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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
Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications0
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D FeaturesCode1
A Graph-Based Approach for Category-Agnostic Pose EstimationCode2
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage RefinementCode1
Video-based Sequential Bayesian Homography Estimation for Soccer Field RegistrationCode0
Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning0
3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud0
TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply ChainsCode0
X-Pose: Detecting Any KeypointsCode2
Open-Vocabulary Animal Keypoint Detection with Semantic-feature MatchingCode0
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