✔️Using Image Processing to Identify and Score Darts thrown into a Dartboard ✔️Introduction The game of darts is a throwing sport in which participants toss projectiles into a circular target attached to a vertical surface. The target is divided into many regions which correspond to different point and multiplier values. Darts is commonly played across North America and Europe and is a popular past time for many so an application that allows players to more conveniently keep score would be widely beneficial. “Darts” is a general term for a targeting game following this basic premise and many game variations exist within this archetype; all utilizing the standard dart projectiles with a regulation dart board. Each game variant may have different objectives for which players to aim but identifying the region in which the dart has hit in the dartboard is necessary for proper scoring. This project proposes the use of image processing to identify thrown da...
Chess Board Detection ✔️ Introduction Chinese chess is one of the most popular board games in China. It is a two-player strategy board game set up with 32 chess pieces on a board nine lines wide and ten lines long. The chess pieces are all of the same flat circular disk shape. Each piece is labeled with a Chinese character to represent one of the seven types. The color of the piece indicates the player‘s ownership. ✔️ Project Goal The goal of the proposed project is to correctly recognize the state of a Chinese chess game by processing the images captured by the camera of an Android mobile phone. With the essential information extracted from the images, we will be able to save and share the record of a game very efficiently. ✔️ Work Flow To reach the goal, we need to detect the chess board, identify the location of each chess piece and recognize the type of the piece. Chess Board Detection To ext...
Image Processing Pipeline for Facial Expression Recognition under Variable Lighting 1 Introduction Automated facial expression recognition has proven to be beneficial in a variety of settings. For instance, in the Wall Lab of Stanford Medical School, expression recognition is used in a Google Glass application that helps children and adults with Autism detect the emotions of people they are interacting with. Thus, research into increased classification accuracy for expression recognition can have great impact. Many studies addressing this subject use images with uniform lighting conditions[1]. This is understandable because it allows for accurate evaluation of the recognition algorithm. However, for most practical applications, the emotions recognition task is done in real-world conditions where the lighting is diverse and far from being uniform[2]. In this project, we aim to study the effects of different lighting/shadowing on the emotions recognition task and find t...
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