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Keypoint Detection

Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Showing 151160 of 339 papers

TitleStatusHype
Monocular 3D Object Detection via Geometric Reasoning on Keypoints0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines0
Efficient grouping for keypoint detection0
A Comparison of CNN and Classic Features for Image Retrieval0
Joint Object Contour Points and Semantics for Instance Segmentation0
EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy0
An analysis of the factors affecting keypoint stability in scale-space0
Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection0
Do Convnets Learn Correspondence?0
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