NR-SIQA Using Convolutional Neural Network for Adaptive Feature Extraction
摘要： 在本文提出的算法中，使用CNN根据图像的质量分数将图像进行分类处理，得到网络高层所提取到的视觉感知特征。然后使用视觉显著模型将提取的左右视图特征进行融合，同时多尺度下的视差图统计特征被提取出来，最终综合以上特征，使用SVR进行训练。 方法详述 首先，一个CNN模型从零开始训练，自适应地提取图像质量感知特征。使用这个模型，从左右视图中提取感知特征，然后左右视图基于显著性权重进行融合得到融合特
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相关文章
- 1. RCNN(Regions with Convolutional Neural Network Feature)
- 2. Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
- 3. Convolutional Neural Network - Object Detection
- 4. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
- 5. 理解：Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
- 6. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 7. Convolutional Neural Network
- 8. Convolutional Neural Network For Sentence Classification
- 9. Synaptic Strength For Convolutional Neural Network
- 10. Ensemble Neural Relation Extraction with Adaptive Boosting