Pre-Training with Fractal Images Facilitates Learned Image Quality Estimation
Current image quality estimation relies on data-driven approaches, however the scarcity of annotated data poses a bottleneck. This paper introduces a novel pre-training approach utilizing synthetic fractal images. The proposed method is tested on...
A Differentiable Gaussian Prototype Layer for Explainable Fruit Segmentation
We introduce a GMM Layer for gradient-based prototype learning. It is used to cluster feature vectors by computing their probabilities for each gaussian and using the soft cluster assignment for prediction. Hence prototypical image regions can be...
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation
We introduce the Concept Relevance Propagation (CRP) approach, which combines the local and global perspectives and thus allows answering both the ‘where’ and ‘what’ questions for individual predictions. We demonstrate the capability of our...
When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review
Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is...
Surgical Phase Recognition for different hospitals
Surgical phase recognition is an important aspect of surgical workflow analysis, as it allows an automatic analysis of the performance and efficiency of surgical procedures. A big challenge for training a neural network for surgical phase...
Hybrid semantic clustering of 3D point clouds in construction
In this work, we present an artificial intelligence (AI)-based semantic segmentation approach for three-dimensional (3D) point clouds which were generated from 2D images with a structure from motion (SfM) pipeline. We utilize state-of-the-art...
3D Hyperspectral Light-Field Imaging: a first intraoperative implementation
Hyperspectral imaging is an emerging technology that has gained significant attention in the medical field due to its ability to provide precise and accurate imaging of biological tissues. The current methods of hyperspectral imaging, such as...
From Multispectral-Stereo to Intraoperative Hyperspectral Imaging: a Feasibility Study
Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method...
Video-Driven Animation of Neural Head Avatars
We present a new approach for video-driven animation of high-quality neural 3D head models, addressing the challenge of person-independent animation from video input.In order to achieve person-independent animation from video input, we introduce...
Unsupervised learning of style-aware facial animation from real acting performances
This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for...