Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks

proposed a multi-channel deep convolutional neural network (MCDCNN) [20] to utilize the features learned from different channels for multivariate time series classification.

In this paper, we have presented an aliasing theory of shadow mapping. A generalized representation of aliasing errors are derived from solid mathematical derivations.Given the

Unlike these two techniques, we restrict ourselves to a single color sample per pixel, but allow multiple geometric subsamples (positions and normals).. Similar approaches

As the question of efficiently using deep Convolutional Neural Networks (CNNs) on 3D data is still a pending issue, we propose a framework which applies CNNs on multiple 2D image

Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models.. Neural rendering is

Deferred shading [ST90] is a rendering technique that aims to in- crease the usable number of light sources in a scene or reduce the computational cost of lighting in case of

We build on a state-of-the-art SVBRDF capture deep network [DAD ∗ 18], which we re-train to take as input a single image captured under environment lighting, and output SVBRDF

Keywords: Annotated image dataset, Deep neural networks, Fish detection, Fish species recognition, Marine aquaculture applications..