Automated Mosque Recognition: A Convolutional Neural Networks and Laplacian Pyramid Approach: Fid-Bau Portal

Automated Mosque Recognition: A Convolutional Neural Networks and Laplacian Pyramid Approach

Baziyad, Mohammed / Nassif, Ali Bou / Rabie, Tamer

In this paper, a mosque recognition system based on the Convolutional Neural Networks (CNN) and the Laplacian Pyramid is proposed. CNN networks have the ability to autonomously obtain and extract from images the features that can assist in the decision making. However, due to some factors such as different environmental conditions as well as different capturing conditions, the learned features by the CNN network can be misleading if trained using an incomplete dataset. Thus, in this paper, the preferred features which are invariant to different capturing conditions are first extracted from raw images using the Laplacian pyramid. This is done by extracting the lowest level of the Laplacian pyramid of each image. The first Laplacian level is a high-frequency representation of an image that emphasizes the edges of an image, which are invariant features to various lighting conditions. Next, these high-frequency images are fed to the CNN network instead of the raw images. Experimental results have proved the efficiency of the proposed technique, where the accuracy of the proposed scheme has achieved an accuracy percentage of 97.25% surpassing other conventional machine learning techniques such as Muti-Layer Perceptron (MLP) and Support Vector Machine (SVM).