本文選題：圖像處理　切入點(diǎn)：邊緣檢測(cè)　出處：《長(zhǎng)春工業(yè)大學(xué)》2017年碩士論文

【摘要】：邊緣檢測(cè)作為數(shù)字圖像處理中一種常用的處理方法,是很多圖像處理技術(shù)的基礎(chǔ)。圖像的邊緣包含了大量的圖像特征信息,可用于特征描述、圖像增強(qiáng)、圖像分割、模式識(shí)別等圖像分析和處理中。因此圖像邊緣檢測(cè)算法一直是圖像處理和計(jì)算機(jī)視覺(jué)領(lǐng)域的研究熱點(diǎn),期望尋找一種檢測(cè)精度和抗噪能力都令人滿意的算法。面癱是一種人臉面部神經(jīng)運(yùn)動(dòng)功能受損的疾病,它給人的身心都帶來(lái)極大的傷害。但現(xiàn)在醫(yī)學(xué)對(duì)面癱的判定大都基于主觀判斷,沒(méi)有一個(gè)統(tǒng)一的評(píng)價(jià)標(biāo)準(zhǔn)。利用邊緣檢測(cè)算法建立面癱識(shí)別系統(tǒng),準(zhǔn)確判定面癱程度,對(duì)于提早發(fā)現(xiàn)疑似病例、指導(dǎo)臨床用藥、評(píng)估治療效果以及學(xué)術(shù)領(lǐng)域交流等具有重要的意義。本文利用邊緣檢測(cè)算法對(duì)面癱圖像進(jìn)行了處理,期望建立一個(gè)較為標(biāo)準(zhǔn)的面癱識(shí)別系統(tǒng),從而實(shí)現(xiàn)對(duì)面癱圖像的識(shí)別與分級(jí)。本文主要完成改進(jìn)邊緣檢測(cè)算法和建立面癱識(shí)別系統(tǒng)兩方面的工作。本文首先對(duì)一些傳統(tǒng)的邊緣檢測(cè)算法進(jìn)行了分析與研究,并介紹了三種新興的邊緣檢測(cè)方法,在對(duì)這些方法歸納研究的基礎(chǔ)上,提出了一種改進(jìn)的邊緣檢測(cè)算法。通過(guò)增加模板方向來(lái)改進(jìn)Sobel算法,再利用遺傳算法改進(jìn)Canny算法的閾值選取過(guò)程,然后采用小波變換的方法對(duì)兩種改進(jìn)的算法進(jìn)行圖像融合處理,結(jié)合兩種算法的優(yōu)點(diǎn),得到改進(jìn)的邊緣檢測(cè)算法。然后,針對(duì)面癱圖像特征的位置信息,設(shè)計(jì)了一種面部圖像采集系統(tǒng),獲取含有區(qū)域分塊的人臉圖像。在建立面癱識(shí)別系統(tǒng)時(shí),利用改進(jìn)的邊緣檢測(cè)算法對(duì)面癱圖像進(jìn)行處理以提取圖像邊緣特征信息。根據(jù)面癱患者面部不對(duì)稱的性質(zhì),計(jì)算特征信息矩陣之間的差值,提出一種基于對(duì)稱軸的面癱分級(jí)方法。再將其與基于距離的面癱評(píng)分方法相結(jié)合,完成對(duì)圖像最終的等級(jí)劃分,建立面癱識(shí)別系統(tǒng),實(shí)現(xiàn)對(duì)面癱圖像的識(shí)別與分級(jí)。
[Abstract]:Edge detection, as a common processing method in digital image processing, is the basis of many image processing techniques.The edge of the image contains a large amount of image feature information, which can be used in image analysis and processing such as feature description, image enhancement, image segmentation, pattern recognition and so on.Therefore, image edge detection algorithm has been a hot topic in the field of image processing and computer vision. It is expected to find an algorithm with satisfactory detection accuracy and anti-noise ability.Facial paralysis is a kind of facial nerve function damage disease, it brings great harm to the body and mind.But now the medical judgment of facial paralysis is mostly based on subjective judgment, without a unified evaluation standard.Using edge detection algorithm to establish facial paralysis recognition system and accurately determine the degree of facial paralysis is of great significance for the early detection of suspected cases, the guidance of clinical medication, the evaluation of therapeutic effect and the exchange of academic fields.In this paper, the edge detection algorithm is used to deal with the facial paralysis image, and it is expected to establish a standard facial paralysis recognition system, so as to realize the recognition and classification of the facial paralysis image.In this paper, the improvement of edge detection algorithm and the establishment of facial paralysis recognition system are mainly completed.In this paper, some traditional edge detection algorithms are analyzed and studied, and three new edge detection methods are introduced. Based on the research of these methods, an improved edge detection algorithm is proposed.The Sobel algorithm is improved by adding the template direction, and the threshold selection process of the Canny algorithm is improved by genetic algorithm. Then the image fusion of the two improved algorithms is carried out by wavelet transform, which combines the advantages of the two algorithms.An improved edge detection algorithm is presented.Then, according to the position information of facial paralysis image feature, a facial image acquisition system is designed to obtain the face image with regional block.In order to extract the edge feature information of facial paralysis image, the improved edge detection algorithm is used to process the facial paralysis image.According to the character of facial asymmetry in facial paralysis patients, the difference between characteristic information matrix is calculated, and a method of facial paralysis classification based on symmetry axis is proposed.Then combining it with the distance based facial paralysis scoring method, the final classification of the image is completed, and the recognition system of facial paralysis is established to realize the recognition and classification of the facial paralysis image.
【學(xué)位授予單位】：長(zhǎng)春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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