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Creating Drawings from Digital Images Description There are many algorithms out there for non-photorealistic rendering of images to look like   drawings. Jin et al and Li et al create drawings from images by creating directional lines that   match the intensity of the image. However, the resulting images don’t stylistically look like an   artist drew them. Efros et al found that image quilting can be used to transfer a specific drawing   style to an image such that it looks like it has actually been drawn by an artist. I will attempt to  implement his algorithm to create drawings from digital images. Plan I believe the following steps will be necessary to creating drawings from digital images that  resemble the drawings of famous artists. 1. Split the source texture (for example Enrico Donati’s drawing) into overlapping blocks of  size B. 2. Split the source image into overlapping blocks of size B. 3. Choose a block from the source texture such that it has similar intens

Plane Extraction on Surfaces OUTPUT Description :  The use of SLAM has many applications in drones and augmented reality to track the 6DOF  position of the camera. Currently there are two state of the art methods: ORB-SLAM and LSD-SLAM. We would be choosing to use ORB-SLAM because of the speed benefits of constructing a sparse feature  map instead of a dense feature map and its robustness in tracking from literature. On the other hand, an  implementation of semi-direct visual odometry is similar to ORB-SLAM but uses direct methods by use of photometric error to estimate pose instead of feature-based methods in SLAM which allows for speed-up. For now, we plan on using ORB-SLAM because of Android support online, but if possible we would like  to use the implementation of Semi-Direct Visual Odometry to do the tracking since it is faster, however  there is no loop closure or relocalization included with the open-source code at the current time.  After ORB-SLAM is implemented

Thumbnails for Light Fields OUTPUT Background and Context: Light field (LF) cameras are gaining popularity because of advances in lens technologies and new  post-processing capabilities [1]. Consequently, new products are constantly being developed that  are designed to attract more consumers. For examples, there are currently several projects to  develop and apply LF technology on phone cameras (i.e. Linx and Pelican Imaging). As the  technology becomes more readily available to everyday consumers, new and creative rendering  procedures will be required to maximize the utility of the captured data. For the technology to be  adopted, the rendered images must provide fun/creative attributes that are beyond conventional  image manipulation (i.e. social media). Project Goals: Given the growing availability for consumer cameras, there is an increasing potential for using light  fields online in applications such as social media or advertising [2]. For this project, we propose

Algorithm for Drop Breakup Characterization Output Microfluidics is a research area that focuses on studying fluids in the length scales of 100 µm or less. Recently, there has been interest in using drops as vessels to study chemical reactions and single cell studies . However, some channel designs could cause drops to break unintentionally.We would like to study the physical parameters that cause drops to breakup in a constriction. Our channel of interest, a tapered channel seems to promote squeezing between drops which consequentlycauses drops to break at the constriction. A single layer of monodisperse drops (drops that are the same volume) will be used to flow through the microfluidic channel. Furthermore, these monodisperse drops are packed together to produce a high volume fraction (high number of drops per volume). For the image processing algorithm, we want to characterize the drop pair (drop A and B) prior to flowing into the constriction (ie. Centroid of each drop

 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 extract an accurate representation of the chess