Statistical methods calculate distinct texture characteristics and are appropriate if the size of the texture is similar to the size of the pixels. © 2020, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Jyotismita Chaki

School of Computer Science and Engineering

Vellore Campus

Nilanjan Dey

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Statistical Texture Features

Texture Feature Extraction Techniques for Image Recognition SpringerBriefs in Applied Sciences and Technology

Plants species recognition is one of the most important research topics in the biological sciences. Although leaves are convenient markers for identification, a major drawback is that they are prone to be damaged easily by various environmental and biological factors. The proposed research work aimed to tackle this situation by proposing a leaf recognition system that can specifically handle fragmented leaf images. As leaf images are fragmented they can not be recognize based on shape features. Here a novel approach is proposed by using the combination of fuzzy-color and edge-texture histogram in order to recognize fragmented leaf images. First, the dataset leaf images that are similar to the query fragmented leaf image is identified by using bag-of-feature. Then, the combined feature is used to generate the feature vector. Since fragmented leaves provide less information, this work also attempted to derive a fragment size threshold beyond which results become unpredictable, and whether such thresholds are universal or vary depending on other factors. The efficacy of the proposed method was studied using a multi-layer-perceptron classifier. As there is no public database of the fragmented image, a method was designed to create the reproducible each fragmented leaf image from the whole corresponding one. © 2018 Elsevier GmbH

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Fragmented plant leaf recognition: Bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron

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Journal	Data powered by TypesetTexture Feature Extraction Techniques for Image Recognition SpringerBriefs in Applied Sciences and Technology
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2191-530X
Open Access	0