【摘要】 在比较目前特征提取和匹配的几种方法的基础上,提出了一种基于改进特征提取和匹配的拼接方法,使得图像拼接的质量和速度得到提高.该算法首先利用改进的尺度不变特征变换(scale invariant feature transform,SIFT)特征提取方法获得图像特征点,其次利用近似最近邻匹配进行特征匹配并引入随机抽样一致性(random sampleconsensus,RANSAC)算法去除误匹配对,最后根据匹配的特征点对得到的图像间的变换参数进行拼接和融合.该算法具有很强的鲁棒性,允许图像有缩放变换、旋转变换,不受图像噪声、色差的影响.实验证明,该方法可实现高质量快速的图像拼接.更多还原

【Abstract】 By comparing with some conventional methods of feature extraction and matching,the paper proposes an improved method of feature extraction and matching for image mosaic which improves image quality and processing speed.It uses scale invariant feature transform(SIFT) to extract invariant features from images mosaic,approximate nearest neighbor searching and random sample consensus(RANSAC) to perform reliable matching.Parameters of the transformation between images are obtained from the matched feature points to realize image stitching and blending.The feature points are invariant to affine transformation,noise contamination and illumination variation,leading to robustness of the method.Experimental results show that the proposed method is fast and can produce high quality image mosaic.更多还原