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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 141150 of 339 papers

TitleStatusHype
Centroid Distance Keypoint Detector for Colored Point CloudsCode1
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation0
Long-Lived Accurate Keypoints in Event Streams0
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip0
MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis0
Translating a Visual LEGO Manual to a Machine-Executable Plan0
Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network0
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples0
Human keypoint detection for close proximity human-robot interaction0
Semi-supervised Human Pose Estimation in Art-historical ImagesCode0
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