本文選題：手勢識別 + Otsu分割算法　； 參考：《沈陽理工大學(xué)》2017年碩士論文

【摘要】：人與計算機的頻繁互動已經(jīng)成為生活中的日常操作,其中,關(guān)于手勢的研究已然成為目前人機交互研究領(lǐng)域的主要研究方向之一。手勢識別技術(shù)的研究將會改變傳統(tǒng)的人機交互方式,手勢的的使用必然會使得人機交互技術(shù)從以機器為中心逐步轉(zhuǎn)移到以人為中心,讓人機交互的方式變得便捷,人機交互方式變得豐富,也使使用計算機的門檻降低。本文對手勢識別完整系統(tǒng)進行表述,主要包含了四個主要部分,靜態(tài)手勢圖像的預(yù)處理,關(guān)于手勢的圖像分割,手勢的特征提取以及最后的手勢的識別方法。系統(tǒng)通過攝像頭捕獲手勢圖像,對該圖像進行預(yù)處理,其中包括彩色空間轉(zhuǎn)換、平滑處理、形態(tài)學(xué)運算、灰度化、二值化、輪廓提取,其中詳細介紹通過常用的顏色空間,分析影響到手勢特征提取及分割的色彩分量,并通過彩色空間轉(zhuǎn)換減弱甚至消除該影響。本文詳細介紹基于canny邊緣的檢測方法,并根據(jù)在手勢邊緣提取方法上的不足提出改進。手勢分割部分是手勢識別系統(tǒng)的關(guān)鍵步驟之一,在簡單、單一背景的室內(nèi)環(huán)境下分割手勢的算法要求不高,但是在復(fù)雜背景下的室外環(huán)境下,有太多的干擾,這使得傳統(tǒng)的分割方法無法將手勢從背景中干凈的分割出來,本文介紹的傳統(tǒng)的Otsu算法雖然在單一背景下效果不錯,但是復(fù)雜背景下顯得捉襟見肘,通過改進灰度圖像的劃分方法使的Otsu算法能夠分割出手勢。干凈的手勢圖像中的信息量太多,如果作為分類識別系統(tǒng)的輸入,這會增加識別系統(tǒng)的計算量以及計算復(fù)雜度,所以手勢圖像的特征提取是需要的,本文使用的是圖像區(qū)域幾何的特征的不變距,不變距由7個不變距的值組成,我們?yōu)榱耸棺R別系統(tǒng)分類的輸入具有旋轉(zhuǎn)、平移、尺度變化不變性,就需要通過仿真并且比較從中挑選出符合條件的分量并組合成輸入向量。在識別方法的挑選中,本文挑選的基于自適應(yīng)神經(jīng)-模糊推理系統(tǒng)(ANFIS)的手勢識別方法具有自主學(xué)習(xí)的能力,而且魯棒性好。雖然該方法的識別能力好但是計算復(fù)雜度高,我們通過對不變距的篩選結(jié)合自適應(yīng)神經(jīng)-模糊推理系統(tǒng)的手勢識別法,提高整個系統(tǒng)的手勢識別率,并且與BP神經(jīng)網(wǎng)絡(luò)和模糊神經(jīng)網(wǎng)絡(luò)進行,平均識別率95.3%說明自適應(yīng)神經(jīng)-模糊推理系統(tǒng)在識別率方面的效果,符合高識別率的實際準則。
[Abstract]:The frequent interaction between human and computer has become a daily operation in daily life, among which, the research on gesture has become one of the main research directions in the field of human-computer interaction. The research of gesture recognition technology will change the traditional human-computer interaction mode. The use of gesture will inevitably make the human-computer interaction technology shift from machine center to human-centered, so that the human-computer interaction becomes convenient. The man-machine interaction way becomes rich, also causes the computer to use the threshold to lower. This paper describes the complete system of gesture recognition, which includes four main parts: the preprocessing of static gesture image, the segmentation of gesture image, the feature extraction of gesture and the final gesture recognition method. The system captures the gesture image through the camera and preprocesses the image, including color space conversion, smoothing processing, morphological operation, grayscale, binarization, contour extraction, in which the commonly used color space is introduced in detail. The color components which affect gesture feature extraction and segmentation are analyzed, and the influence is weakened or even eliminated by color space conversion. This paper introduces the method of edge detection based on canny in detail, and proposes some improvements according to the shortcomings of the method of gesture edge detection. Gesture segmentation is one of the key steps of gesture recognition system. The algorithm of hand gesture segmentation in simple, single background indoor environment is not high, but in the outdoor environment of complex background, there is too much interference. This makes it impossible for traditional segmentation methods to segment gestures from the background cleanly. Although the traditional Otsu algorithm introduced in this paper has a good effect in a single background, it appears to be overstretched in a complex background. By improving the grayscale image partition method, the Otsu algorithm can segment the gesture. There is too much information in a clean gesture image. If it is used as the input of the classification recognition system, it will increase the computation and complexity of the recognition system, so the feature extraction of the gesture image is needed. In this paper, we use the invariant distance of the geometric features of the image region. The invariance is composed of seven invariant values. In order to make the input of the classification of the recognition system have the invariance of rotation, translation and scale change. It is necessary to select the suitable components and combine them into input vectors by simulation and comparison. In the selection of recognition methods, the gesture recognition method selected in this paper based on adaptive neural fuzzy inference system (ANFIS) has the ability of autonomous learning and good robustness. Although this method has good recognition ability and high computational complexity, we improve the gesture recognition rate of the whole system by selecting invariant distance and combining with the gesture recognition method of adaptive neural fuzzy inference system. Compared with BP neural network and fuzzy neural network, the average recognition rate is 95.3%, which shows that the adaptive neural fuzzy inference system is effective in recognition rate and accords with the practical criterion of high recognition rate.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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