H
Haibin Ling
Researcher at Stony Brook University
Publications - 434
Citations - 28262
Haibin Ling is an academic researcher from Stony Brook University. The author has contributed to research in topics: Computer science & Video tracking. The author has an hindex of 72, co-authored 383 publications receiving 20858 citations. Previous affiliations of Haibin Ling include Peking University & Temple University.
Papers
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Journal ArticleDOI
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Xiaojie Guo,Yu Li,Haibin Ling +2 more
TL;DR: Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.
Journal ArticleDOI
Shape Classification Using the Inner-Distance
Haibin Ling,David W. Jacobs +1 more
TL;DR: It is suggested that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts.
Proceedings ArticleDOI
Real time robust L1 tracker using accelerated proximal gradient approach
TL;DR: This paper proposes an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers and a very fast numerical solver is developed to solve the resulting ℓ1 norm related minimization problem with guaranteed quadratic convergence.
Journal ArticleDOI
Robust Visual Tracking and Vehicle Classification via Sparse Representation
Xue Mei,Haibin Ling +1 more
TL;DR: This paper proposes a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework and extends the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes.
Proceedings ArticleDOI
Robust visual tracking using ℓ 1 minimization
Xue Mei,Haibin Ling +1 more
TL;DR: In this paper, a robust visual tracking method was proposed by casting tracking as a sparse approximation problem in a particle filter framework, where each target candidate is sparsely represented in the space spanned by target templates and trivial templates.