發(fā)布時間：2019-03-17 13:26

【摘要】：針對NAO機器人識別目標(biāo)準(zhǔn)確率過低的問題,為降低光照對識別的影響,提出一種基于HSV顏色空間的輪廓信息特征識別的算法,通過融合顏色特征和輪廓特征識別圖像中的目標(biāo)。利用HSV空間模型,通過顏色閾值分割對圖像進(jìn)行預(yù)處理,提取紅綠色目標(biāo);根據(jù)目標(biāo)規(guī)則的多邊形輪廓,對其形狀信息加以約束;利用二值圖像的輪廓特征矩加以判決,得到識別目標(biāo)及其在圖像中的中心坐標(biāo),實現(xiàn)目標(biāo)的精確識別。利用NAO機器人采集圖像進(jìn)行模擬實驗,改變NAO與目標(biāo)的相對位置并多次測量,成功定位的準(zhǔn)確率可達(dá)到92.67%。實驗結(jié)果表明,NAO機器人采用該算法可以快速穩(wěn)定地實現(xiàn)目標(biāo)識別,提高了準(zhǔn)確率。
[Abstract]:In order to reduce the influence of illumination on recognition, an algorithm of contour information feature recognition based on HSV color space is proposed to solve the problem of low accuracy of target recognition by NAO robot. In order to reduce the influence of illumination on recognition, an algorithm is proposed to recognize targets in the image by fusion of color features and contour features. The HSV space model is used to pre-process the image by color threshold segmentation to extract the red-green target, and the shape information of the image is constrained according to the polygon contour of the target rule. The recognition target and its center coordinate in the image can be obtained by using the contour characteristic moment of binary image, and the target recognition can be realized accurately. The NAO robot is used to collect images for simulation experiment, and the relative position between NAO and target is changed and measured many times. The accuracy of successful localization can reach 92.67%. The experimental results show that the NAO robot can quickly and stably realize the target recognition and improve the accuracy.
【作者單位】： 山東大學(xué)控制科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(61673244、61273277)
【分類號】：TP242;TP391.41

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