MVN_CNN and UBN_CNN for endocardial edge detection

Ketout, Hussin; Horne, Gabrielle

doi:10.1109/icnc.2011.6022163

Cited by 5 publications

(1 citation statement)

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“…The FC algorithm is inefficient in extracting and learning features of a neural network and has limitations in increasing accuracy. CNN is a structure that extracts features of data and identifies patterns of features [6]. The CNN algorithm proceeds through the convolution process and the pooling process.…”

Section: Fig 2 Fc Neural Network Algorithmsmentioning

confidence: 99%

A Study on Pagoda Image Search Using Artificial Intelligence (AI) Technology for Restoration of Cultural Properties

2021

KSII TIIS

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The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

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Section: Fig 2 Fc Neural Network Algorithmsmentioning

confidence: 99%