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This project aims to build a diagnostic task for an insect that is easy to image with simple computer vision techniques and then train a deep neural network to perform such a task. We will be using computer vision to extract key features that represent the images and train a deep neural network to identify these features. A public dataset of neuron imaging was created as a database. To validate the accuracy of the method, an auto-ID test performed using ten frames from 120 neurons from a living fly. The accuracy of this project was 95%. The next project was to develop an algorithm that was specifically designed to efficiently identify neurons of a certain type. The result was an accurate neural tagger that had a 96% agreement with manual annotation.

Once I started to understand the basics of the task, I started working on the cameras. The data collection for the devices started out being fairly crude and manual. A person would manually load real images of the animal into the computer and label it as a “captured image” or a “non-captured image”. The tutorial had the images and labels so I had no problems loading them in as well and and training the filter as it had the labels.

This project’s goal was to create and evaluate a learning-based method for the detection of color edges in a highly noisy input. Using both a simple filter and Convolutional Neural Network (CNN), we evaluated the effectiveness of the filter and CNN utilizing Chroma-space Mixture of Gaussians filter (CMOG) and Convolution neural network (CNN) on detection of color edges in color edge images.

One of the problems in practice is that of mislocalization. Attempting to predict real-world positions of objects when only presenting landmarks and their relation has been an area of intense research for decades. In the area of mobile robotics, the most challenging and difficult prediction problem is the prediction of the robot’s current position among other obstacles in its field of view. This problem has been identified as a long-standing research problem and has been particularly intriguing due to presenting both a research and a real-world application. d2c66b5586

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