Hand Segmentation for Hand-Object Interaction from Depth map

Abstract

Hand segmentation for hand-object interaction is a necessary preprocessing step in many applications such as augmented reality, medical application, and human-robot interaction. However, typical methods are based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations. Thus, we propose hand segmentation method for hand-object interaction using only a depth map. It is challenging because of the small depth difference between a hand and objects during an interaction. To overcome this challenge, we propose the two-stage random decision forest (RDF) method consisting of detecting hands and segmenting hands. To validate the proposed method, we demonstrate results on the publicly available dataset of hand segmentation for hand-object interaction. The proposed method achieves high accuracy in short processing time comparing to the other state-of-the-art methods.

References in corpus (1)

• Depth Adaptive Deep Neural Network for Semantic Segmentation

Cited by in corpus (6)

• A/D Converter Architectures for Energy-Efficient Vision Processor
• An Accurate Interconnect Test Structure for Parasitic Validation in On-Chip Machine Learning Accelerators
• Destructive Read by Wave Interference for Arbitration
• Chaotic-Based Processor for Communication and Multimedia Applications
• Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network
• Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning