Now showing items 1-5 of 5
Acoustic Event Classification Using Convolutional Neural Networks
The classification of human-made acoustic events is important for the monitoring and recognition of human activities or critical behavior. In our experiments on acoustic event classification for the utilization in the sector of health care, we defined different acoustic events which represent critical events for elderly ...
Fast and accurate creation of annotated head pose image test beds as prerequisite for training neural networks
In this paper we present an experimental setup consisting of 36 cameras on 4 height levels covering more than half space around a centrally sitting person. The synchronous image release allows to build a 3D model of the human torso in this position. Using this so-called body scanner we recorded 36 different positions ...
WS34 - Deep Learning in heterogenen Datenbeständen
Deep learning techniques, especially artificial neural networks, have become irreplaceable in almost every aspect of modern information science. Breakthrough technologies evolve rapidly, driven by researchers with both, scientific and economic backgrounds. This workshop is a platform for students, post-docs, innovative ...
Preparing clinical ophthalmic data for research application
This paper presents an analysis of clinical examination, diagnostic and patient data belonging to persons with eye diseases like age-related macular degeneration (AMD). Our purpose is to investigate potential correlations of extracted features to discover their impacts on the disease. This is a first step to the ...
Evaluation of CNN-based algorithms for human pose analysis of persons in red carpet scenarios
We evaluate two CNN-based algorithms for keypoint-based human pose analysis on two image test sets containing red carpet scenarios, one taken under controlled conditions in a TV studio environment and another more heterogeneous data set taken from FlickR without any restriction but to contain a red carpet. We focus on ...