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)
Alpha Matting with KL-Divergence Based Sparse SamplingIEEE Transactions on Image Processing
Levent Karacan, Aykut Erdem, and Erkut ErdemA Region Covariances-based Visual Attention Model for RGB-D ImagesInternational Journal of Intelligent Systems and Applications in Engineering, Vol. 4, No. 4., pp. 128-134, October 2016
Erkut ErdemStructure-Texture Decomposition of RGB-D ImagesInternational Journal of Intelligent Systems and Applications in Engineering, Vol. 4, No. 4., pp. 111-118, October 2016
Aykut ErdemImage Matting with KL-Divergence Based Sparse SamplingIEEE International Conference on Computer Vision (ICCV 2015), Santiago, Chile, December 2015
Levent Karacan, Aykut Erdem, Erkut ErdemStructure Preserving Image Smoothing via Region CovariancesACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2013), 32(6), November 2013.
Levent Karacan, Erkut Erdem, Aykut Erdem