本文選題：單目視覺 + 膚色分割�。� 參考：《廣東技術師范學院》2017年碩士論文

【摘要】：近年來,隨著計算機領域技術發(fā)展迅猛,更加自然、高效的新型人機交互方式不斷涌現。手勢是人類的基本溝通方式之一,其符合人類的日常交流習慣。基于單目視覺技術,通過手勢識別實現更符合人類交流習慣的人機交互,已成為人機交互領域的研究熱點。目前,雖有不少手勢膚色分割和手勢識別算法被提出,但現有的算法在識別率、執(zhí)行效率、以及實用性等方面仍然存在不足,有待改進。比如,大多數靜態(tài)手勢識別算法的復雜度高,而且在復雜背景或光照條件差的環(huán)境下難以獲得理想的手勢分割效果,進而導致手勢識別率低下。針對這些問題,本文著重圍繞手勢膚色分割和靜態(tài)手勢識別這兩個方面開展理論及應用研究,主要完成的工作及貢獻如下:1.在分析相關技術的基礎上,提出了一種綜合多要素的手勢膚色分割方法。該方法首先采用橢圓膚色模型對膚色進行初步分割,然后利用運動物體檢測方法建立背景模型來排除背景中近似膚色的區(qū)域,進而結合人臉識別技術排除人臉膚色區(qū)域,最終分割出手勢膚色區(qū)域。實驗結果表明,本文提出的手勢膚色分割方法在復雜背景或光照條件差的環(huán)境下能獲得較好的手勢分割效果。2.提出了一種簡單有效的統(tǒng)計模板匹配算法,用以實現靜態(tài)手勢識別。首先,基于正態(tài)分布概率模型,利用采集得到的手勢圖像樣本生成各種手勢對應的統(tǒng)計模板特征;其次,利用模板特征定義手勢圖像之間的相似度,進而設計匹配判斷規(guī)則對手勢圖像進行區(qū)分,以判斷待識別手勢圖像對應的手勢類別。針對11種手勢的識別實驗結果表明,本文提出的算法能獲得高于93.5%的平均識別率,優(yōu)于現有的同類算法。3.將前述提出的手勢膚色分割和手勢識別算法應用于人機交互,以C++為編程語言,結合MFC開發(fā)框架及OpenCV開源庫,設計并實現了一個手勢識別交互系統(tǒng)。該系統(tǒng)提供了11種手勢,利用這些手勢可以模擬鼠標和鍵盤操作,達到控制PPT、播放器等軟件操作的目的。該系統(tǒng)界面友好,執(zhí)行效率高,具有較高的通用性。
[Abstract]:In recent years, with the rapid development of computer technology, more natural, efficient and new human-computer interaction methods are emerging. Gesture is one of the basic ways of human communication, which accords with human daily communication habits. Based on monocular vision technology, it has become a research hotspot in the field of human-computer interaction to realize human-computer interaction which is more in line with human communication habits through gesture recognition. At present, although a lot of gesture color segmentation and gesture recognition algorithms have been proposed, the existing algorithms in recognition rate, execution efficiency, and practicability are still insufficient, and need to be improved. For example, most static gesture recognition algorithms have high complexity, and it is difficult to obtain ideal gesture segmentation results in complex background or poor lighting conditions, which leads to low gesture recognition rate. In order to solve these problems, this paper focuses on the theoretical and applied research of gesture skin color segmentation and static gesture recognition. The main work and contributions are as follows: 1. Based on the analysis of related techniques, a new method of gesture skin color segmentation is proposed. In this method, the skin color is initially segmented by using elliptical skin color model, and then the background model is established by moving object detection method to exclude the region of approximate skin color in the background, and then the skin color region of face is excluded by combining face recognition technology. Finally, the skin area of the gesture is segmented. The experimental results show that the proposed skin color segmentation method can achieve a better result of gesture segmentation under complex background or poor illumination conditions. A simple and effective statistical template matching algorithm is proposed to realize static gesture recognition. Firstly, based on the probability model of normal distribution, the statistical template features of various gesture images are generated by using the collected gesture image samples. Secondly, the similarity between gesture images is defined by template features. Then the matching judgment rules are designed to distinguish the gesture images to judge the corresponding gesture categories of the gesture images to be recognized. The experimental results of 11 hand gestures recognition show that the proposed algorithm can achieve an average recognition rate of more than 93.5%, which is better than the existing similar algorithm .3. The algorithm of gesture skin color segmentation and gesture recognition is applied to human-computer interaction. An interactive system of gesture recognition is designed and implemented by using C as programming language combined with MFC development framework and OpenCV open source library. The system provides 11 gestures which can be used to simulate mouse and keyboard operations to control PPTs, players and other software operations. The system has friendly interface, high execution efficiency and high generality.
【學位授予單位】：廣東技術師范學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前10條

1 易靖國;程江華;庫錫樹;;視覺手勢識別綜述[J];計算機科學;2016年S1期

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本文編號：1979747