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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 276–300 of 339 papers

TitleStatusHype
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing—0
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models—0
On Mutual Information in Contrastive Learning for Visual Representations—0
On the Visibility of Point Clouds—0
Orthogonal Decomposition Network for Pixel-Wise Binary Classification—0
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection—0
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection—0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing—0
Processing and Segmentation of Human Teeth from 2D Images using Weakly Supervised Learning—0
RADA: Robust and Accurate Feature Learning with Domain Adaptation—0
RDD: Robust Feature Detector and Descriptor using Deformable Transformer—0
Reading a Ruler in the Wild—0
Real-time Detection of 2D Tool Landmarks with Synthetic Training Data—0
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks—0
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle—0
Refinement Module based on Parse Graph of Feature Map for Human Pose Estimation—0
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Rewards-based image analysis in microscopy—0
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy—0
Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance—0
Rotation-Equivariant Keypoint Detection—0
SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras—0
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion—0
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images—0
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