Rotation invariant angle-density based features for an ice image classification system

Yue, Shengkai and Yuan, Minglei and Lu, Tong and Shivakumara, Palaiahnakote and Blumenstein, Michael and Shi, Jie and Kumar, G. Hemantha (2020) Rotation invariant angle-density based features for an ice image classification system. Expert Systems with Applications, 162. ISSN 1873-6793

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Abstract

One of the natural disasters which cause economic loss and are a serious threat to society are ice covering phenomena for overhead transmission power lines. This paper presents a new method for ice and non-ice image classification to improve ice detection results. The proposed method uses wavelet decomposition to extract robust features, such as those invariant to rotation, scaling and thickness of ice for classification. The proposed approach estimates the average of the high frequency sub-bands for each level. Then it obtains Canny edge components for the average wavelet image at each level. Our approach studies shape of edge components to identify the presence of ice. To achieve this, the proposed method finds the major and minor axes for each edge component, and then draws parallel lines to the major and minor axes over the edge components. For each parallel line to the major and minor axes, the proposed approach further extracts angle and density-based features for pixels that fall on the parallel lines to the major and minor axes. Next, our method selects features from each average wavelet image and further calculates the mean for the feature vectors corresponding to the level, which results in a feature matrix. Finally, the feature matrix is fed to a Multi-Layer Perceptron Neural Network for classifying ice and non-ice images. Experimental results on a diversified dataset and comparative study with an existing method show that the proposed method is useful for accurate ice detection with better accuracy. (C) 2020 Elsevier Ltd. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Wavelet decomposition; Canny edge components; Major axis; Angle; Density and Multi-layer perceptron neural network
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Mr Umendra uom
Date Deposited: 01 Apr 2021 10:14
Last Modified: 01 Apr 2021 10:14
URI: http://eprints.uni-mysore.ac.in/id/eprint/15454

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