Automated Exploration and Inspection: Comparing Two Visual Novelty Detectors | Zendy

Hugo Vieira Neto | Zendy; Ulrich Nehmzow | Zendy

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Automated Exploration and Inspection: Comparing Two Visual Novelty Detectors

Author(s) -

Hugo Vieira Neto,

Ulrich Nehmzow

Publication year - 2005

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/5770

Subject(s) - computer science , novelty , novelty detection , artificial intelligence , principal component analysis , measure (data warehouse) , robot , mobile robot , component (thermodynamics) , computer vision , detector , artificial neural network , machine learning , data mining , physics , telecommunications , philosophy , thermodynamics , theology

Mobile robot applications that involve exploration and inspection of dynamic environments benefit, and often even are dependant on reliable novelty detection algorithms. In this paper we compare and discuss the performance and functionality of two different on-line novelty detection algorithms, one based on incremental Principal Component Analysis and the other on a Grow-When-Required artificial neural network. A series of experiments using visual input obtained by a mobile robot interacting with laboratory and real-world environments demonstrate and measure advantages and disadvantages of each approach

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