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patient-chart-visualizer

Visualize a patient's medical chart as a superposition of medical event objects on a Leap Motion interactive 3D human model.

patientnet

EECS498 Senior Design Project: HoloLens Telemedicine Application

person_following_bot

This is a deep learning based robot which follows a person in dynamic environment.

poc-hololens-bim360

Prototype application integrating BIM360 with HoloLens.

pointcloudrendering

Fast rendering of large point clouds in OpenGL through compute shaders.

polygonization-by-frame-field-learning

This repository contains the code for our fast polygonal building extraction from overhead images pipeline.

predicting-breast-cancer-malignancy-from-x-rays

Regarding the data, all 628 screening mammograms in this project have been classified as ab-normal by 2 radiologists and thus require a biopsy. Radiologists are cautious during screenings, the consequences of having a false negative push them to send women for biopsy if there is the slight-est doubt for them to have cancer. As explained, this results in a high number of false positive since the ‘cost’ is lower than having a false negative. The dataset used in this project comes from the OPTIMAM Medical Image Database, which collects NHS Breast Screening Programme (NHSB-SO) images in the UK. A deep learning approach is used to classify abnormal screenings as either malignant or benign cancer with a certain probability. Transfer Learning makes it possible to obtain high performances on small datasets. This project achieved a ROC of 80%, 86% sensitivity, and 77% NPV, which were reached with a pre-trained ResNet50v2, a state-of-the-art neural network optimized through fine-tuning hy-perparameters and data pre-processing.

projectaiai

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.

proper

Vision guided 3D Direct Laser Repair of Complex Metal Parts

pynite

A 3D structural engineering finite element library for Python.

pywallance

We propose a lightweight alternative to blockchain, called Wallance to secure interactions in a decentralized IoT infrastructure. Taking into account all its constraints as memory, computing power and energy, our proof of concept promotes the sharing of data and the utilization of services as a real IoT network, in a secure way.

qt-pyqt-pyside-custom-widgets

Awesome custom widgets made for QT Desktop Applications. Simplify your UI development process. These widgets can be used in QT Designer then imported to PySide code.

r2live

R2LIVE is a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses the measurement from the LiDAR, inertial sensor, visual camera to achieve robust, accurate state estimation.

r3live

A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package

real-time-abnormal-events-detection-and-tracking-in-surveillance-system

The main abnormal behaviors that this project can detect are: Violence, covering camera, Choking, lying down, Running, Motion in restricted areas. It provides much flexibility by allowing users to choose the abnormal behaviors they want to be detected and keeps track of every abnormal event to be reviewed. We used three methods to detect abnormal behaviors: Motion influence map, Pattern recognition models, State event model. For multi-camera tracking, we combined a single camera tracking algorithm with a spatial based algorithm.

real-time-car-parking-occupancy

This is the implementation of "Acharya, D., Yan, W., & Khoshelham, K. (2018) Real-time image-based parking occupancy detection using deep learning. In the proceedings of the 5th Annual Conference of Research@Locate, Adelaide, Australia, CEUR Workshop Proceedings, 2087: 33-40"

real2cad-3dv

Shape Matching of Real 3D Object Data to Synthetic 3D CADs (3DV project @ ETHZ)

realtimeamenagement

Augmented reality real time amenagement for furnitures

recent-stars-2020

:star: SLAM, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code

recursivestereouav

Obstacle Detection and Avoidance for Autonomous Unmanned Aerial Vehicles Using Recursive Filtering of 3D Point Clouds in Stereo Vision

relative3d

Code release for "3D-RelNet: Joint Object and Relation Network for 3D prediction"

retrospective-v8.2

Self-gated CINE MRI reconstruction. Features: 2D/3D, phased-array coils, CS & parallel imaging, L-curve, 3D variable-flip-angle for T1 mapping.

rgbd_tm

Robust Texture Mapping using RGB-D Cameras

road-boundary-detection-based-on-lidar

This algorithm is based on Velodye HDL 64E

road-crack-detection

robopilot

Live Dense Multi Modal 3D Mapping — A system designed for real time 3D reconstruction using a fusion of multiple depth and camera sensors simultaneously at real time speed — A Generic Framework for Distributed Deep Neural Networks over the Cloud, the Edge, and End Devices for Computer Vision Applications.

robot-operating-system-project-ros-

In this project, you I control a simulated Turtlebot 3 burger1 mobile robot in various environments, so as to make it navigate from one starting position to a goal position depending on the task to solve. The navigation will successively exploit: • an image obtained from a simulated camera to detect and follow a line, • a laser scan obtained from a simulated LDS to detected and avoid some obstacles, • and finally both of them to navigate in some challenging environment where both sensors are required

robotics-course-project

Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos

roch_gpsr

packages for Roch robot in GPSR(**机器人大赛家庭组) , including GPSR framework and the application of iFLYTEK(科大讯飞) voice technology.

ros-turtlebot-tutorial

Tutorial to run TurtleBot examples on ROS1 and ROS2