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Now showing items 21-30 of 45
Human experts vs. machines in taxa recognition
(
Elsevier B.V.
, 2020 , Article)
The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards ...
Real-Time Glaucoma Detection from Digital Fundus Images Using Self-ONNs
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later ...
Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications
(
Springer
, 2017 , Conference Paper)
Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the ...
One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery
(
Springer
, 2022 , Conference Paper)
This paper presents a novel real-time rotating machinery damage monitoring system. The system detects, quantifies, and localizes damage in ball bearings in a fast and accurate way using one-dimensional convolutional neural ...
Performance Comparison of Learned vs. Engineered Features for Polarimetric SAR Terrain Classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
In this work, we propose to use learned features for terrain classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. In the proposed classification framework, the learned features are extracted from sliding ...
1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals under White Gaussian Noise
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (-40dB ...
Improving text-to-image generation with object layout guidance
(
Springer
, 2021 , Article)
The automatic generation of realistic images directly from a story text is a very challenging problem, as it cannot be addressed using a single image generation approach due mainly to the semantic complexity of the story ...
Binarization of Degraded Document Images Using Convolutional Neural Networks and Wavelet-Based Multichannel Images
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Convolutional neural networks (CNNs) have previously been broadly utilized to binarize document images. These methods have problems when faced with degraded historical documents. This paper proposes the utilization of CNNs ...
Smartphone-based food recognition system using multiple deep CNN models
(
Springer
, 2021 , Article)
People with blindness or low vision utilize mobile assistive tools for various applications such as object recognition, text recognition, etc. Most of the available applications are focused on recognizing generic objects. ...
Binarization of degraded document images using convolutional neural networks based on predicted two-channel images
(
IEEE Computer Society
, 2019 , Conference Paper)
Due to the poor condition of most of historical documents, binarization is difficult to separate document image background pixels from foreground pixels. This paper proposes Convolutional Neural Networks (CNNs) based on ...