A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to ...A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.展开更多
针对Bernsen算法的缺陷,提出一种名为BM(Bernsen and Mean)的新算法,该算法对于细节较多、形状多样的图像有着良好的处理效果,且能够克服不均匀光照的影响。通过调节算法中的参数就能够满足不同场合和不同类图像的需要。用其得到的目标...针对Bernsen算法的缺陷,提出一种名为BM(Bernsen and Mean)的新算法,该算法对于细节较多、形状多样的图像有着良好的处理效果,且能够克服不均匀光照的影响。通过调节算法中的参数就能够满足不同场合和不同类图像的需要。用其得到的目标图形完整真实,且噪声点很少。利用细节信息丰富的车标图片对BM算法进行测试,结果表明该算法整体效果优于其他算法。展开更多
文摘A patch-based method for detecting vehicle logos using prior knowledge is proposed.By representing the coarse region of the logo with the weight matrix of patch intensity and position,the proposed method is robust to bad and complex environmental conditions.The bounding-box of the logo is extracted by a thershloding approach.Experimental results show that 93.58% location accuracy is achieved with 1100 images under various environmental conditions,indicating that the proposed method is effective and suitable for the location of vehicle logo in practical applications.
文摘针对Bernsen算法的缺陷,提出一种名为BM(Bernsen and Mean)的新算法,该算法对于细节较多、形状多样的图像有着良好的处理效果,且能够克服不均匀光照的影响。通过调节算法中的参数就能够满足不同场合和不同类图像的需要。用其得到的目标图形完整真实,且噪声点很少。利用细节信息丰富的车标图片对BM算法进行测试,结果表明该算法整体效果优于其他算法。
