• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4866-4888
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• 2020

One major and time-consuming task in fish production is obtaining an accurate estimate of the number of fish produced. In most Nigerian farms, fish counting is performed manually. Digital image processing (DIP) is an inexpensive solution, but its accuracy is affected by noise, overlapping fish, and interfering objects. This study developed a catfish recognition and counting algorithm that introduces detection before counting and consists of six steps: image acquisition, pre-processing, segmentation, feature extraction, recognition, and counting. Images were acquired and pre-processed. The segmentation was performed by applying three methods: image binarization using Otsu thresholding, morphological operations using fill hole, dilation, and opening operations, and boundary segmentation using edge detection. The boundary features were extracted using a chain code algorithm and Fourier descriptors (CH-FD), which were used to train an artificial neural network (ANN) to perform the recognition. The new counting approach, based on the geometry of the fish, was applied to determine the number of fish and was found to be suitable for counting fish of any size and handling overlap. The accuracies of the segmentation algorithm, boundary pixel and Fourier descriptors (BD-FD), and the proposed CH-FD method were 90.34%, 96.6%, and 100% respectively. The proposed counting algorithm demonstrated 100% accuracy.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.809-818
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• 2019

The Republic of Korea is very vulnerable to damage from storm and flood due to the rainfall phenomenon in summer and the topography of the narrow peninsula. The damage is recently getting worse because of the concentration rainfall. The accurate damage information production and analysis is required to prepare for future disaster. In this study, we analyzed the water surface area changes of Byeokjeong, Sajeom, Subu and Boryeong using Sentinel-1 satellite imagery. The surface area of the Sentinel-1 satellite, taken from May 2015 to August 2019, was preprocessed using RTC and image binarization using Otsu. The water surface area of reservoir was compared with the storage capacity from WAMIS and RIMS. As a result, Subu and Boryeong showed strong correlations of 0.850 and 0.941, respectively, and Byeokjeong and Sajeom showed the normal correlation of 0.651 and 0.657. Thus, SAR satellite imagery can be used to objective data as disaster management.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.2 s.302
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• pp.31-38
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• 2005

Recently, Biometrics-based personal identification is regarded as an effective method of person's identity with recognition automation and high performance. In this paper, the palmprint recognition method based on Hu invariant moment is proposed. And the low-resolution(750dpi) palmprint image$(5.5Cm\times5.5Cm)$ is used for the small scale database of the effectual palmprint recognition system. The proposed system is consists of two parts: firstly, the palmprint fixed equipment for the acquisition of the correctly palmprint image and secondly, the algorithm of the efficient processing for the palmprint recognition. And the palmprint identification step is limited 3 times. As a results, when the coefficient is 0.001 then FAR and GAR are $0.038\%$ and $98.1\%$ each other. The authors confirmed that FAR is improved $0.002\%$ and GAR is $0.1\%$ each other compared with [3].

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.6-17
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• 2013

Recently, the color of vehicle license plate has been changed from green to white. Thus the vehicle plate recognition system used for parking management systems, speed and signal violation detection systems should be robust to the both colors. This paper presents a vehicle license plate recognition system, which works on both of green and white plate at the same time. In the proposed system, the image of license plate is taken from a captured vehicle image by using morphological information. In the next, each character region in the license plate image is extracted based on the vertical and horizontal projection of plate image and the relative position of individual characters. Finally, for the recognition process of extracted characters, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis) are sequentially utilized. In the experiment, vehicle license plates of both green background and white background captured under irregular illumination conditions have been tested, and the relatively high extraction and recognition rates are observed.