This study shows a preliminary investigation of shadow detection in drone-acquired images using a deep learning method with minimal labelled shadow images. The aim is to discuss how the selected U-Net architecture performs in a small-sized dataset consisting of various types of shadow brightness and objects. Two types of data augmentation methods, which are shadow variant and geometric transformation are implemented, aiming to improve the segmentation accuracy. Several experimental procedures are performed to observe the model performance. The study shows that adding images for training increases the accuracy of shadow detection in drone images from 0.95 to 0.96, and geometric transformation data augmentation method increases the accuracy from 0.961 to 0.963, while the shadow variant method increases the flexibility of detection.

This study evaluates the performance of shadow detection using different image color models. The pixel-level supervised classification procedure employed in this study includes filtering images, creating a trained shadow model, obtaining shadow masks and post-processing of the output masks. Considering the advent of drones usage, we discuss the results of shadow detection on aerial images. Based on the results, the method using YCbCr color features yielded 92.71% average accuracy. The low performance of shadow detection on images with small shadowed regions and images under various weather conditions indicated that additional investigation is necessary to create detection schemes for challenging input images with high spatial resolution.

There are many mobile applications that are used in the daily lives of Muslims to fulfill their obligations and Islamic lifestyle. However, the user interface of most of these mobile applications has been found to have certain issues with elderly users, in other words, not elderly-friendly. The usability challenges that are faced by older adults when using regular applications include physical challenges, visual problems and issues related to their cognitive ability at their age. This study aims to derive the design guidelines from past studies focusing on addressing common physical and cognitive challenges for elderly people in using regular mobile applications to be implemented in the user interface design of an Islamic mobile application. Several existing Islamic mobile applications are analyzed before developing a prototype of an elderly-Muslim-friendly mobile application based on the design guidelines. Usability evaluation of this prototype is conducted to provide empirical findings on such design guidelines that can be implemented in designing an elderly-Muslim-friendly mobile application. The results affirmed the design guidelines that can be used in the development of Islamic mobile applications that cater for the elderly people. This study contributes in providing a better understanding of the design guidelines that can be implemented in such applications.