A
Asmaa Hosni
Researcher at Vienna University of Technology
Publications - 10
Citations - 2034
Asmaa Hosni is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Filter (signal processing) & Image segmentation. The author has an hindex of 7, co-authored 10 publications receiving 1844 citations.
Papers
More filters
Proceedings ArticleDOI
Fast cost-volume filtering for visual correspondence and beyond
TL;DR: This paper proposes a generic and simple framework comprising three steps: constructing a cost volume, fast cost volume filtering and winner-take-all label selection, and achieves state-of-the-art results that achieve disparity maps in real-time, and optical flow fields with very fine structures as well as large displacements.
Journal ArticleDOI
Fast Cost-Volume Filtering for Visual Correspondence and Beyond
TL;DR: This work proposes a generic and simple framework comprising three steps: constructing a cost volume, fast cost volume filtering, and 3) Winner-Takes-All label selection that achieves 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements.
Proceedings ArticleDOI
Local stereo matching using geodesic support weights
TL;DR: The proposed algorithm is the top performer among local stereo methods at the current state-of-the-art in local stereo matching by using the geodesic distance transform.
Journal ArticleDOI
Secrets of adaptive support weight techniques for local stereo matching
TL;DR: An extensive evaluation study of different strategies for computing adaptive support weights in local stereo matching, including the original bilateral filter-based weights, as well as more recent approaches based on geodesic distances or on the guided filter.
Proceedings ArticleDOI
REal-time local stereo matching using guided image filtering
TL;DR: This paper proposes the first local method which is both fast (real-time) and produces results comparable to global algorithms, and uses the recently proposed guided filter to overcome the limitation of bilateral filtering.