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閱讀筆記:VISUAL COMFORT ASSESSMENT FOR STEREOSCOPIC 3D IMAGES BASED ON SALIENT DISCOMFORT REGIONS
時間 2021-07-12
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題目:VISUAL COMFORT ASSESSMENT FOR STEREOSCOPIC 3D IMAGES BASED ON SALIENT DISCOMFORT REGIONS 基於立體3D圖像顯著不舒適區域的視覺舒適度評估 舒適度分數的獲取方法:由顯著權重視差和最大視差加權的視差特徵向量獲得,其中顯著權重視差由色彩顯著圖和視差顯著圖得到 整體框圖: Sc獲取方法:GBVS基於圖像的視覺顯著
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相關文章
1.
閱讀筆記:A Visual Comfort Assessment Approach of Stereoscopic Images based on Random Forest Regressor
2.
閱讀筆記:Visual comfort assessment for stereoscopic images based on sparse coding with multi-scale dict
3.
閱讀筆記:Binocular Fusion Net: Deep Learning Visual Comfort Assessment for Stereoscopic 3D
4.
閱讀筆記:VISUALCOMFORTASSESSMENTOFSTEREOSCOPICIMAGES USINGDEEPVISUALANDDISPARITYFEATURESBAS
5.
閱讀筆記:Visual comfort evaluated by hue asymmetries in stereoscopic images
6.
閱讀筆記:3D visual discomfort predictor based on subjective perceived-constraint sparse representation
7.
論文閱讀筆記---MSTGAR: Multioperator-Based Stereoscopic Thumbnail Generation With Arbitrary Resolution
8.
NIMA:Neural Image Assessment論文閱讀筆記
9.
閱讀筆記:3-D Visual Discomfort Assessment Considering Optical and Neural Attention Models
10.
A Simple Pooling-Based Design for Real-Time Salient Object Detection閱讀筆記
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