论文标题
增强现实国际象棋分析仪（Archessanalyzer）：通过董事会分割对物理国际象棋游戏位置的设备推理，并使用卷积神经网络识别作品
Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Network
论文作者
论文摘要
国际象棋游戏位置分析对于改善游戏很重要。它需要将移动进入国际象棋发动机，该引擎繁琐而易于错误。我们提出了Archessanalyzer，这是一个从物理国际象棋游戏的现场图像捕获到板板和棋子识别的完整管道，以移动分析，最后到增强了国际象棋图位置的现实（AR）叠加层并在物理板上移动。 Archessanalyzer就像场景分析仪 - 它使用传统图像和视觉技术的合奏来细分场景（即国际象棋游戏），并使用卷积神经网络（CNN）来预测分段的零件并将其结合在一起以分析游戏。本文首先在同类方面促进了最终的技术，以结束对板的强大检测和分割，在手持设备应用程序中使用微调的Alexnet CNN和Chess Engine Analyzer进行了国际棋盘检测。整个国际象棋位置预测管道的准确性为93.45 \％，从实时捕获到AR覆盖的3-4.5秒。我们还验证了我们的假设，即Archessanalyzer在分析方面比所有董事会职位的手动输入都要快。我们的希望是，该应用程序提供的即时反馈将有助于全球各地的象棋学习者改善他们的游戏。
Chess game position analysis is important in improving ones game. It requires entry of moves into a chess engine which is, cumbersome and error prone. We present ARChessAnalyzer, a complete pipeline from live image capture of a physical chess game, to board and piece recognition, to move analysis and finally to Augmented Reality (AR) overlay of the chess diagram position and move on the physical board. ARChessAnalyzer is like a scene analyzer - it uses an ensemble of traditional image and vision techniques to segment the scene (ie the chess game) and uses Convolution Neural Networks (CNNs) to predict the segmented pieces and combine it together to analyze the game. This paper advances the state of the art in the first of its kind end to end integration of robust detection and segmentation of the board, chess piece detection using the fine-tuned AlexNet CNN and chess engine analyzer in a handheld device app. The accuracy of the entire chess position prediction pipeline is 93.45\% and takes 3-4.5sec from live capture to AR overlay. We also validated our hypothesis that ARChessAnalyzer, is faster at analysis than manual entry for all board positions for valid outcomes. Our hope is that the instantaneous feedback this app provides will help chess learners worldwide at all levels improve their game.
