Feature Detector & Descriptor(三)
提取到Feature point以后，如何match？ 这就需要描述子(匹配比对的根据)了。 描述子要足够鲁棒，对光照和几何不敏感。 请看下面最简单的描述方法： 这样可以做match，前提最好不要有像素强度、光照、几何变化。 当我们用梯度来描述时，可以对像素shift鲁棒。 或者用像素梯度图。(丢掉像素的空间位置)，如下 优点是：可以抵抗旋转变化。 缺点是：丢掉太多信息，描述性不够准确。 接
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相关文章
- 1. Feature Detector & Descriptor(二)
- 2. FSSD: Feature Fusion Single Shot Multibox Detector
- 3. Feature Enhancement Network: A Refined Scene Text Detector
- 4. 【论文学习】Feature Pyramid Based Scene Text Detector
- 5. Working hard to know your neighbor’s margins: Local descriptor learning loss(2018)(三)
- 6. 2D Features framework6(Feature Description )
- 7. Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level(2020)(八)
- 8. 【论文笔记】R2D2: Repeatable and Reliable Detector and Descriptor
- 9. [论文解读]R2D2: Reliable and Repeatable Detector and Descriptor
- 10. 2D Features framework7(Feature Matching with FLANN )