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閱讀筆記:Visual comfort assessment for stereoscopic images based on sparse coding with multi-scale dict
時間 2020-12-29
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Visual comfort assessment for stereoscopic images based on sparse coding with multi-scale dictionaries 題目:基於多尺度稀疏編碼字典的立體圖像舒適度評價 方法步驟:1.建立舒適度級別的多尺度字典2.估計測試樣本屬於每個級別的概率3.尋找概率對應的權重係數,求最後的舒適度分數 傳統方法的缺點:(傳統
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相關文章
1.
閱讀筆記:VISUAL COMFORT ASSESSMENT FOR STEREOSCOPIC 3D IMAGES BASED ON SALIENT DISCOMFORT REGIONS
2.
閱讀筆記:A Visual Comfort Assessment Approach of Stereoscopic Images based on Random Forest Regressor
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.
Visual saliency detection based on multiscale deep cnn features
9.
《A sparse annotation strategy based on attention-guided active learning for 3D medic》--閱讀筆記-Arxiv
10.
[論文]Light Field Compression with Disparity Guided Sparse Coding based on Structural Key Views
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