J
Junle Wang
Researcher at Centre national de la recherche scientifique
Publications - 39
Citations - 476
Junle Wang is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Computer science & Stereoscopy. The author has an hindex of 10, co-authored 30 publications receiving 417 citations. Previous affiliations of Junle Wang include Tencent & South China University of Technology.
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Journal ArticleDOI
Saliency Detection for Stereoscopic Images
TL;DR: A new stereoscopic saliency detection framework based on the feature contrast of color, intensity, texture, and depth, which shows superior performance over other existing ones in saliency estimation for 3D images is proposed.
Proceedings ArticleDOI
Quantifying the relationship between visual salience and visual importance
TL;DR: It is found that the vast majority of early gaze position samples were made on the main subjects, suggesting that a possible strategy of early visual coding might be to quickly locate the main subject(s) in the scene.
Journal ArticleDOI
Comparative Study of Fixation Density Maps
Ulrich Engelke,Hantao Liu,Junle Wang,P. Le Callet,Ingrid Heynderickx,Hans-Jurgen Zepernick,Anthony Maeder +6 more
TL;DR: It is shown that the FDM are very similar and that their impact on the applications is low, but the individual experiment comparisons are found to be significantly different, showing that inter-laboratory differences strongly depend on the experimental conditions of the laboratories.
Proceedings ArticleDOI
Quantifying how the combination of blur and disparity affects the perceived depth
TL;DR: The influence of a monocular depth cue, blur, on the apparent depth of stereoscopic scenes will be studied and it is found that when blur is added to the background of the image, the viewer can perceive larger depth comparing to the images without any blur.
Journal ArticleDOI
Saliency-based stereoscopic image retargeting
TL;DR: Experimental results have shown that both the stereoscopic saliency detection and image retargeting methods can obtain better performance than the existing related methods on the public databases.