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System integration and image pre-pro...

Tonde, Chetan.

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System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish./
Author:	Tonde, Chetan.
Description:	130 p.
Notes:	Source: Masters Abstracts International, Volume: 49-03, page: 1973.
Contained By:	Masters Abstracts International49-03.
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1487804
ISBN:	9781124419619

System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish.
Tonde, Chetan.

System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish. - 130 p.

Source: Masters Abstracts International, Volume: 49-03, page: 1973.

Thesis (M.S.)--Rutgers The State University of New Jersey - New Brunswick, 2010.

In this work we build an underwater vision system capable of monitoring the activities of fish found near coral reefs. We propose a unique hardware platform capable of monitoring a volume of water in a very efficient and cost effective way. We also develop algorithms required to take advantage of such a system. There are three main contributions of this work, which are; (1) using two right-angled camera's to capture underwater image sequences, (2) developing algorithms to track and pre-process images for recognition (3) and demonstrating that we can recognize fish families or in some cases exact fish species using fish shape (with size, color and pattern features to be added later). We conclude from this work that using just two cameras in a right-angled setup is a cheap and effective way of monitoring fish activities in general. It is cost effective when compared to using multiple cameras and also less computationally intensive. We developed and modified our approach based on observations we made while testing this setup and accommodated these modifications in our software. We installed this system at the artificial coral reef in the New York Aquarium and periodically collected image sequences for processing. We demonstrate our results on the collected sequences and show pre-processing results on them. We also demonstrate, using shape feature from a fish sequence we collected at the aquarium (using cross-validation); that we can recognize fish families or in some cases exact species using those features.

ISBN: 9781124419619Subjects--Topical Terms:

1669061
Engineering, Computer.

System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish.
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020 $a 9781124419619
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100 1 $a Tonde, Chetan. $3 1680505
245 1 0 $a System integration and image pre-processing for an automated, real-time identification and monitoring system for coral reef fish.
300 $a 130 p.
500 $a Source: Masters Abstracts International, Volume: 49-03, page: 1973.
500 $a Adviser: Joseph D. Wilder.
502 $a Thesis (M.S.)--Rutgers The State University of New Jersey - New Brunswick, 2010.
520 $a In this work we build an underwater vision system capable of monitoring the activities of fish found near coral reefs. We propose a unique hardware platform capable of monitoring a volume of water in a very efficient and cost effective way. We also develop algorithms required to take advantage of such a system. There are three main contributions of this work, which are; (1) using two right-angled camera's to capture underwater image sequences, (2) developing algorithms to track and pre-process images for recognition (3) and demonstrating that we can recognize fish families or in some cases exact fish species using fish shape (with size, color and pattern features to be added later). We conclude from this work that using just two cameras in a right-angled setup is a cheap and effective way of monitoring fish activities in general. It is cost effective when compared to using multiple cameras and also less computationally intensive. We developed and modified our approach based on observations we made while testing this setup and accommodated these modifications in our software. We installed this system at the artificial coral reef in the New York Aquarium and periodically collected image sequences for processing. We demonstrate our results on the collected sequences and show pre-processing results on them. We also demonstrate, using shape feature from a fish sequence we collected at the aquarium (using cross-validation); that we can recognize fish families or in some cases exact species using those features.
520 $a Keywords: Background Modelling, Camera Calibration, Multi-Target Tracking, coral Reef, Fish Species, Shape Analysis, Recognition.
590 $a School code: 0190.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Computer Science. $3 626642
690 $a 0464
690 $a 0544
690 $a 0984
710 2 $a Rutgers The State University of New Jersey - New Brunswick. $b Graduate School - New Brunswick. $3 1019196
773 0 $t Masters Abstracts International $g 49-03.
790 1 0 $a Wilder, Joseph D., $e advisor
790 $a 0190
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1487804