The Use of Multiple Cues and Contextual Knowledge in Computer Vision
Aysun Kocak, Bora Celikkale, Kemal Cizmeciler, Levent Karacan, Mehmet Gunel, Yasin Kavak
Oct, 2012 - Oct, 2015 (3 years)
This project will explore the influences of visual context and multiple cues on a number of computer vision problems. First, a novel visual saliency or attention model will be developed towards a direction that combines information coming from multiple cues with the contextual knowledge. The goal is to come up with a model that can effectively predict where people look in an image. In the second part of the project, we will investigate the problem of image filtering with a focus on devising appropriate ways of extracting high-level contextual knowledge for filtering and using them to guide the ongoing image smoothing process. The third part of the project will be about developing adaptive approaches to image segmentation that integrates information obtained from multi cues. A novel and effective segmentation algorithm will be developed that adaptively combines high-level prior knowledge with the information obtained from different visual cues at different scales.
Sponsors: The Scientific and Technological Research Council of Turkey (TUBITAK) Career Development Program (Award# 112E146)