Institut für Informatik und Computational Science
Refine
Year of publication
Document Type
- Article (679) (remove)
Keywords
- Didaktik (13)
- Ausbildung (12)
- Hochschuldidaktik (12)
- Informatik (12)
- Answer set programming (10)
- answer set programming (8)
- Answer Set Programming (6)
- Computer Science Education (4)
- E-Learning (4)
- Competence Measurement (3)
Institute
- Institut für Informatik und Computational Science (679)
- Institut für Physik und Astronomie (2)
- eLiS - E-Learning in Studienbereichen (2)
- Department Erziehungswissenschaft (1)
- Department Linguistik (1)
- Extern (1)
- Historisches Institut (1)
- Universitätsbibliothek (1)
- Zentrum für Qualitätsentwicklung in Lehre und Studium (ZfQ) (1)
Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps.