Ameni Sassi

Work place: REGIM-Lab.: REsearch Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, 3038, Sfax, Tunisia



Research Interests: Computational Science and Engineering, Computational Engineering, Computer systems and computational processes, Engineering


Ameni Sassi is a Computer Engineer since 2011. She received theMastersdegree in New Technologies and Domain-Specific Computer Systems from theNational School of Engineers of Sfax, University of Sfax, in 2013. Currently, she is a Ph.D student in Computer Systems Engineering.

Author Articles
Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding

By Ameni Sassi Wael Ouarda Chokri Ben Amar Serge Miguet

DOI:, Pub. Date: 8 Apr. 2019

Skyline scenes are a scientific matter of interest for some geographers and urbanists. These scenes have not been well-handled in computer vision tasks. Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches. In this paper, we proposed a new CNN learning approach to categorize skyline scenes. The proposed model requires a pre-processing step enhancing the deep-learned features and the training time. To evaluate our suggested system; we constructed the SKYLINEScene database. This new DB contains 2000 images of urban and rural landscape scenes with a skyline view. In order to examine the performance of our Sky-CNN system, many fair comparisons were carried out using well-known CNN architectures and the SKYLINEScene DB for tests. Our approach shows it robustness in Skyline context understanding and outperforms the hand-crafted approaches based on global and local features.

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