南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (2): 481–.
• • 上一篇
张栋冰
Zhang Dongbing
摘要： 针对环境复杂的车标区域难以定位的问题，提出了一种基于视觉注意机制(Visual Attention Theory，VAT)与支持向量机(Support Vector Machine，SVM)结合的车标定位算法. 首先采用多信息融合特征对车牌进行综合定位，利用车标和车牌的相对位置关系确定车标感兴趣区域(Area of Interest，ROI)，对该区域进行边缘检测处理，并对边缘所在位置进行显著性分析，利用支持向量机方法训练车标显著性模型选择分类器，然后利用模型选择分类器选取最优模型进行显著性区域提取，最后对显著性区域设计了一套离散车标前景合并机制进行最终的车标区域定位. 实验表明：该方法能够有效地进行车标区域定位，与其他车标定位方法相比，具有检测率高、鲁棒性好、时效性快等特点，能够较好地定位不同天气不同场景的车标，具有较好的实用价值．
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