Overview Monocular visual odometry LIDAR based odometry Automotive occupancy mapping Object detection and tracking Obstacle detection based on 3D imaging 3D scene mapping Low level point cloud processing Multimodal sensor fusion High dynamic range video capture Real-time sensor data processing for autonomous vehicles using Quasar Monocular visual odometry Current consumer vehicle navigation relies on Global Navigation Satellite Systems (GNSS) such as GPS, GLONASS and Galileo.
Environmental perception systems for autonomousvehicles are often built using heterogeneous technologies thatoperate in a sequential manner. In the task of object trackingin particular, where the classical detector-tracker interactionis a serial …
Depth images generated by direct projection of LiDAR point clouds on the image plane suffer from a great level of sparsity which is difficult to interpret by classical computer vision algorithms. We propose a method for completing sparse depth images …
Image/video quality assessment
Overview Video and image quality assessment Content-aware video quality assessment Color differences Methodological considerations for subjective QA studies Video and image quality assessment Quality assessment (QA) consists of measuring the user’s subjective opinion of perceived image/video quality, the user’s quality preferences, or the utility of the images for a specific task. The goal of QA is to evaluate and compare imaging systems, and to help drive system design (e.
Topics in image and video restoration at imec-IPI-Ghent University Overview High Dynamic Range imaging Denoising of time-of-flight depth images and sequences Multicamera image fusion Non-local image reconstruction Multiframe superresolution Demosaicing Error Concealment Wavelet-based denoising of images Non-local means denoising of images Restoration of historical videos Joint removal of blocking artifacts and resolution enhancement High Dynamic Range Imaging Conventional display and image capture technology is limited to a narrow range of luminosities and images associated with such systems have been retroactively called Low Dynamic Range images(LDR).
Overview Hyperspectral Image Restoration Hyperspectral Multi-sensor Data Fusion Spatial Information Modelling Image classification in hyperspectral images Hyperspectral Image Restoration Despite advances in sensor technology, hyperspectral (HS) images are inevitably degraded by noise and blur, which can affect information retrieval and content interpretation. Using denoising and deblurring as a preprocessing tool will improve various post-processing tasks, e.g. classification, target detection, unmixing, etc.
We propose a novel restoration algorithm for HS images.