We invite authors to submit manuscripts on topics related to computer vision for robotics, related to deep learning. We welcome work showing new learning algorithms, applications, and deployed vision systems, as well as, providing insights into the capabilities and limitations of such approaches.
Call For Papers: Special Issue on Deep Learning for Robotic Vision
Submission guidelines:
Manuscripts should be submitted to: http://www.editorialmanager.com/visi/
Please indicate in the cover letter that your submission is for the special issue for “Deep Learning for Robotic Vision”. Detailed instructions are available on the IJCV website.
Topics include:
- Deep learning for robotics vision.
- DNN based object recognition, detection and segmentation for robotics, especially under open-set or detrimental conditions.
- End-to-end perception algorithms.
- Deep learning in navigation and autonomous driving
- Vision-based Semantic Simultaneous Localization and Mapping (SLAM).
- 3D Scene understanding.
- Semi-supervised and self-supervised learning for robotics.
- Real-time algorithms for robotics perception.
- Deep learning in human-robot interaction, learning from human demonstrations
- Lifelong and continuous deep learning in robotics
- Active learning and active perception
- Meta learning for robotic vision
- Perception algorithms deployed on various robotic systems.
- Reliable confidence measures for deep classifiers, Bayesian Deep Learning, and uncertainty estimation.
- Deep learning for embedded systems and platforms with limited computational power
- Deep learning for smart environments
- Deep learning applications for the visually impaired and for the ageing society
- Deep learning vision algorithms deployed in real-life robotic systems.
- Large-scale datasets relevant to robotics learning.
- Data collection and simulation challenges for robotics perception and control.
- Limits of deep learning for robotic vision
Manuscript submission: http://www.editorialmanager.com/visi/
Submission deadline: July 18, 2018
Guest Editors:
Anelia Angelova - Google Brain / Google Research
Gustavo Carneiro - University of Adelaide
Niko Sünderhauf - Queensland University of Technology
Jürgen Leitner - Queensland University of Technology