Plant Disease Identifer Using K-Means and GLSM in Convolution Neural Network
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In this paper, the problem of image segmentation using artificial neural network ( ANN) is discussed. A new ANN model for image segmentation is proposed which consists of two sub-networks in cascade, i.e. the master sub-network (MSN) and the auxiliary sub-network (ASN). The dynamics of this model is studied in details and the corresponding algorithm is described in the paper. Finally, the simulations are carried out for the medical image using the moment-preserving thresholding and the proposed ANN model, and the corresponding results indicate that the image segmented by the proposed ANN model is much better than that obtained by the moment-preserving thresholding.< >
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In this paper, we present a style transfer-based artwork using deep learning. Our proposed artwork aims to perform a different style transfer to the foreground and background using images of different styles. The framework of our proposed artwork comprises three steps: i) human detection and segmentation, ii) style transfer to the human and background regions, and iii) fusion of the style-transferred result images. The resultant style transferred image is preserved the content of the original image while changing the original style with the target style. The proposed artwork can provide a different artistic effect and style with an esthetic sense to human and background regions using different style images.
Transfer of learning
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This paper studies the convolutional neural network applied in medical image processing, and with glioma MRI figure as a specific research problem, the medical images using convolution neural network structure, change the traditional single convolution neural network structure, designed the multiple columns convolution neural networks, effectively solve the glioma grading difficult question. Experiments show that this method is effective in solving such medical problems.
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The concept of Deep Learning is emanated in machine learning as an enhanced research area and is empirical to various image applications. The objective of the project propounded in the paper, is applying the abstract of an algorithm of Deep Learning, viz, Convolutional neural networks (CNN) for mult
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Artificial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classification system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.
Convolution (computer science)
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