TY  - BOOK
AU  - Panaligan, Nel R.
TI  - Automated fish fingerlings counter system: an evaluation of image segmentation algorithms in overlapping objectives 
PY  - 2018///
CY  - Davao City
PB  - Ateneon de Davao University
KW  - Fish fingerlings
KW  - Counting
KW  - Evaluation
KW  - Image segmentation
KW  - Bangus fry
KW  - Image processing
N1  - Thesis (Master of Science in Information Technology ) -- Ateneo de Davao University, October 2018; CHED Funded Research
N2  - The development of automatic fish counters has been driven by the need for accurate, long-term and cost-effective counting and in terms of object recognition in line with advancement of aquaculture in the country. Non-invasive methods of fish counting are ultimately limited by the properties of the immerging technologies like when candidates for counting are transparent and or small (Bangus Fry). Image processing is one of the most modem approach in automating the counting process. The main objective of the study is to evaluate three image segmentation algorithms in an image (2D image of bangus fry) with touching or overlapping fry, whether or not they are capable of segmenting tiny objects (2 weeks old bangus fry) in an image. The study will be evaluating three (3) Image segmentation algorithms with different methods applied in each, (1) Watershed Algorithm, (2) Hough Transform, (3) Concavity Analysis. This study involves 4 basic steps used in image processing; Image acquisition, Image Pre-Processing, Image segmentation, and Object counting. Result shows that the second method of the Watershed Algorithm which identifies the Local Maxima and the Distance transform preforms best compared with the other algorithm, with an accuracy rate of 86.4 7% and 0 false detection in an experimental data of four sets of 2D image ranging from 100, 200, 300, and 400 bangus fry per test Image
UR  - http://62.72.45.86/cgi-bin/koha/opac-retrieve-file.pl?id=d3a47c3a37b3c75414c4598209127717
UR  - http://62.72.45.86/cgi-bin/koha/opac-retrieve-file.pl?id=c96bdf4879ca1d1932c323ebd5475b5a
ER  -