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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 281290 of 339 papers

TitleStatusHype
R2D2: Reliable and Repeatable Detector and DescriptorCode0
Simple and Lightweight Human Pose EstimationCode0
ViewSynth: Learning Local Features from Depth using View SynthesisCode0
Human Keypoint Detection by Progressive Context RefinementCode0
Distribution-Aware Coordinate Representation for Human Pose EstimationCode0
kPAM-SC: Generalizable Manipulation Planning using KeyPoint Affordance and Shape Completion0
Pose Neural Fabrics SearchCode0
Learning Enhanced Resolution-wise features for Human Pose Estimation0
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila0
A Comparison of CNN and Classic Features for Image Retrieval0
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