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Shadows cause problems in many remote sensing applications like images segmentation, objects extraction and stereo vision. This paper presents a new and an automatic approach to detect and remove shadows from pair of dense urban very high resolution (VHR) remote sensing images. The main contribution of this paper is twofold. First, a proposed approach is efficient to restore objects hidden by shadows, second, it improves a stereo matching process. We have chosen to operate on Ikonos pairs as an example of urban remote sensing images, for that, shadow detection is achieved using a new technique of property based method, operating directly in red, green and blue colour space (RGB). Shadow removal proposed technique aims to produce a needed amount of light to the shadow regions by multiplying the shadow regions by constants, after that, the shadow edge correction is applied to reduce the errors due to diffusion in the shadow boundary. Once pair of shadow free images is recovered, we apply a stereo matching process using a Hopfield neural technique in order to find homologous regions. Our results from different urban pairs show the effectiveness, the simplicity and the fastness of the proposed approach to reveal details hidden by shadows and to obtain a high stereo matching rate.

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The COVID-19 pandemic forced much of the world into lockdown. For that reason, INTTIC switched from blended learning to total e-learning. In this paper, we explore the impact of e-learning on INTTIC students during the COVID-19 lockdown. To this end, we focus on four main variables: the effectiveness, the cost, the flexibility, and the independent work involved in e-learning. Our results show that e-learning cannot be entirely effective without the teacher’s online interaction. It is budget-friendly because students can save on transportation, food and daily school expenses, and it offers students a large degree of flexibility. Nevertheless, almost all students struggle to complete their homework on a deadline. The main causes could include the psychological aspects of lockdown, the lack of prior experience with total e-learning, and a need for teachers' supervision. Future research should study the impact of e-learning on teachers during the COVID-19 lockdown.

Face recognition has been very popular in recent years, for its advantages such as acceptance by the wide public and the price of cameras, which became more accessible. The majority of the current facial biometric systems use the visible spectrum, which suffers from some limitations, such as sensitivity to light changing, pose and facial expressions. The infrared spectrum is more relevant to facial biometric, for its advantages such as robustness to illumination change. In this paper, we propose two multispectral face recognition approaches that use both the visible and infrared spectra. We tested the new approaches with Uniform Local Binary Pattern (uLBP) as a local descriptor and Zernike Moments as a global descriptor on IRIS Thermal/Visible and CSIST Lab 2 databases. The experimental results clearly demonstrate the effectiveness of our multispectral face recognition system compared to a system that uses a single spectrum.

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