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Computer Vision
research related to computer vision
Neuroinformatics Group
Universität Bielefeld › Technische Fakultät › NI
Understanding and reasoning about physics is an important ability of intelligent agents. We are developing an AI agent capable of solving physical reasoning tasks. If you would like to know more about this project/thesis opportunity, check the websites [1][2] or contact Dr. Andrew Melnik.
The visual detection and recognition of human actions by technical systems is a fundamental problem with many applications the human-computer interaction domain. Activities involving hand-object interactions and action sequences in goal-oriented tasks, such as manufacturing work, pose a particular challenge. We use deep learning to detect and recognize such actions in real-time, and we propose an assistance system that provides the user with feedback and guidance based on the recognized actions.
Computer Vision and processing of multimodal sensor data is very important to take Smart Homes to the next level. An intelligent everyday environment should be aware of its residents. It should understand their actions and ideally even be able to predict their behavior. In the 
A major pre-requisite for many robotics tasks is to identify and localize objects within scenes. Our model-free approaches to scene segmentation employs RGBD cameras to segmented highly cluttered scenes in real-time (30 Hz). To this end, we first identify smooth object surfaces and subsequently combine them to form object hypotheses employing basic heuristics such has convexity, shape alignment and color similarity.
While language provides us with a concise code capturing much of the movement complexity of our mouth, we still lack a comparable representation for the movement of our hands. This project aims to create a database of human hand interaction patterns from a variety of multimodal data sources. An associated goal is to develop methods for the clustering of captured trajectory data into physics-based models of manual interaction. We hope that the resulting database can make a contribution towards a better grounding of control strategies for anthropomorphic robot hands and develop for robotics a similar utility as the WordNet database has for linguistics.