Szymon Wasik

Szymon Wasik, Maciej Antczak, Jan Badura et al.

Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we introduce a formal definition of an online judge system and summarize the common evaluation methodology supported by such systems. Finally, we briefly discuss an Optil.io platform as an example of an online judge system, which has been proposed for the solving of complex optimization problems. We also analyze the competition results conducted using this platform. The competition proved that online judge systems, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.

Maciej A. Czyzewski, Artur Laskowski, Szymon Wasik

Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. However, its solution is crucial for many experienced players who wish to compete against AI bots, but also prefer to make decisions based on the analysis of a physical chessboard. It is also important for organizers of chess tournaments who wish to digitize play for online broadcasting or ordinary players who wish to share their gameplay with friends. Typically, such digitization tasks are performed by humans or with the aid of specialized chessboards and pieces. However, neither solution is easy or convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations. We designed a method that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. The proposed algorithm processes pictures iteratively. During each iteration, it executes three major sub-processes: detecting straight lines, finding lattice points, and positioning the chessboard. Finally, we identify all chess pieces and generate a description of the board utilizing standard notation. For each of these steps, we designed our own algorithm that surpasses existing solutions. We support our algorithms by utilizing machine learning techniques whenever possible. The described method performs extraordinarily well and achieves an accuracy over $99.5\%$ for detecting chessboard lattice points (compared to the $74\%$ for the best alternative), $95\%$ (compared to $60\%$ for the best alternative) for positioning the chessboard in an image, and almost $95\%$ for chess piece recognition.