【作者】 黄令允；

【导师】 林秋华；

【作者基本信息】 大连理工大学 ， 电路与系统， 2010， 硕士

【摘要】 计算机视觉一直是人们研究的热点。简单地说,计算机视觉就是使用计算机智能地认知周围物体的科学。图像匹配是计算机视觉中的一个基本问题。图像匹配的条件是存在一幅待匹配图像和一幅目标图像,待匹配图像一般是目标图像的一部分,但有一定尺度、旋转和光照等方面的变化。图像匹配的任务就是在目标图像中找到待匹配图像的位置。尺度不变特征变换(Scale Invariant Feature Transform, SIFT)是目前图像匹配领域中最活跃的算法之一。SIFT特征对于图像缩放、平移和旋转都具有良好的不变性,对于光照变化和仿射变换或三维投影也具有一定的鲁棒性。由于SIFT特征的不变性优势,SIFT算法被广泛应用到图像匹配领域。但是,SIFT算法具有计算量大、计算时间长的问题,在处理实时问题时有一定的局限性。为了进一步提高SIFT的实际应用能力,本文做了以下几方面工作：(1)深入研究了SIFT算法,发现SIFT计算时间主要耗费在极值点检测和特征向量描述这两个步骤。对于一般图像,SIFT算法可以提取出数百甚至数千的匹配点对,这些匹配点对于图像拼接而言已经远远超过要求。因此,本文提出了一种自适应阈值改进SIFT算法,主要通过自动调整尺度空间极值检测中的阈值,将SIFT特征点的数量控制在一定范围,进而减小运算量。(2)实现了基于图像尺寸压缩与自适应阈值SIFT算法的图像快速拼接方法。本文将最近邻插值法、双线性插值法和双立方插值法等图像尺寸压缩方法与自适应阈值SIFT方法相结合并应用到图像拼接领域。实验结果表明,在处理由一个较大的图像序列生成全景图,要求整体效果好,拼接速度快,但对图像细节要求不高这一类问题时,该算法性能优于传统算法。(3)研究了基于相位相关与自适应阈值SIFT算法的图像拼接算法。在实际中进行图像拼接时,相邻图像的重叠区域是一定的,而且重叠区域只占图像的一小部分,在重叠区域以外进行SIFT极值点检测及描述对图像拼接没有作用。本文将相位相关法与本文提出的自适应阈值SIFT算法相结合,应用到图像拼接问题,并和传统SIFT算法、相位相关结合传统SIFT的算法进行了比较,实验结果表明本文算法有一定的速度优势。更多还原

【Abstract】 Computer vision has always been one of the hot topics. It uses computer intelligence to identify surrounding objects. Image matching is a fundamental aspect of many problems in computer vision. SIFT (Scale Invariant Feature Transform) is one of the most efficient and commonly used image matching algorithms. SIFT features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection.Although SIFT has many advantages, it suffers from the problem of high computation load and high cost of computation time. As such, SIFT algorithm has some limitations in dealing with practical problems. To further enhance the ability of the practical application of SIFT, the thesis includes the following researches:(1) The SIFT algorithm is deeply studied. It is found that the computation time is mainly spent in the detection and description of extreme points. Generally, SIFT algorithm can extract hundreds or even thousands of matching points, the matching points have been enough for the image mosaic. This thesis thus presents a novel improved SIFT algorithm to decrease the computation complexity. Specifically, the new algorithm controls the number of SIFT feature points within a certain range by adaptively adjusting thresholds in the detection of scale-space extreme.(2) A fast image mosaic is achieved based on image size compression and the adaptive threshold SIFT algorithm. We combined image size compression methods including nearest neighbor interpolation, bilinear interpolation and cubic convolution interpolation and the proposed SIFT method, and made an application to the image mosaic. The results show that, the algorithm outperforms the traditional algorithm in dealing with the formation of a larger panorama image sequence, requiring the overall effect, fast speed, but not much on image details.(3) This thesis presents a fast image mosaic method based on phase correlation and the proposed adaptive threshold SIFT. In practice, there are certain overlaps for the adjacent images and the overlapped part is only a small portion of the image. The detection and description of extreme points outside the overlapped region is useless for the image mosaic. This thesis then combines the phase correlation method with the proposed algorithm to do fast image mosaic with a comparison with the traditional SIFT algorithm and the combined method by the phase correlation and the traditional SIFT algorithm. The experimental results show that the proposed algorithm has a certain speed advantage over the traditional one.更多还原