本文選題：皮膚檢測　切入點：卷積變換　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：皮膚檢測技術(shù)是眾多人體模式識別的基礎(chǔ)研究課題之一,它在多個領(lǐng)域中表現(xiàn)出較高的應(yīng)用價值,例如醫(yī)學(xué)診斷、Web圖像過濾、手勢識別、面部表情識別等。雖然目前存在眾多的皮膚檢測算法,但其檢測效果仍然存在提高的空間。本文對皮膚檢測中的預(yù)處理階段、判決階段及后期處理階段進行深入研究,并在此基礎(chǔ)上提出改進算法。主要內(nèi)容及創(chuàng)新點可以概括如下:(1)拍攝過程中光照分布的不均勻性易產(chǎn)生圖像像素點之間灰度值的差異,為皮膚檢測過程增加了難度。本文提出在皮膚檢測預(yù)處理階段引用改進的Z-score標準化與卷積變換相結(jié)合的方法來降低光照不均勻的影響,仿真實驗結(jié)果表明本文算法不僅能夠?qū)崿F(xiàn)對灰度值較低或者灰度值較高的補償,而且能將整幅圖像各像素點的灰度值基本補償?shù)?6-175之間,新的補償方法提升了皮膚檢測效果。(2)傳統(tǒng)的皮膚檢測方法是通過首先建立皮膚像素點的底層特征信息模型然后驗證待檢圖像像素點是否屬于特征信息范圍來實現(xiàn)檢測的,然而此類檢測方法僅僅考慮到皮膚像素點的基礎(chǔ)特征信息(如YCbCr顏色空間特征),并沒有建立更深層次的皮膚像素點特征,在檢測的過程中必然存在一定的局限性。本文提出在皮膚檢測技術(shù)的判決階段,使用深度學(xué)習(xí)算法建立深層次皮膚像素點特征,并使用待檢圖像與深層次皮膚像素點特征對比的方式來實現(xiàn)皮膚檢測,仿真實驗結(jié)果表明相比其他皮膚檢測算法,本文算法在皮膚檢測過程中不僅提高了正檢率而且降低了誤檢率,具有較好的性能。(3)在復(fù)雜背景圖像中,近似皮膚區(qū)域像素點會對皮膚檢測算法造成較大干擾。本文提出在皮膚檢測技術(shù)的后期處理階段通過使用密度峰值聚類分析算法來減弱近似皮膚區(qū)域給皮膚檢測技術(shù)帶來的干擾,仿真實驗結(jié)果表明本文算法在增強皮膚檢測技術(shù)方面具有積極的影響,不僅減弱了近似皮膚像素點的干擾而且提高了皮膚的檢出率。
[Abstract]:Skin detection technology is one of the basic research topics of human pattern recognition. It has high application value in many fields, such as medical diagnosis web image filtering, gesture recognition, etc. Facial expression recognition and so on. Although there are many skin detection algorithms at present, there is still room for improvement in the detection effect. In this paper, the preprocessing stage, the judgment stage and the post-processing stage of skin detection are deeply studied. On this basis, the improved algorithm is put forward. The main contents and innovations can be summarized as follows: (1) the inhomogeneity of light distribution in the shooting process can easily result in the difference of gray values between pixels in the image. In this paper, an improved Z-score standardization and convolution transformation method is proposed to reduce the effect of uneven illumination in the skin detection preprocessing stage. The simulation results show that the algorithm can not only compensate for the lower or higher gray value, but also can compensate the gray value of every pixel in the whole image to 76-175. The new compensation method improves the effectiveness of skin detection. (2) the traditional skin detection method realizes the detection by first establishing the underlying feature information model of skin pixels and then verifying whether the pixel points of the image under inspection belong to the range of feature information. However, this detection method only takes into account the basic feature information of skin pixels (such as YCbCr color space features), and does not establish deeper skin pixel features. There must be some limitations in the process of detection. In the judgment stage of skin detection technology, the depth learning algorithm is used to establish the pixel feature of deep skin. The method of contrast between the image to be detected and the pixel feature of the deep skin is used to realize the skin detection. The simulation results show that compared with other skin detection algorithms, In the process of skin detection, the algorithm not only improves the positive detection rate but also reduces the false detection rate, and has good performance in complex background images. Pixel points in the approximate skin region will cause great interference to the skin detection algorithm. In this paper, we propose to reduce the approximate skin area by using the peak density cluster analysis algorithm in the post-processing phase of the skin detection technology. Interference from technology, The simulation results show that the proposed algorithm has a positive effect on the enhancement of skin detection technology, which not only weakens the interference of similar skin pixels, but also improves the detection rate of skin.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 劉穎瑩;劉培玉;王智昊;李情情;朱振方;;一種基于密度峰值發(fā)現(xiàn)的文本聚類算法[J];山東大學(xué)學(xué)報(理學(xué)版);2016年01期

2 崔更申;楊飛;黃廷輝;;基于相似度的光照魯棒性膚色檢測[J];計算機工程與設(shè)計;2014年12期

3 趙杰;桑慶兵;劉毅錕;;基于分裂式K均值聚類的膚色檢測方法[J];計算機工程與應(yīng)用;2014年01期

4 方帥;楊靜榮;曹洋;武鵬飛;饒瑞中;;圖像引導(dǎo)濾波的局部多尺度Retinex算法[J];中國圖象圖形學(xué)報;2012年07期

5 張家怡;;圖像識別的技術(shù)現(xiàn)狀和發(fā)展趨勢[J];電腦知識與技術(shù);2010年21期

6 胡敏;李梅;汪榮貴;;改進的Otsu算法在圖像分割中的應(yīng)用[J];電子測量與儀器學(xué)報;2010年05期

7 梁琳;何衛(wèi)平;雷蕾;張維;王紅霄;;光照不均圖像增強方法綜述[J];計算機應(yīng)用研究;2010年05期

8 陳鍛生;劉政凱;;膚色檢測技術(shù)綜述[J];計算機學(xué)報;2006年02期

9 靳簡明;江紅英;王慶人;;數(shù)學(xué)公式圖像處理綜述[J];模式識別與人工智能;2005年04期

10 張新明,沈蘭蓀;基于小波的同態(tài)濾波器用于圖像對比度增強[J];電子學(xué)報;2001年04期

相關(guān)博士學(xué)位論文 前3條

1 肖宇;聚類分析及其在圖像處理中的應(yīng)用[D];北京交通大學(xué);2012年

2 魏偉一;非均勻光照圖像的灰度校正與分割技術(shù)研究[D];蘭州理工大學(xué);2011年

3 尚曉清;多尺度分析在圖像處理中的應(yīng)用研究[D];西安電子科技大學(xué);2004年

相關(guān)碩士學(xué)位論文 前2條

1 王丹妹;基于卷積變換的圖像匹配方法研究[D];重慶交通大學(xué);2015年

2 馬冬梅;基于深度學(xué)習(xí)的圖像檢索研究[D];內(nèi)蒙古大學(xué);2014年

，

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