Using grayscale images for object recognition with convolutional-recursive neural network

Bui, Hieu Minh; Lech, Margaret; Cheng, Eva; Neville, Katrina; Burnett, Ian

doi:10.1109/cce.2016.7562656

Cited by 58 publications

(50 citation statements)

References 17 publications

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“…RNNs do this by using the previous layer's output as the dependency for the current layer's input -there is some function that connects the output of the previous layer to the input of the current layer in a specified sequence (a "recurrence relation"). Following Bui et al (2016), we would utilise a convo-lutional RNN (C-RNN) which takes the feature maps from the last convolutional layer in our original network (after activation) as an input and outputs a compact representation of each feature over many convolutions. This allows the C-RNN to learn a general form for our features (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…For example, if we take an image that contains at least one sunspot and we want to know if it has a single sunspot or multiple sunspots then we will use two RNN blocks -one to predict that the image contains a sunspot and the second to predict how many sunspots are in the image. This has seen great success in other image classification cases (Bui et al, 2016;Wang et al, 2016) and could work well for solar images.…”

Section: Discussionmentioning

confidence: 99%

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Fast Solar Image Classification Using Deep Learning and Its Importance for Automation in Solar Physics

Armstrong

Fletcher

2019

Sol Phys

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The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the Daniel K. Inouye Solar Telescope (DKIST) we will be entering the age of petabyte solar data. Automated feature detection will be an invaluable tool for post-processing of solar images to create catalogues of data ready for researchers to use. We propose a deep learning model to accomplish this; a deep convolutional neural network is adept at feature extraction and processing images quickly. We train our network using data from Hinode/Solar Optical Telescope (SOT) Hα images of a small subset of solar features with different geometries: filaments, prominences, flare ribbons, sunspots and the quiet Sun (i.e. the absence of any of the other four features). We achieve near perfect performance on classifying unseen images from SOT (≈ 99.9%) in 4.66 seconds. We also for the first time explore transfer learning in a solar context. Transfer learning uses pre-trained deep neural networks to help train new deep learning models i.e. it teaches a new model. We show that our network is robust to changes in resolution by degrading images from SOT resolution (≈ 0.33 at λ = 6563 Å) to Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) resolution (≈ 1.2) without a change in performance of our network. However, we also observe where the network fails to generalise to sunspots from SDO/AIA bands 1600/1700 Å due to small-scale brightenings around the sunspots and prominences in SDO/AIA 304 Å due to coronal emission. Keywords Instrumentation and data management 1. Introduction With each new solar physics mission/telescope, instruments are improving in spatial, temporal and/or wavelength resolution. Increased resolution in any of these three categories B J.A. Armstrong

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Fast Solar Image Classification Using Deep Learning and Its Importance for Automation in Solar Physics

Armstrong

Fletcher

2019

Sol Phys

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“…According to [42] those results make good sense, as the classification accuracy of a CNN model depends largely on the image lighting, and colored images are susceptible to lighting. The effect of the lighting intensity can be seen in [43] where it was studied thoroughly along with other factors.…”

Section: B Grayscale Imagesmentioning

confidence: 95%

Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs

Najah¹,

Mustafa²,

Hacham³

2021

alkej

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Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers. In this research, we present an adopted approach based on convolutional neural networks to design a system for quality inspection with high level of accuracy and low cost. The system is designed using transfer learning to transfer layers from a previously trained model and a fully connected neural network to classify the product’s condition into healthy or damaged. Helical gears were used as the inspected object and three cameras with differing resolutions were used to evaluate the system with colored and grayscale images. Experimental results showed high accuracy levels with colored images and even higher accuracies with grayscale images at every resolution, emphasizing the ability to build an inspection system at low costs, ease of construction and automatic extraction of image features.

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“…Third, Bui et al demonstrated that the use of grayscale pictures can generate better accuracy in differentiating figures in some neural network models than color ones. [14]. Therefore, grayscale photos were chosen as the input of the model, considering it may reduce the potential interference caused by colors and enhance the accuracy.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Differentiation of Cytopathic Effects (CPE) induced by influenza virus infection using deep Convolutional Neural Networks (CNN)

et al. 2020

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Cell culture remains as the golden standard for primary isolation of viruses in clinical specimens. In the current practice, researchers have to recognize the cytopathic effects (CPE) induced by virus infection and subsequently use virus-specific monoclonal antibody to confirm the presence of virus. Considering the broad applications of neural network in various fields, we aimed to utilize convolutional neural networks (CNN) to shorten the timing required for CPE identification and to improve the assay sensitivity. Based on the characteristics of influenza-induced CPE, a CNN model with larger sizes of filters and max-pooling kernels was constructed in the absence of transfer learning. A total of 601 images from mock-infected and influenza-infected MDCK cells were used to train the model. The performance of the model was tested by using extra 400 images and the percentage of correct recognition was 99.75%. To further examine the limit of our model in evaluating the changes of CPE overtime, additional 1190 images from a new experiment were used and the recognition rates at 16 hour (hr), 28 hr, and 40 hr post virus infection were 71.80%, 98.25%, and 87.46%, respectively. The specificity of our model, examined by images of MDCK cells infected by six other non-influenza viruses, was 100%. Hence, a simple CNN model was established to enhance the identification of influenza virus in clinical practice.

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Using grayscale images for object recognition with convolutional-recursive neural network

Cited by 58 publications

References 17 publications

Fast Solar Image Classification Using Deep Learning and Its Importance for Automation in Solar Physics

Fast Solar Image Classification Using Deep Learning and Its Importance for Automation in Solar Physics

Building a High Accuracy Transfer Learning-Based Quality Inspection System at Low Costs

Differentiation of Cytopathic Effects (CPE) induced by influenza virus infection using deep Convolutional Neural Networks (CNN)

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