Local Features - Revision history

BPeat at 17:41, 3 May 2023

2023-05-03T17:41:48Z

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2023-03-29T04:45:19Z

Text replacement - "http://" to "https://"

BPeat at 14:46, 26 March 2023

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BPeat at 15:24, 19 March 2023

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BPeat at 01:29, 1 July 2019

2019-07-01T01:29:56Z

BPeat at 01:16, 1 July 2019

2019-07-01T01:16:43Z

BPeat at 01:16, 1 July 2019

2019-07-01T01:16:12Z

BPeat at 01:14, 1 July 2019

2019-07-01T01:14:24Z

BPeat at 00:58, 1 July 2019

2019-07-01T00:58:38Z

BPeat at 00:58, 1 July 2019

2019-07-01T00:58:22Z

@@ Line 9: / Line 9: @@
 * [[Deep Features]]
-* [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
+* [[Attention]] Mechanism  ...[[Transformer]] ...[[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[Vision]]
 * [https://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Local+Features+machine+learning+artificial+intelligence YouTube search...]
+[https://www.youtube.com/results?search_query=Local+Features+machine+learning+artificial+intelligence YouTube search...]
-[http://www.google.com/search?q=Local+Features+machine+learning+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Local+Features+machine+learning+artificial+intelligence ...Google search]
 * [[Deep Features]]
 * [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
 * [[Vision]]
-* [http://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]
+* [https://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]
-* [http://www.semanticscholar.org/paper/Image-Retrieval-with-Deep-Local-Features-and-Noh-Araujo/0e04af52fc230986064994d47207074fe1bccaf2 Image Retrieval with Deep Local Features and Attention-based Keypoints |  H. Noh, A. Araujo, and B Han] DELF (DEep Local Feature)
+* [https://www.semanticscholar.org/paper/Image-Retrieval-with-Deep-Local-Features-and-Noh-Araujo/0e04af52fc230986064994d47207074fe1bccaf2 Image Retrieval with Deep Local Features and Attention-based Keypoints |  H. Noh, A. Araujo, and B Han] DELF (DEep Local Feature)
-Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. []http://www.mathworks.com/help/vision/ug/local-feature-detection-and-extraction.html Local Feature Detection and Extraction | MathWorks]
+Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. []https://www.mathworks.com/help/vision/ug/local-feature-detection-and-extraction.html Local Feature Detection and Extraction | MathWorks]
-http://opencv-python-tutroals.readthedocs.io/en/latest/_images/sift_scale_invariant.jpg
+https://opencv-python-tutroals.readthedocs.io/en/latest/_images/sift_scale_invariant.jpg
 <youtube>dlqn-wPvjxg</youtube>
@@ Line 23: / Line 23: @@
 == SIFT ==
-So, in 2004, D.Lowe, University of British Columbia, came up with a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, Distinctive Image Features from Scale-Invariant Keypoints, which extract keypoints and compute its descriptors. (This paper is easy to understand and considered to be best material available on SIFT. So this explanation is just a short summary of this paper). [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]
+So, in 2004, D.Lowe, University of British Columbia, came up with a new algorithm, Scale Invariant Feature Transform (SIFT) in his paper, Distinctive Image Features from Scale-Invariant Keypoints, which extract keypoints and compute its descriptors. (This paper is easy to understand and considered to be best material available on SIFT. So this explanation is just a short summary of this paper). [https://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>NPcMS49V5hg</youtube>
 == 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]
+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 https://papers.nips.cc/paper/7861-lf-net-learning-local-features-from-images.pdf | Y. Ono, E. Trulls, P Fua, and K.Yi]

@@ Line 10: / Line 10: @@
 * [[Deep Features]]
 * [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
-* [[Image Retrieval / Object Detection]]
+* [[Vision]]
 * [http://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]
 * [http://www.semanticscholar.org/paper/Image-Retrieval-with-Deep-Local-Features-and-Noh-Araujo/0e04af52fc230986064994d47207074fe1bccaf2 Image Retrieval with Deep Local Features and Attention-based Keypoints |  H. Noh, A. Araujo, and B Han] DELF (DEep Local Feature)

@@ Line 9: / Line 9: @@
 * [[Deep Features]]
-* [[Attention]]
+* [[Attention]] Mechanism  ...[[Transformer]] Model   ...[[Generative Pre-trained Transformer (GPT)]]
 * [[Image Retrieval / Object Detection]]
 * [http://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]

@@ Line 10: / Line 10: @@
 * [[Deep Features]]
 * [[Attention]]
 * [http://openreview.net/forum?id=SkfMWhAqYQ Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet | Wieland Brendel, Matthias Bethge]
 * [http://www.semanticscholar.org/paper/Image-Retrieval-with-Deep-Local-Features-and-Noh-Araujo/0e04af52fc230986064994d47207074fe1bccaf2 Image Retrieval with Deep Local Features and Attention-based Keypoints |  H. Noh, A. Araujo, and B Han] DELF (DEep Local Feature)

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	Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. []http://www.mathworks.com/help/vision/ug/local-feature-detection-and-extraction.html Local Feature Detection and Extraction \| MathWorks]		Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity. []http://www.mathworks.com/help/vision/ug/local-feature-detection-and-extraction.html Local Feature Detection and Extraction \| MathWorks]
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		+	http://opencv-python-tutroals.readthedocs.io/en/latest/_images/sift_scale_invariant.jpg

	<youtube>dlqn-wPvjxg</youtube>		<youtube>dlqn-wPvjxg</youtube>