Material Information Acquisition Using a ToF Range Sensor for Interactive Object Recognition

We propose an approach for recognizing the pose and surface material of diverse objects, leveraging diffuse reflection principles and data fusion. Through theoretical analysis and the derivation of factors influencing diffuse reflection on objects, the method concentrates on and exploits surface information.To validate the feasibility of our theoretical research, the depth and active infrared intensity data obtained from a single time-of-flight camera are initially combined. Subsequently, these data undergo processing using feature extraction and lightweight machine-learning techniques. In addition, an optimization method is introduced to enhance the fitting of intensity. The experimental results not only visually showcase the effectiveness of our proposed method in accurately detecting the positions and surface materials of targets with varying sizes and spatial locations but also reveal that the vast majority of the sample data can achieve a recognition accuracy of 94.8% or higher.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Material Information Acquisition Using a ToF Range Sensor for Interactive Object Recognition

Cited by 2 publications

References 19 publications

Learning object-specific DAGs for multi-label material recognition

Learning object-specific DAGs for multi-label material recognition

Object pose and surface material recognition using a single-time-of-flight camera

Contact Info

Product

Resources

About