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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 261–270 of 339 papers

TitleStatusHype
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud RegistrationCode0
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real TransferCode0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
CoKe: Localized Contrastive Learning for Robust Keypoint Detection—0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Joint Object Contour Points and Semantics for Instance Segmentation—0
Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation—0
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