本文選題：交通標(biāo)志 + 識別　； 參考：《公路》2017年04期

【摘要】：為了提高交通標(biāo)志分類問題的正確率,提取有效的特征值才可以獲得更高的分類正確率。校核分析交通標(biāo)志圖像特點,在分類研究的背景下提出了特征融合的思路,在主成分分析(PCA)降低維度的基礎(chǔ)上,提取灰度直方圖的特征,將PCA提取的特征和灰度直方圖特征融合,并且將融合數(shù)據(jù)作為分類的輸入特征,通過交通標(biāo)志數(shù)據(jù)庫進行實驗分析,多次改變要降低的維度,然后融合灰度直方圖特征進行分類,用MATLAB和GUI工具進行仿真,實例驗證結(jié)果表明,得出的正確率明顯提高,在交通標(biāo)志的分類中效果顯著。
[Abstract]:In order to improve the correct rate of traffic sign classification problems and extract effective eigenvalues, a higher classification accuracy can be obtained. The characteristics of traffic sign images are analyzed and the idea of feature fusion is put forward in the background of classification research. On the basis of the dimension reduction of principal component analysis (PCA), the features of gray histogram are extracted and PCA is extracted. The characteristics and gray histogram feature fusion, and use the fusion data as the input characteristics of the classification, through the traffic sign database experiment analysis, many times change to reduce the dimension, then fusion gray histogram characteristics to classify, using MATLAB and GUI tools to simulate. Example verification results show that the correct rate is obviously raised. High, it has a remarkable effect in the classification of traffic signs.

【作者單位】： 濟南軌道交通集團有限公司;
【分類號】：U495;TP391.41
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本文編號：1792082