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

Abstract

One of the natural disasters which cause economic loss and are a serious threat to society are ice covering phenomena for overhead transmission. This paper presents a new method for ice and non-ice image classification to improve ice detection results. The proposed method explores wavelet decomposition to extract robust features, such as those invariant to rotation, scaling and thickness of ice for classification. The proposed method 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. The proposed method studies shapes 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 method further extracts angle and density-based features for pixels that fall on the parallel lines to the major and minor axes. Next, the proposed 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.

Multi‐gradient‐direction based deep learning model for arecanut disease identification

“…For disease identification, we need to extract the arecanut region from the background rather than considering the whole image. If we use the whole image for feature extraction, it can be represented as classification but not disease identification [20][21][22][23]. In this way, the disease identification is different from image classification.…”