閱讀筆記:Binocular Fusion Net: Deep Learning Visual Comfort Assessment for Stereoscopic 3D
題目：雙目融合網絡：立體舒適度評估的深度學習 深度網絡：BFN：雙目融合深度網絡，學習雙目特徵；DRN：視差正則化網絡，提升預測結果； 整體框圖： 訓練： 測試： CNN的主要組成：卷積層，下采樣層，激活層，全連接層； 數據庫：MPI-Sintel和IEEE-SA 實驗過程：用MPI訓練DRN，獲取初始參數，利用該參數在IEEE上訓練。將DRN和BFN端到端的連接，十則交叉驗證。 實驗結果： 1.
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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：VISUALCOMFORTASSESSMENTOFSTEREOSCOPICIMAGES USINGDEEPVISUALANDDISPARITYFEATURESBAS
- 3. 閱讀筆記：Visual comfort assessment for stereoscopic images based on sparse coding with multi-scale dict
- 4. 閱讀筆記：A Visual Comfort Assessment Approach of Stereoscopic Images based on Random Forest Regressor
- 5. 閱讀筆記：Visual comfort evaluated by hue asymmetries in stereoscopic images
- 6. Stereoscopic image quality assessment combining statistical features and binocular theory
- 7. Deep Residual Learning for Image Recognition閱讀筆記
- 8. Deep Reinforcement Learning for Dialogue Generation閱讀筆記
- 9. deep learing解決3D圖像質量評價（image quality assessment）問題
- 10. 文獻閱讀筆記5 On the Use of Deep Learning for Blind Image Quality Assessment（2D）