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Fall Workshop Final Project Suggestions Face Detection Difficulty: Hard Synopsis: Being able to identify a person from an image has obvious applications, hence why face detection has become a important tool of the surveillance state. The project consists of the first stage of facial recognition, which is of detection of facial features. The larger goal of being able to recognize a person from their features is beyond the short scope of this workshop. Inputs: A set of images of faces of various people. Desired Outputs: Identification the regions of the image corresponding to the eyes, nose and mouth of the person in the image. Handwriting Detection Difficulty: Easy Synopsis: In this project you will build a system to detect handwritten numbers and letters. There are several ways to create a program that recognizes handwriting any you may choose the implement whichever you want. One of the most common though is to use a nearest neighbors algorithm and to train the algorithm on a large data set of presorted data. Inputs: 50 pictures of hand written numbers Desired Outputs: A list of numbers. Heat Equation Simulation Difficulty: Easy Synopsis: In this project you will implement a numerical approximation of heat dissipation through a surface (or volume). Using the heat equation and numerical approximation you will create a program that can take in any initial heat signature on a plane or 3d volume and iteratively compute the heat flow in a time series. Inputs: Initial temperature configuration and the thermal conductivity of the 2d material Desired Outputs: A time series or movie depicting the heat flow in time. Wave Equation Simulation Difficulty: Easy Synopsis: Simulate the 1, 2 or 3 dimensional wave equation by both using analytic results and numerical approximation. Inputs: An initial impulse or wave configuration Desired Outputs: A time series of images or a movie of the wave propagation. Non-Linear Dispersion Simulation Difficulty: Medium Synopsis: Inputs: Desired Outputs: Fluid Dynamics Simulation Difficulty: Hard Synopsis: Fluid dynamics constitutes a “hard” problem for mathematics since the differential equations thought to govern it can be easily written down but as of yet have no exact solutions. However, from the differential equations alone the behavior of fluids can be approximated and modeled extremely well. In this project you will use the shallow water equations to simulate sloshing of the surface of a fluid given and certain initial configuration. Inputs: A 2d matrix giving an initial height map for a surface of a liquid. Desired Outputs: A video stream simulating the relaxing a sloshing of the liquid given the initial condition. Motion De-Blurring Difficulty: Medium Synopsis: Inputs: Desired Outputs: Object Tracking Difficulty: Medium Synopsis: performing an analysis on a stream of video to flag the continuity of features between frames has many applications, from image stabilization to security camera tracking to programing a webcam to watch your experiment for you and text you when the mouse has reached the cheese. Object Tracking: Stabilization This project focuses either taking in several seconds of shaky video and performing cropping and rotations to align the frames. Inputs: A shaky video stream of a still image/still life Desired Outputs: A video stream of the same still life with the shaking removed. Object Tracking: Movement Tracking In this project, a stream of stable video with a moving object, say a thrown ball, will be analyzed. The moving objects should be highlighted and tracked across time. Inputs: A video stream with fixed background and moving foreground Desired Outputs: A video stream with moving objects highlighted Edge/Line/Corner Detection Difficulty: Easy Synopsis: Edge detection is one of the simplest but most important techniques in any kind of image analysis (I’m actually serious, I don’t think this would be considered controversial by anyone). Edge detection is the main tool that allows you to single out objects in a picture and separate features from background data. In this project you will implement at least one of the common edge detection algorithms and Inputs: Desired Outputs: Stereo Image Analysis Difficulty: Medium Synopsis: Inputs: Desired Outputs: Feature Classification Difficulty: Medium/Hard/Research Level Synopsis: The human eye takes in photons and transforms the information into nerve impulses. These nerve impulses seem to be optimized to detect different kinds of feature natural to our environment, in optical neuroscience one of the tools used to suss out which of these features our brain seems to prefer and which are left out and “assumed in postprocessing.” One of the current leading theories is that our eyes would be evolutionary adapted to encode image data from “natural images.” The point of this project would be to determine what that means, by finding an algorithm that can sort 100 pictures into “natural images” (like forests, grass, etc.) and “synthetic” images (pure noise, one tone, checker board, etc) Inputs: 50 pictures Desired Outputs: A classification of each picture as either “natural” or “unnatural.”