學習筆記：LAB Feature with Feature-centric Cascade for Fast and Accurate Face Detection
該筆記只是用於知識總結，且本人第一次寫博客。請多擔待。 LAB特徵是中科院計算所山世光研究員團隊提出來的，主要用於人臉檢測。 LAB的概念：全稱是Locally Assembled Binary ，主要是將Haar特徵按照LBP特徵的方式進行提取。也就是黑色矩形的像素減去白色矩形的像素的結果如果大於0就令該特徵爲1，否則爲0。公式如下： Haar特徵計算方法： σ 是候選窗口x的方差。 組合的方式
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相關文章
- 1. cascade learning 級聯學習 learning SURF Cascade for Fast band Accurate Object Detection
- 2. RCNN學習筆記(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 深度學習論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
- 6. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. 《Receptive Field Block Net for Accurate and Fast Object Detection》論文筆記
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- 9. Rich feature hierarchies for accurate object detection and semantic segmentation (RCNN)筆記
- 10. 論文：Rich feature hierarchies for accurate object detection and semantic segmentation 個人筆記