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Centrosymmetric constrained Convolutional Neural Networks
Complex signals can be viewed as compositions of numerous sine waves with different frequencies and amplitudes. As the fundamental unit of perceiving...
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Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs
Diabetic retinopathy (DR) is a common eye disorder that can lead to vision problems and blindness, necessitating accurate grading for effective...
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Convolutional Neural Networks
This chapter introduces convolutional neural networks (CNNs) and describes how they can be used in the context of sports analytics. CNNs are suitable...
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Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks
The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?"...
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Convolutional Neural Networks and Architectures
This chapter briefly introduces Convolutional Neural Networks (CNNs). One of the first CNNs is proposed in [41] (known as LeNet) to deal with...
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Convolutional Neural Networks
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech....
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Symmetry-structured convolutional neural networks
We consider convolutional neural networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions. Such networks arise in...
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Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks
Whenever a visual scene is cast onto the retina, much of it will appear degraded due to poor resolution in the periphery; moreover, optical defocus...
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Convolutional Neural Networks
Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können....
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Convolutional Neural Networks
Convolutional neural networks (CNNs) are a category of neural networks that can be used to identify spatial patterns in a robust manner. They achieve...
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Explainable convolutional neural networks for assessing head and neck cancer histopathology
PurposeAlthough neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains...
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Convolutional Neural Networks
A series of successful applications of Convolutional Neural Networks (CNNs) in various computer vision competitions in 2011 and 2012 were a major...
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Basketball technique action recognition using 3D convolutional neural networks
This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural...
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Exploring adversarial examples and adversarial robustness of convolutional neural networks by mutual information
Convolutional neural networks (CNNs) are susceptible to adversarial examples, which are similar to original examples but contain malicious...
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Optical Videoscope Image Super-Resolution Based on Convolutional Neural Networks
Image super-resolution is the process performed to improve the resolution of the images from Low Resolution (LR) to High Resolution (HR). Videoscope...
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Convolutional Neural Networks for Medical Applications
Convolutional Neural Networks for Medical Applications consists of research investigated by the author, containing state-of-the-art knowledge,...
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Unveiling the power of convolutional neural networks in melanoma diagnosis
BackgroundConvolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the...
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Residential building type classification from street-view imagery with convolutional neural networks
Computer vision techniques are increasingly used to develop efficient and automatic methods that provide alternative data sources. Micro-level...
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Detecting urban tree canopy using convolutional neural networks with aerial images and LiDAR data
The detection of urban tree canopy plays a crucial role in assessing the ecosystem of trees and reducing greenhouse gases in smart cities. This...
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Novel applications of Convolutional Neural Networks in the age of Transformers
Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial...
