Refine
Year of publication
Document Type
- Conference Proceeding (229)
- Bachelor Thesis (100)
- Article (99)
- Master's Thesis (34)
- Part of a Book (27)
- Report (20)
- Book (17)
- Part of Periodical (13)
- Contribution to a Periodical (8)
- Doctoral Thesis (7)
Language
- English (287)
- German (275)
- Multiple languages (4)
Keywords
- Hochschule Ruhr West (9)
- Zeitschrift (9)
- Fachhochschule (8)
- Mülheim an der Ruhr (8)
- Intergenerational Collaboration (3)
- Intergenerational Innovation (3)
- Sentiment Analysis (3)
- Usability (3)
- Automotive HMI (2)
- Digitalisierung (2)
Institute
- Fachbereich 1 - Institut Informatik (372)
- Fachbereich 4 - Institut Mess- und Senstortechnik (96)
- Fachbereich 2 - Wirtschaftsinstitut (54)
- Fachbereich 1 - Institut Energiesysteme und Energiewirtschaft (16)
- Fachbereich 3 - Institut Bauingenieurwesen (11)
- Fachbereich 3 - Institut Maschinenbau (5)
- Fachbereich 4 - Institut Naturwissenschaften (3)
A light-weight real-time ap- plicable hand gesture recognition system for automotive applications
(2015)
We present a novel approach for improved hand-gesture recognition by a single time-of-flight(ToF) sensor in an automotive environment. As the sensor's lateral resolution is comparatively low, we employ a learning approach comprising multiple processing steps, including PCA-based cropping, the computation of robust point cloud descriptors and training of a Multilayer perceptron (MLP) on a large database of samples. A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps. Overall results are very satisfactory when evaluated on a large benchmark set of ten different hand poses, especially when it comes to generalization on previously unknown persons.
In this paper we describe a session management system for setting up various collabora- tive classroom ,scenarios. The approach ,is addressing the additional workload ,of administrating classroom networks on the teacher, which is an important aspect for teachers' willingness to im- plement technology enhanced,learning in schools. The system facilitates preparation of classroom scenarios and the adhoc installation of networked collaborative sessions. We provided a graphical interface, which is usable for administration, monitoring, and for specification of a wide variety of different classroom ,situations with group work. The resulting graphical specifications are well suited to be re-used in the more formal learning design format IMS/LD; this is achieved by a auto- matable transformation of the scenarios to LD documents. Keywords: Collaborative classroom scenarios, lightweight classroom orchestration, learning de- sign, shared workspaces.
We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.
Knowledge of fundamentals of human-computer interaction resp. usability engineering is getting more and more important in technical domains. However this interdisciplinary field of work and corresponding degree programs are not broadly known. Therefore at the Hochschule Ruhr West, University of Applied Sciences, a program was developed to give teen-aged pupils insights into this area in a project-based learning environment with professional tools. Within the last 18 month this project was successfully conducted several times with participants of different age.
We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios.
We present a system for efficient dynamic hand gesture recognition based on a single time-of-flight sensor. As opposed to other approaches, we simply rely on depth data to interpret user movement with the hand in mid-air. We set up a large database to train multilayer perceptrons (MLPs) which are subsequently used for classification of static hand poses that define the targeted dynamic gestures. In order to remain robust against noise and to balance the low sensor resolution, PCA is used for data cropping and highly descriptive features, obtainable in real-time, are presented. Our simple yet efficient definition of a dynamic hand gesture shows how strong results are achievable in an automotive environment allowing for interesting and sophisticated applications to be realized.
We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.
The influence of national culture on knowledge sharing has important implications for all organizations. However, the existing frameworks only cover a subset of relevant factors or limit the research of the framework to either organizational or national level. Hence, a more encompassing framework is needed. The question this articles answers is how does national culture influence knowledge sharing. Based on extensive literature review and interviews carried out in Finland and Japan, this article sets forth a foundation for a new framework. The framework details how national culture influences individual level and organizational level factors and technical tools. Additionally, the framework includes a new dimension, time-dimension, which is usually disregarded in knowledge sharing research. For researchers and practitioners, the derived framework provides key insight on relevant factors on knowledge sharing and national culture. Finally, future research directions are discussed.
We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.