Plant identification method based on elliptical Fourier descriptors and weighted sparse representation

Abstract

The invention provides a plant identification method based on elliptical Fourier descriptors and weighted sparse representation. The plant identification method is mainly and technically characterized by comprising the steps that leaf images are preprocessed, wherein all the colored leaf images are converted into grayscale images, the leaf images are separated from the background through an Otsu segmentation algorithm and converted into binary images, and small holes of the leaf images are eliminated through an erosion algorithm; edge detection is conducted by adopting a Canny edge detector; centroids of boundaries are calculated; the Fourier descriptors are calculated; a complete dictionary is constructured, wherein Fourier descriptor vectors of all leaf image data sets are divided into training sets and test sets, and the complete dictionary is composed of the Fourier descriptor vectors of all the training sets; and optimization is conducted by a weighted sparse representation classifier. According to the plant identification method, by adopting the elliptical Fourier descriptors, good robustness is achieved on noise and other factors, and by applying the weighted sparse representation classifier (WSRC) to plant species identification and particularly to a low-dimension space, the identification rate is obviously increased.

Description

technical field [0001] The invention belongs to the technical field of image recognition, in particular to a plant recognition method based on elliptic Fourier descriptors and weighted sparse representation. Background technique [0002] Plant species identification technology using image processing and computer vision technology is very important to environmental protection, land managers and forest farmers. Leaves are an important indicator of plant species identification. Plant species identification based on leaf morphology is a very important and challenging research field. Since the blade is a two-dimensional image, it is the most suitable part for image processing. We can use shape, color, grain, edge, texture, veins, tip and base to describe leaves well. In a large variety of species, the leaves of most plants have high inter-specific similarity and low intra-specific similarity, such as figure 1 As shown, the difficulty of the leaf image itself is that it is var...

AI Technical Summary

Problems solved by technology

However, the key problem that these methods cannot overcome is how to deal with different deformations of blade features and large and small cross-class changes.

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