Intelligent adaptive object recognition

Safa Almazmome; Li Zhang

doi:10.1109/FSKD.2016.7603535

Intelligent adaptive object recognition

Safa Almazmome, Li Zhang

Computer and Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation. Experimental results indicate that the proposed system achieves high accuracy 98% accurate for real-time object recognition tasks.

Original language	English
Title of host publication	2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Place of Publication	Piscataway
Publisher	IEEE
Pages	2272-2276
Number of pages	5
ISBN (Print)	978-1-5090-4094-0
DOIs	https://doi.org/10.1109/FSKD.2016.7603535
Publication status	Published - Aug 2016

Keywords

Computer vision and image processing
Neural networks
Object recognition

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10.1109/FSKD.2016.7603535

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title = "Intelligent adaptive object recognition",

abstract = "This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation. Experimental results indicate that the proposed system achieves high accuracy 98% accurate for real-time object recognition tasks.",

keywords = "Computer vision and image processing, Neural networks, Object recognition",

author = "Safa Almazmome and Li Zhang",

year = "2016",

month = aug,

doi = "10.1109/FSKD.2016.7603535",

language = "English",

isbn = "978-1-5090-4094-0",

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booktitle = "2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)",

publisher = "IEEE",

address = "United States",

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T1 - Intelligent adaptive object recognition

AU - Almazmome, Safa

AU - Zhang, Li

PY - 2016/8

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AB - This research proposes an object recognition system using image processing and neural network based classification. The system is capable of recognizing 7 objects from an uncluttered background by extracting color, texture and shape features. The proposed system consists of image segmentation, feature extraction and classification. Diverse neural network topology settings have been employed for evaluation. Experimental results indicate that the proposed system achieves high accuracy 98% accurate for real-time object recognition tasks.

KW - Computer vision and image processing

KW - Neural networks

KW - Object recognition

U2 - 10.1109/FSKD.2016.7603535

DO - 10.1109/FSKD.2016.7603535

M3 - Chapter

SN - 978-1-5090-4094-0

SP - 2272

EP - 2276

BT - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

PB - IEEE

CY - Piscataway

ER -

Intelligent adaptive object recognition

Abstract

Keywords

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