Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters(2019)(十三)
本文要解決的問題： 本文主要解決 detector 問題，爲什麼要解決這個問題呢？目前已知的detector的 可重複性檢測不魯棒 作者分析是沒有像fast等這樣使用像素的梯度信息，因此本文提出了一種 新的方法解決detector問題。 本文主要的貢獻： 提出了一種融合傳統detector方法和deeplearning的方法的特徵det
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相關文章
- 1. HPatches A benchmark and evaluation of handcrafted and learned local descriptors——2017.04
- 2. saliency detection by forward and backward cues in deep-cnn
- 3. AdaLAM: Revisiting Handcrafted Outlier Detection
- 4. GoodPoint: unsupervised learning of keypoint detection and description∗
- 5. CornerNet-Lite: Efficient Keypoint-Based Object Detection
- 6. CenterNet: Keypoint Triplets for Object Detection 論文學習
- 7. ExtremeNet:Bottom-up Object Detection by Grouping Extreme and Center Points
- 8. face detection[Face R-CNN]
- 9. 論文學習筆記（十） Bottom-up Object Detection by Grouping Extreme and Center Points
- 10. SIFT: Theory and Practice - Keypoint orientations（轉）