Gate to Computer Vision and Pattern Recognition (gtCVPR) – Volume 1 Number 1 – 2015

Gate to Computer Vision and Pattern Recognition (gtCVPR)

ISSN (online): 2408 – 0551

Volume 1 Number 1 – 2015

Special Issue: Recent Advances in Plant Leaf Classification

Editorial - Special Issue: Recent Advances in Plant Leaf Classification

Abdul Kadir and George A. Papakostas
(pages 1-1) DOI: 10.15579/gtcvpr.0101.001001

Downloads: 133

Leaf Identification Using Fourier Descriptors and Other Shape Features

Abdul Kadir
(pages 3-7) DOI: 10.15579/gtcvpr.0101.003007

Views: 11
Downloads: 352


Leaf identification is a challenging research. So far, many approaches have been proposed. In this paper, an approach that combines Fourier descriptors with other shape features was investigated to identify 100 hundred kinds of leaves. The result shows that the combination of Fourier descriptors and several other shape features can be used to identify leaves with the accuracy rate of 88%. This result indicates that this approach is a promising way for identifying leaves.

Database Formation for Authentication of Basil (Ocimum tenuiflorum) Leaf Using Image Processing Technique

T. Vijayashree and A.Gopal
(pages 9-17) DOI: 10.15579/gtcvpr.0101.009017

Views: 9
Downloads: 231


Plants play one of the main roles in our ecosystem. Manual identification for the leaves sometimes leads to greater difference due to look-alike. Hence authentications of leaves are much required for medicinal purposes. The aim of this work is to form the database in order to classify and authenticate tulsi leaf for the purpose of herbal medicines. This paper aims in developing the database with 50 samples of tulsi under various conditions for classification. After forming the database the next step is to compare it with the test leaf to identify the closest match.

Comparison of Edge Detection Methods in Leaf Shape Analysis For Plant Classification

Manisha Amlekar, Ashok Gaikwad, Pravin Yannawar, Ramesh Manza
(pages 19-22) DOI: 10.15579/gtcvpr.0101.019022

Views: 9
Downloads: 222


In this research paper, comparison of edge detection methods is presented in leaf shape analysis for plant classification. Leaf is a very significant component of plant species that identify and classify the plants. Plant classification is the task performed by trained botanists and taxonomists. This task includes performing a set of operations. Because of this the task of classification of plants manually is time consuming. There are many biometric features of the leaves of the plants for classification. In this paper, Sobel and Canny edge detection methods are compared for their performance in leaf shape analysis operation for plant classification.