Living plants recognition from leaf features

Pao Yue-kong Library Electronic Theses Database

Living plants recognition from leaf features

 

Author: Lin, Jing
Title: Living plants recognition from leaf features
Degree: M.Sc.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Image processing -- Digital techniques.
Plants -- Imaging.
Leaves -- Imaging.
Department: Dept. of Electronic and Information Engineering
Pages: x, 98 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2303060
URI: http://theses.lib.polyu.edu.hk/handle/200/4597
Abstract: This thesis presents leaf image feature extraction and retrieval. Two main contributions are reported in the thesis. They include: (1) specific features of leaf shape and texture; and (2) a botany ontology based similarity measure of leaf images. In the first investigation, we propose four descriptors to characterize a leaf in terms of shape, size, as well as texture. The first two features are shape features which describe the lobation and margin calculated base on the CCD curve. A leaf can be labeled as either a lobed leaf or an unlobed leaf by calculating the local minimums of the normalized CCD curve. Leaves have two local minimum CCD curve are unlobed; others are lobed. The second shape feature called teeth indicates the margin of a leaf is either entire or toothed. Besides the above two shape features, a feature called comparative area is proposed. By computing the ratio of the leaf area and the black bar area, plants with large size leaves and those of small size leaves can be easily differentiated. For the texture, a region-based Grey Level Co-occurrence Matrix is proposed to get rid of the influence from the background when computing the contrast and homogeneity of a leaf image. In the second investigation, we propose a botany ontology based two-stage search algorithm* used in leaf images similarity measure. In the first stage, a query image is categorized to a certain class based on the botany ontology; in the second stage, only the images in the same class are searched using the Euclidean distance. By using this method, the precision of the retrieval results can be improved.

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