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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 321330 of 339 papers

TitleStatusHype
Hand Keypoint Detection in Single Images using Multiview BootstrappingCode0
ArtTrack: Articulated Multi-person Tracking in the WildCode0
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real TransferCode0
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud RegistrationCode0
An Effective Image Copy-Move Forgery Detection Using Entropy InformationCode0
GLAMpoints: Greedily Learned Accurate Match pointsCode0
GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative SpacecraftCode0
Data Distillation: Towards Omni-Supervised LearningCode0
FC-GNN: Recovering Reliable and Accurate Correspondences from InterferencesCode0
Self-Supervised 3D Keypoint Learning for Ego-motion EstimationCode0
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