圖像微觀結(jié)構(gòu)的二值化表示與目標(biāo)識(shí)別應(yīng)用
doi: 10.11999/JEIT170513
基金項(xiàng)目:
國家自然科學(xué)基金(61602397),湖南省自然科學(xué)基金(2017JJ2251, 2017JJ3315),湖南省重點(diǎn)學(xué)科建設(shè)項(xiàng)目
Binarization Representation of Image Microstructure and the Application of Object Recognition
Funds:
The National Natural Science Foundation of China (61602397), The Natural Science Foundation of Hunan Province (2017JJ2251, 2017JJ3315), The Key Discipline Construction Project of Hunan Province
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摘要: 該文提出一種新穎的基于二值圖像微觀結(jié)構(gòu)模式(Binary Image Micorsructure Pattern, BIMP)表達(dá)和灰度圖像微觀結(jié)構(gòu)二值模式(Gray Image Micorsruct Maximum Response Pattern, GIMMRP)編碼方法。通過對圖像33鄰域結(jié)構(gòu)進(jìn)行二值編碼,獲得圖像微觀結(jié)構(gòu)的描述,進(jìn)而選取其中的重要執(zhí)行模式子集和池化操作,實(shí)現(xiàn)整體圖像的表示。為了檢驗(yàn)算法的有效性,在ORL, YALE兩個(gè)人臉公開數(shù)據(jù)集,MNIST, USPS兩個(gè)手寫數(shù)字公開數(shù)據(jù)集,以及非公開車標(biāo)數(shù)據(jù)集上進(jìn)行了測試,顯示該方法具有很強(qiáng)的鑒別能力和魯棒性,可以達(dá)到和超過很多最新算法的性能。Abstract: A novel expression based on Binary Image Microstructure Pattern (BIMP) and Gray Image Micorstructure Maximum Response Pattern (GIMMRP) coding method is proposed. Through the binary coding of the 33 neighborhood structure of the image, the description of the microstructure of the image is obtained, and then selecting the important execution mode subset and the pooling operation to realize the representation of the whole image. In order to verify the effectiveness of the algorithm, experiments are carried out on the ORL, YALE two human face data set, MNIST, USPS two handwritten digital public data sets, as well as non-public vehicle standard data set. The results show the method has strong discriminative power and robustness and can achieve better performance than many of the latest algorithms.
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