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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
DexYCB: A Benchmark for Capturing Hand Grasping of ObjectsCode1
GoodPoint: unsupervised learning of keypoint detection and descriptionCode1
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint EstimatesCode1
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same ActionCode1
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast DescriptorCode1
BPFNet: A Unified Framework for Bimodal Palmprint Alignment and FusionCode1
GRIT: General Robust Image Task BenchmarkCode1
Auto Learning AttentionCode1
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion ApproachCode1
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