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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 311–320 of 339 papers

TitleStatusHype
Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network—0
Deep Alignment Network: A convolutional neural network for robust face alignmentCode0
Generative Partition Networks for Multi-Person Pose EstimationCode1
Hand Keypoint Detection in Single Images using Multiview BootstrappingCode0
Interspecies Knowledge Transfer for Facial Keypoint DetectionCode0
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection—0
Mask R-CNNCode1
6-DoF Object Pose from Semantic KeypointsCode0
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors—0
Towards Accurate Multi-person Pose Estimation in the Wild—0
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