Automated Edge Detection Using Convolutional Neural Network

El-Sayed, Mohamed A.; Estaitia, Yarub A.; Khafagy, Mohamed A.

doi:10.14569/ijacsa.2013.041003

Cited by 16 publications

(5 citation statements)

References 13 publications

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“…Salah satu fitur yang dapat diekstrak adalah edge pada tangan. CNN memiliki tahapan convolution yang secara otomatis akan melakukan ekstraksi pada citra [6]. Sehingga CNN akan cocok digunakan untuk klasifikasi gesture tangan manusia untuk game rock, paper, dan scissors.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

Naufal

Huda

Budilaksono

et al. 2021

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Permainan batu, gunting, dan kertas sangat populer di seluruh dunia. Permainan ini biasanya dimainkan saat sedang berkumpul untuk mengundi ataupun hanya bermain untuk mengetahui yang menang dan yang kalah. Namun, perkembangan zaman dan teknologi mengakibatkan orang dapat berkumpul secara virtual. Untuk bisa melakukan permainan ini secara virtual, penelitian ini membuat model klasifikasi citra untuk membedakan objek tangan yang menunjuk batu, kertas, dan gunting. Performa metode klasifikasi merupakan hal yang harus diperhatikan dalam kasus ini. Salah satu metode klasifikasi citra yang populer adalah Convolutional Neural Network (CNN). CNN adalah salah satu jenis neural network yang biasa digunakan pada data klasifikasi citra. CNN terinspirasi dari jaringan syaraf manusia. Algoritma ini memiliki 3 tahapan yang dipakai, yaitu convolutional layer, pooling layer, dan fully connected layer. Uji coba 5-Fold cross validation klasifikasi objek tangan yang menunjuk citra batu, kertas, dan gunting menggunakan CNN pada penelitian ini menghasilkan rata-rata akurasi sebesar 97.66%.

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Section: Pendahuluanunclassified

Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

Naufal

Huda

Budilaksono

et al. 2021

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“…Traditional fault diagnosis methods include time-domain analysis (Gangsar and Tiwari, 2014), frequency-domain analysis (Chen et al, 2022), and time–frequency domain analysis; features are extracted by analyzing information in time and frequency domains. In recent years, the application of neural network to fault diagnosis is popular, and the deep neural network (DNN) (Siniscalchi et al, 2013), convolutional neural network (CNN) (El-Sayed et al, 2013), deep belief network (DBN) (Tran et al, 2014), and other methods have been more mature. They use neural networks to extract features from signals or images, and the distance between source domain and target domain is shortened by domain adaptation layer, and the prediction result is obtained by fault classification.…”

Section: Introductionmentioning

confidence: 99%

A multi-representation transfer adversarial network for intelligent fault diagnosis of rotating machinery

Zhang,

She,

Wang

et al. 2024

Transactions of the Institute of Measurement and Control

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Fault diagnosis of rolling bearings is among the most crucial links in the prognostic and health management of bearings. To solve the problem that cross-domain fault diagnosis cannot be performed due to the distribution differences between different working conditions, a transfer diagnosis method based on multi-representation adversarial neural network is proposed. First, the multi-representation neural network is applied to extract multiscale features. Second, the domain adversarial network is utilized to set the gradient inversion layer and extract the domain invariant features in the multiscale features. In terms of the loss function, the Wasserstein function and cross-entropy loss function are utilized to measure the distance between the source domain and the target domain. The experimental case of rolling bearing supports the effectiveness and superiority of the proposed method.

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“…Simultaneously, artificial intelligence methods, such as artificial neural networks (NNs), have frequently proven their merits in the context of edge detection, classification, and segmentation [12,[18][19][20]. Recently, research has focused on convolutional NNs that were also applied to field boundary detection [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

Wagner

Oppelt

2020

Remote Sensing

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Field mapping and information on agricultural landscapes is of increasing importance for many applications. Monitoring schemes and national cadasters provide a rich source of information but their maintenance and regular updating is costly and labor-intensive. Automatized mapping of fields based on remote sensing imagery may aid in this task and allow for a faster and more regular observation. Although remote sensing has seen extensive use in agricultural research topics, such as plant health monitoring, crop type classification, yield prediction, and irrigation, field delineation and extraction has seen comparatively little research interest. In this study, we present a field boundary detection technique based on deep learning and a variety of image features, and combine it with the graph-based growing contours (GGC) method to extract agricultural fields in a study area in northern Germany. The boundary detection step only requires red, green, and blue (RGB) data and is therefore largely independent of the sensor used. We compare different image features based on color and luminosity information and evaluate their usefulness for the task of field boundary detection. A model based on texture metrics, gradient information, Hessian matrix eigenvalues, and local statistics showed good results with accuracies up to 88.2%, an area under the ROC curve (AUC) of up to 0.94, and F1 score of up to 0.88. The exclusive use of these universal image features may also facilitate transferability to other regions. We further present modifications to the GGC method intended to aid in upscaling of the method through process acceleration with a minimal effect on results. We combined the boundary detection results with the GGC method for field polygon extraction. Results were promising, with the new GGC version performing similarly or better than the original version while experiencing an acceleration of 1.3× to 2.3× on different subsets and input complexities. Further research may explore other applications of the GGC method outside agricultural remote sensing and field extraction.

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Automated Edge Detection Using Convolutional Neural Network

Cited by 16 publications

References 13 publications

Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

A multi-representation transfer adversarial network for intelligent fault diagnosis of rotating machinery

Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction

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