Method for extracting and describing image characteristics with turnover invariance
An image feature extraction and image technology, which is applied in the field of image processing, can solve problems such as the failure of SIFT feature extraction methods, and achieve the effects of increasing robustness, speed, and expanding application fields
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[0031] First, the scale of the input image is changed. This change is done through Gaussian convolution. In a series of images of different scales, the extreme point of the gray value of the pixel is found for each pixel. However, not all extreme points meet the criteria as feature points. Since the feature points need to have certain prominence and robustness, by setting appropriate thresholds for Difference of Gaussian and Hessian matrix , so as to filter the candidate points with low contrast and edge response of feature points respectively. The extreme points left after these two steps of screening are the desired feature points. For these extreme points, after fitting them with a three-dimensional quadratic curve, their precise coordinates and scale information are obtained. The coordinates, scale and other information of the feature points are preserved to provide usable information for the subsequent matching stage.
[0032] The above part can be considered as a featur...
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