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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 8190 of 339 papers

TitleStatusHype
DexYCB: A Benchmark for Capturing Hand Grasping of ObjectsCode1
Explicit Box Detection Unifies End-to-End Multi-Person Pose EstimationCode1
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint EstimatesCode1
Keypoint CommunitiesCode1
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
Learning Delicate Local Representations for Multi-Person Pose EstimationCode1
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and FusionCode1
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual LocalizationCode1
MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose EstimationCode1
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion ApproachCode1
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