@article {10.3844/jcssp.2010.1088.1094,
article_type = {journal},
title = {Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network},
author = {Alsmadi, Mutasem Khalil and Omar, Khairuddin Bin and Noah, Shahrul Azman and Almarashdeh, Ibrahim},
volume = {6},
number = {10},
year = {2010},
month = {Aug},
pages = {1088-1094},
doi = {10.3844/jcssp.2010.1088.1094},
url = {https://thescipub.com/abstract/jcssp.2010.1088.1094},
abstract = {Problem statement: Image recognition is a challenging problem researchers had been research into this area for so long especially in the recent years, due to distortion, noise, segmentation errors, overlap and occlusion of objects in digital images. In our study, there are many fields concern with pattern recognition, for example, fingerprint verification, face recognition, iris discrimination, chromosome shape discrimination, optical character recognition, texture discrimination and speech recognition, the subject of pattern recognition appears. A system for recognizing isolated pattern of interest may be as an approach for dealing with such application. Scientists and engineers with interests in image processing and pattern recognition have developed various approaches to deal with digital image recognition problems such as, neural network, contour matching and statistics. Approach: In this study, our aim was to recognize an isolated pattern of interest in the image based on the combination between robust features extraction. Where depend on size and shape measurements, that were extracted by measuring the distance and geometrical measurements. Results: We presented a system prototype for dealing with such problem. The system started by acquiring an image containing pattern of fish, then the image features extraction is performed relying on size and shape measurements. Our system has been applied on 20 different fish families, each family has a different number of fish types and our sample consists of distinct 350 of fish images. These images were divided into two datasets: 257 training images and 93 testing images. An overall accuracy was obtained using the neural network associated with the back-propagation algorithm was 86% on the test dataset used. Conclusion: We developed a classifier for fish images recognition. We efficiently have chosen a features extraction method to fit our demands. Our classifier successfully design and implement a decision which performed efficiently without any problems. Eventually, the classifier is able to categorize the given fish into its cluster and categorize the clustered fish into its poison or non-poison fish and categorizes the poison and non-poison fish into its family.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}