Chokri Ben Amar

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



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms


Chokri Ben Amar received the B.S. degree in Electrical Engineering from the National Engineering School of Sfax (ENIS) in 1989, the M.S. and PhD degrees in Computer Engineering from the National Institute of Applied Sciences in Lyon, France, in 1990 and 1994, respectively. He spent one year at the University of ”Haute Savoie” (France) as a teaching assistant and researcher before joining the higher School of Sciences and Techniques of Tunis (ESSTT) as Assistant Professor in 1995. In 1999, he joined the Sfax University (USS), where he is currently a professor in the Department of Electrical Engineering of the National Engineering School of Sfax. His research interests include ComputerVision and Image and video analysis. These research activities are centered on Wavelets and Wavelet networks and their applications to data Classification and approximation, Pattern Recognition and image and video coding, indexing and watermarking.

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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