“Data-driven Visual Computing” Chaired by
Conference:
Type(s):
Title:
- Data-driven Visual Computing
Presenter(s)/Author(s):
Abstract:
With the rapid development of acquisition techniques and machine learning methods, data-driven visual computing has been receiving more and more attention in recent years. The main task of data-driven visual computing is aggregating information a large collection of images or models, learning semantic information from the collection and utilizing learned knowledge to support higher level tasks of understanding, processing, and even novel data generation. The generated or processed data, typically possessing semantic information, can be used to enrich the input data sets and enhance the learning tasks in future, forming a data-driven visual computing loop which can boost the emergence of “big visual data”. In this course, we will talk about some recent developments of data-driven visual computing, from both graphics and vision community. Specifically, we will introduce the recent advances on image-driven smart image processing and manipulation, data-driven 3D shape analysis and modeling. We will also cover joint analysis and processing of 2D and 3D data.
Additional Information:
Level
Intermediate
Prerequisites
Basic background in geometry processing and image processing. Basic working knowledge on data analysis and machine learning.
Intended Audience
Graduate students, practitioners, and researchers interested in geometry and image processing and, in particular, methods based on large data collections.
