Fast matching computation method used for fruit picture

Abstract

The invention discloses a fast matching computation method used for a fruit picture. Through the extraction of an effective matching area and the improvement on a SIFT (scale invariant feature transform) method, a fruit image can be fast matched; the side surface image of a fruit is firstly obtained; the left boundary points and the right boundary points of a fruit profile are searched in the side surface image so as to extract an effective area required for matching; then, the feature points of the effective area are extracted; feature point sets with feature point descriptors are obtained; the feature points are matched by calculating an Euclidean distance between the feature point descriptors in the two feature point sets; and after a candidate matching point set is obtained, mismatching points are removed to obtain a correct matching point set. According to the fast matching computation method used for the fruit picture, a single layer of difference-of-Gaussian image is used for replacing a scale space construction in the SIFT method, rotation invariance is omitted, and the feature point descriptors are subjected to dimensionality reduction to reduce redundant computation in a fruit picture feature point detection process; and through the extraction of an effective overlapping area, an unnecessary computation amount is omitted, and operating rate is improved.

Description

technical field [0001] The invention relates to an image matching calculation method, in particular to a fast matching calculation method for fruit images in the technical field of image processing. Background technique [0002] Image matching refers to identifying the same point between two or more images through a certain matching method. In recent years, image matching has become a key technology and research hotspot in the fields of image analysis and processing such as object recognition, robot map perception and navigation, image stitching, 3D model building, gesture recognition, image tracking, and motion comparison. [0003] The acquisition of fruit surface information is the basis for the detection of fruit quality indicators such as size, shape, surface color and surface defects. The accuracy of surface color and surface defect detection depends on the acquisition of complete fruit surface images. Image mosaic technology is the key to achieve complete fruit surfac...

AI Technical Summary

Problems solved by technology

[0005] However, the fruit detection and grading technology has high requirements on the speed of the method. Although the matching adaptability of the SIFT method is high, the method is complex, computationally intensive, and time-consuming.

Images