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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

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Showing 191–200 of 339 papers

TitleStatusHype
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images—0
Human Pose Estimation in Monocular Omnidirectional Top-View Images—0
From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection—0
Semantic Image Attack for Visual Model Diagnosis—0
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty—0
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection—0
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training—0
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint DetectionCode0
Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection—0
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models—0
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