A Fast Sonar-Based Benthic Object Recognition Model Via Extreme Learning Machine

Abstract

The Fast Sonar-Based Object Recognition Turns Out to Be One of the Most Challenging Topics in the Underwater Signal Analysis. in This Paper, We Try to Develop a Fast Benthic Object Recognition Model Via the Extreme Learning Machine (Elm) on the Basis of the Structured Geometrical Feature Extraction. Geometrical Features Such as Major and Minor Axis, Eccentricity, Circularity and So on Are Employed to Construct Learning Samples of Elm. the Classifier based on Elm is Used to Recognize the Target Objects in Sonar Images. It Has Been Shown in the Simulation Experiments that the Proposed Model Could Keep a Quite Good Recognition Performance with a Much Fast Speed.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

ELM; Geometrical Feature Extraction; Object Recognition; Sonar Image

International Standard Book Number (ISBN)

978-093395743-5

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

08 Feb 2016

This document is currently not available here.

Share

 
COinS