Adaptive Threshold Based SIFT Algorithm And Application

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.