Optical Image Mosaic Methods Based on MEMS Gyroscope

Wen-shuang, 伍文双 Wu; Huajun, 冯华君 Feng; Zhihai, 徐之海 Xu; Qi, 李奇 Li; Yueting, 陈跃庭 Chen

doi:10.3788/gzxb20184703.0310001

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“…Due to the obvious stitching of traditional image stitching, Shuai Wen-Shuang et al [22] have proposed a new method to solve this problem and make the stitching result more smoother. This method is based on SIFT algorithm and morphological component analysis (MCA) in training dictionary [23].…”

Section: Introductionmentioning

confidence: 99%

Application of Migration Image Registration Algorithm Based on Improved SURF in Remote Sensing Image Mosaic

2020

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The classical SURF algorithm has many disadvantages, such as high dimension of feature descriptor, large amount of computation, and low matching accuracy when the angle of rotation and angle of view is too large. To solve the above problems, an improved algorithm is proposed. Firstly, image preprocessing is carried out by image binarization, feature points are extracted by Hessian matrix, and then feature description is carried out by using circular neighborhood of feature points. Har wavelet response is used to establish descriptors for each feature point, and the normalized gray-level difference and second-order gradient in the neighborhood are calculated simultaneously to form a new feature descriptor. Finally, the RANSAC algorithm is used to eliminate the mismatch points. The algorithm does not Compared with the classical SURF algorithm, it has the advantage of speed, and makes full use of the gray level information and the detail information, so it has higher accuracy. Experimental results show that the algorithm has good robustness and stability to image blur, illumination difference, angle rotation, field of view transformation and so on. The algorithm is applied to remote sensing image stitching to obtain the stitched image with no obvious geometric shift and good edge connection. This algorithm is a kind of image registration algorithm with short time and high precision, which can meet the registration requirements of remote sensing image stitching.

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Section: Introductionmentioning

confidence: 99%