人臉定位,最終得到圖像中人臉的具體位置。2.提取與中醫(yī)體質(zhì)類型相關(guān)的面象顏色特征和紋理特征。對面部額頭、鼻子、臉頰進行子圖定位,然后對其顏色特征和紋理特征進行提取,將人臉上的特征轉(zhuǎn)化為數(shù)字形式的向量。在顏色特征提取時,將圖像按非線性轉(zhuǎn)換公式由RGB顏色空間轉(zhuǎn)換到HSV顏色空間,然后提取面部子圖的H、S、V三個分量作為面象的顏色特征;對于紋理特征,利用灰度共生矩陣的四個分量表征紋理特征。3.首次對面象特征與中醫(yī)體質(zhì)的關(guān)系進行了客觀化的研究。在圖像預處理和特征提取的工作基礎上,采用SVM對面象特征進行分類學習,分別使用網(wǎng)格遍歷法和粒子群法對SVM中核參數(shù)?和懲罰因子C進行尋優(yōu),并結(jié)合交叉驗證法得到最優(yōu)參數(shù),進而對基于面象特征的中醫(yī)體質(zhì)進行分類研究,探索了面象特征與中醫(yī)體質(zhì)的客觀關(guān)系。4.開發(fā)了基于面象特征的中醫(yī)體質(zhì)自動辨識系統(tǒng)。在以上研究的基礎上,搭建全生命周期健康管理平臺,設計開發(fā)面象自動分析模塊,對中醫(yī)體質(zhì)進行自動辨識,對中醫(yī)全面健康體檢提供新的依據(jù),也為基于面象特征的中醫(yī)體質(zhì)辨識提供了一個新的思路。
[Abstract]:Chinese medicine believes that the human body is an organic whole, according to the individual health care and health care and disease prevention and treatment. Therefore, facial morphology. There is a certain relationship between the changes of facial appearance and physical fitness. The current study of facial examination mainly focuses on the relationship between facial color and viscera disorders or other specific diseases. This paper takes the face images of the subjects. . Using digital image processing, pattern recognition and other technologies, we have preliminarily completed the automatic analysis of traditional Chinese medicine surface diagnosis, and on this basis. The system provides more theoretical basis for physique identification, and has important practical significance for the development of TCM four diagnosis objectification and TCM physique identification. The main work of this thesis is as follows: 1. The preprocessing of the collected human face image is completed. In the process of face image acquisition, due to the human posture or shooting angle and other factors. In order to eliminate the interference of other unstable factors and reduce the error, we can get the unified human face image. It is necessary to preprocess the collected human face image. In this paper, we use the elliptical model based on YCb Cr color space to detect the skin color, and de-noising the detection results. Then the face location based on the face rectangular feature is used to get the specific position of the face in the image. The color and texture features of the face image are extracted which are related to the physique type of traditional Chinese medicine. The nose and cheek are located by subgraph, then the color feature and texture feature are extracted, and the feature of human face is transformed into a vector in the form of number. When the color feature is extracted, the color feature is extracted. The image is transformed from RGB color space to HSV color space according to the nonlinear transformation formula, and then three components of the face image are extracted as the color features of the face image. For texture features. Using four components of gray level co-occurrence matrix to characterize texture feature. 3. The relationship between the first image feature and TCM physique is studied objectively. Based on the work of image preprocessing and feature extraction. The SVM image features are used for classification and the mesh ergodic method and particle swarm optimization method are used to study the kernel parameters in SVM. And penalty factor C to optimize, and combined with cross-validation method to obtain the optimal parameters, and then based on the features of traditional Chinese medicine physique classification research. Explore the objective relationship between facial features and TCM physique. 4. Develop an automatic identification system of TCM physique based on facet features. On the basis of the above research, build the whole life cycle health management platform. The design and development of automatic surface image analysis module for automatic identification of TCM physique provides a new basis for comprehensive physical examination of TCM and also provides a new idea for TCM physique identification based on facial features.
【學位授予單位】：北京工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R241;TP391.41

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