Held in conjunction with ICPR 2021; Milan, Italy, Januari 10th-15th 2021.
Higher dimensional data such as 3D, video, and simulation data are a leading edge of pattern recognition research. With the growth of prevalent application areas such as 3D games, self-driving automobiles, automobile and airplane design, health monitoring and sports activity training, a wide variety of new sensors and simulation techniques have allowed researchers to develop feature description models beyond 2D. In this workshop, we will present an overview and key insights into the state of the art of higher dimensional features from a wide variety of techniques including but not limited to deep learning and also traditional approaches. For example, numerous current pattern recognition methods are using 3D information from the sensor (e.g. KINECT, LIDAR, MRI, …) or are using 3D in modeling and understanding the 3D world. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms.
Submission deadline: October 10th 2020
Workshop author notification: November 10th 2020
Camera-ready submission: November 15th 2020
Finalized workshop program: December 1st 2020
All accepted papers are published in the Springer Workshop proceedings. There will be no printed version.
All papers should adhere to the Springer proceedings guidelines and templates.
Head of the Natural Computing group
Head of the Deep Learning and Computer Vision group
Chief Scientist, HRI-EU