A novel shape matching descriptor for real-time hand gesture recognition
The current state-of-the-art hand gesture recognition methodologies heavily rely in the use of machine learning. However there are scenarios that machine learning cannot be applied successfully, for example in situations where data is scarce. This is the case when one-to-one matching is required between a query and a dataset of hand gestures where each gesture represents a unique class. In situations where learning algorithms cannot be trained, classic computer vision techniques such as feature extraction can be used to identify similarities between objects. Shape is one of the most important features that can be extracted from images, however the most accurate shape matching algorithms tend to be computationally inefficient for real-time applications. In this work we present a novel shape matching methodology for real-time hand gesture recognition. Extensive experiments were carried out comparing our method with other shape matching methods with respect to accuracy and computational complexity using our own collected hand gesture dataset and a modified version of the MPEG-7 dataset.%that is widely used for comparing 2D shape matching algorithms. Our method outperforms the other methods and provides a good combination of accuracy and computational efficiency for real-time applications.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningComputational EfficiencyGesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionSimilar Papers 제목 키워드 기반
BodyPrint: Pose Invariant 3D Shape Matching of Human Bodies
3D human body shape matching has large potential on many real world applications, especially with the recent advances in the 3D range sensing technology. We address this problem by proposing a novel holistic human body s…
Continuous Geodesic Convolutions for Learning on 3D Shapes
The majority of descriptor-based methods for geometric processing of non-rigid shape rely on hand-crafted descriptors. Recently, learning-based techniques have been shown effective, achieving state-of-the-art results in …
Signature of Geometric Centroids for 3D Local Shape Description and Partial Shape Matching
Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the…
3D Object RecognitionDenoisingObject RecognitionSymmetry Informative and Agnostic Feature Disentanglement for 3D Shapes
Shape descriptors, i.e., per-vertex features of 3D meshes or point clouds, are fundamental to shape analysis. Historically, various handcrafted geometry-aware descriptors and feature refinement techniques have been propo…
Symmetry DetectionPoint CloudsDescriptor-Free Multi-View Region Matching for Instance-Wise 3D Reconstruction
This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multi-view instance segmentation becomes challenging for scenes with repetitive textures and sh…
3D ReconstructionInstance SegmentationSegmentationSemantic Segmentation