外文翻譯---關(guān)于顏色識(shí)別中顏色特征分析的方法(已改無錯(cuò)字)
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【正文】 PERIMENTAL RESULTS AND ANALYSIS When identified by human eyes, colors are divided into eleven categories as red, green, blue, yellow, purple, orange, pink, brown, gray, white and black, shown in Fig. 2. The evaluation algorithm is performed respectively on RGB space, CMY space, YUV space, I1I2I3 spaceand HSI space. Feature parameter and assessment indicators are shown in Table Ⅰ . 陜西科技大學(xué) 8 Seen from Table Ⅰ , the HSI space has the best performance pared to other four analyzed color spaces. V. CONCLUSION It is necessary to select an effective color space for colorful image processing. This paper analyzes and pares the performance of five mon color spaces based on divisibility criterion. Experimental results show that HSI color space has the best divisibility performance pared with the other four spaces. It provides a basis for color space selection in color recognition. ACKNOWLEDGMENT This work was supported by a grant from Natural Science Foundation of Liaoning Province (Grant number: 20202153). REFERENCES [1] WANG Hui, LVYan, ZHANG Ka, “Research on Color Space Applicable to Wood Species Recognition”, FORESTRY MACHINERY amp。 WOODWORKING EQUIPMENT, , ,2020. [2] Palus, H. Representations of color images in different color spaces, The Color Image Processing Handbook. London: Chapmanamp。 Hall,1998. [3] WANG Yansong, JIN Weiqi, “Surface Inspection Based on Color Clustering of Mapping Chromatism”, Transactions of BeijingInstitute of Technology, ,2020. [4] CAO Jian qiu, WANG Hua qing and LAN Zhang li, “Skin Color D ivision Base onModified YCrCb Color Space”, JOURNAL OF CHONGQ ING JIAOTONG UN IVERSITY( NATURAL SC IENCE) , Vol. 29, ,2020. [5] WANG Feng, MAN Lichun and XIAO Yijun et al., “Color Recognition of License Plates Based on Immune Data Reduction”. JOURNAL OF SICHUAN UNIVERSITY (ENGINEERING SCIENCE EDITION), , pp. 164170, 2020. [6] XU Qing, SHI Yuexiang, XIE Wenlan and ZHANG Zhengzhen, “Method of face detection based on improved YUV color space”, Computer Engineering and Applications, Vol. 44, ,2020. [7] HAN Xiaowei, Study on Key Technologies of Color Image Processing, Northeast University,2020. [8] LIU Zhongwei, ZHANG Yujin, “A Comparitive and Analysis Study of Ten color Featurebased Image Retrieval Algorithms”,SIGNAL PROCESSING, , , 2020. [9] Liu Jin, Chen Gi, Yu Ruizhao, “Development of Computer Color Science”, COMPUTER ENGINEERING,2,1997. [10] YANG recognition and Intelligent Computation: Matlab Technolgoy, Beijing: Electronic Industry Press, 2020. 陜西科技大學(xué) 9 中文譯文 2020 第八屆模糊系統(tǒng)與知識(shí)挖掘國際學(xué)術(shù)會(huì)議（ FSKD） 關(guān)于顏色識(shí)別中顏色特征分析的方法 紀(jì)建偉 齊曉軒 信電與電氣工程學(xué)院 沈陽農(nóng)業(yè)大學(xué) 沈陽，中國 摘要：分析五種常用的顏色空間的特征并研究其分別對(duì)顏色識(shí)別的影響?；诰? 離標(biāo)準(zhǔn)的可除性鑒定被用來評(píng)定每個(gè)顏色空間內(nèi)的顏色特征，實(shí)驗(yàn)結(jié)果表明 HSI 顏色空間可除性最強(qiáng)。 關(guān)鍵詞：顏色空間；顏色識(shí)別；特征分析；可除性標(biāo)準(zhǔn) I． 概述 顏色是描述色彩圖像時(shí)最直觀的視覺特征。 鑒于顏色特征幾乎不受范圍，旋轉(zhuǎn)及轉(zhuǎn)化對(duì)輸入圖像的干擾，顏色被廣泛地應(yīng)用于圖像識(shí)別【 1】。色彩圖像中的顏色可由不同的顏色空間模式規(guī)定，比如 RGB 空間， CMY 空間， I1I2I3 空間， YUV空間和 HSI 空間，其中， RGB 是最基本也是最常見的顏色空間，并且可以很容易的映
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