Difference between revisions of "Local Features"
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* [[Deep Features]] | * [[Deep Features]] | ||
* [[Attention]] | * [[Attention]] | ||
| − | * Scale- | + | * [[http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html Introduction to SIFT (Scale-Invariant Feature Transform) | OpenCV] |
<youtube>dlqn-wPvjxg</youtube> | <youtube>dlqn-wPvjxg</youtube> | ||
Revision as of 19:58, 30 June 2019
YouTube search... ...Google search
LF-Net
LF-Net has two main components. The first one is a dense, multi-scale, fully convolutional network that returns keypoint locations, scales, and orientations. It is designed to achieve fast inference time, and to be agnostic to image size. The second is a network that outputs local descriptors given patches cropped around the keypoints produced by the first network. We call them detector and descriptor. ...presented a novel deep architecture and a training strategy to learn a local feature pipeline from scratch, using collections of images without the need for human supervision ... propose a sparse-matching method with a novel deep architecture, which we name LF-Net, for Local Feature Network, that is trainable end-to-end and does not require using a hand-crafted detector to generate training data. ...local features have played a crucial role in computer vision, becoming the de facto standard for wide-baseline image matching http://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images.pdf | Y. Ono, E. Trulls, P Fua, and K.Yi]
