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Identification of dried sea cucumber species using color and texture extraction (Record no. 7349)

MARC details
000 -LEADER
fixed length control field	01922nam a2200205 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field	CHED
005 - DATE AND TIME OF LATEST TRANSACTION
control field	20250131101648.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field	ta
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	250131e2018 ph \|\|\|\|\| \|\|\|\| 00\| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency	Commission on Higher Education
100 3# - MAIN ENTRY--PERSONAL NAME
Personal name	Perez, Novie Dave B.
245 00 - TITLE STATEMENT
Title	Identification of dried sea cucumber species using color and texture extraction
Statement of responsibility, etc.	/ Novie Dave B. Perez
260 3# - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	Manila
Name of publisher, distributor, etc.	: Mapua University
Date of publication, distribution, etc.	,2018.
300 ## - PHYSICAL DESCRIPTION
Extent	vi, 33cm.
500 ## - GENERAL NOTE
General note	Thesis (Master of Science in Computer Engineering) -- MApua University, July 2018.
520 3# - SUMMARY, ETC.
Summary, etc.	Sea cucumber is one of the highest valued marine products. The purpose of this study is to design a system that will be used to capture dried sea cucumber samples and implement color and texture feature extraction for specie identification. The system comprised of hardware and software components in which the focus of this study is to implement the algorithm of Color and Texture Extraction to be used in training and testing a Naive Bayes classifier. Haralick texture was extracted using GLCM (Gray-Level Co-occurrence Matrix) method and Color features were taken from the converted RGB to HSV color space model. In texture extraction, each of the image were converted into its gray level counterpart then 13 texture feature classes were extracted from it. In HSV color space, 512 color features were extracted from a quantized 2: )x2: pixel image. The text re and HSV color features were fed into a Naive Bayes Classifier for training and testing. Upon testing the prototype and employing 4-Fold cross validation, the result turns out to be good as it yields a 75.26% overall accuracy in identifying dried sea cucumber species.
650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Sea cucumbers
General subdivision	Identification
--	Image processing.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="http://181.215.242.151/cgi-bin/koha/opac-retrieve-file.pl?id=c361ec8e265605b450ae752c234e02c5">http://181.215.242.151/cgi-bin/koha/opac-retrieve-file.pl?id=c361ec8e265605b450ae752c234e02c5</a>
Public note	Table of Contents
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme	Library of Congress Classification
Koha item type	CHED Funded Research
Suppress in OPAC	No

Holdings
Withdrawn status	Lost status	Source of classification or shelving scheme	Damaged status	Use restrictions	Not for loan	Collection	Home library	Current library	Shelving location	Date acquired	Total checkouts	Full call number	Barcode	Date last seen	Price effective from	Koha item type
		Library of Congress Classification		Restricted Access	Storage Area	Thesis and Dissertation	Commission on Higher Education	Commission on Higher Education	Thesis	01/31/2025		LG 996 2018 C6 P47	CHEDFR-000316	01/31/2025	01/31/2025	CHED Funded Research
		Library of Congress Classification		Room Use Only		Digital Thesis and Dissertation	Commission on Higher Education	Commission on Higher Education	Digital Thesis and Dissertation	01/31/2025		LG 996 2018 C6 P47	DCHEDFR-000059	01/31/2025	01/31/2025	CHED Funded Research