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A method for detecting atrial fibrillation (AF) from electrocardiogram (ECG) measured by a 24‐hour Holter electrocardiograph (Holter‐ECG) is proposed using convolutional neural network (CNN). In the preprocessing stage, artifacts and noises on Holter‐ECG are removed by a bandpass filter. The detection method consists of two stages: extraction of abnormal waveforms using one‐dimensional CNN trained with segmented ECG waveform and its spectral entropy, and identification of AF using two‐dimensional CNN trained with segmented ECG spectrogram. A total of 47 520 datasets obtained from Holter‐ECG were prepared, and used for training at both CNN stages. Newly prepared (untrained) datasets of 24‐hour Holter‐ECG of 10 subjects and MIT‐BIH databases are tested, and the proposed method showed sufficient performance for detecting AF, with the accuracy of approximately 90%. This result indicates the feasibility of the proposed method. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Curb detection with a monocular camera is important to assist driving by detecting curbs to avoid accidents. However, various road scenes and curb shapes make it difficult to detect curbs based on a single image. In this study, a novel image‐based curb detection method that is highly efficient, inexpensive, and less complex than LiDAR‐based solutions was developed. A deep learning framework was used to detect curbs from road images automatically. A custom convolutional neural network (CNN) model was built, and another seven pretrained models were fine‐tuned for curb patch classification. The evaluation metrics, such as accuracy, F1‐score, area under curve, and prediction time, were considered comprehensively to select the optimal CNN architecture. Three promising CNN architectures were employed as classification networks and embedded into the YOLO‐v2 framework to construct curb detectors. The detection performance was evaluated in terms of the average precision on an urban road image dataset. By configuring parameters for the optimal CNN architecture, the best detector achieved an average precision of 99.16%, which verifies the effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Deep residual network (ResNet), one of the mainstream deep learning models, has achieved groundbreaking results in various fields. However, all neurons used in ResNet are based on the McCulloch‐Pitts model which has long been criticized for its oversimplified structure. Accordingly, this paper for the first time proposes a novel dendritic residual network by considering the powerful information processing capacity of dendrites in neurons. Experimental results based on the challenging COVID‐19 prediction problem show the superiority of the proposed method in comparison with other state‐of‐the‐art ones. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

A spherical image has a full field of view. Using spherical images can estimate camera pose more accurately than conventional perspective images. It is known that a sphere can be quasi‐uniform sampled by dividing a geodesic dome repeatedly; it results in a representation with rotational invariance approximately. Based on this fact, in this paper we proposed a method of estimating a spherical camera pose based on the geodesic‐dome‐division‐based discrete spherical image representation by modifying the conventional perspective‐image‐oriented convolutional neural network, called PoseNet. First, a spherical image is represented as a geodesic‐dome‐division‐based discrete spherical image. Then, to cope with the six‐connectivity of the pixels of a geodesic‐dome‐division‐based discrete spherical image using the conventional convolutional neural network framework, the discrete spherical image is flattened as a connected five parallelograms and resampled as an eight‐connectivity image. Consequently, via the comparative experiments, it is found that the geodesic‐dome‐division‐based discrete spherical image representation approach can achieve a better result of camera pose estimation than equirectangular image and CubeMap representations. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

For Automatic High Beam (AHB), the authors are working on a study to classify beam of car headlights as 'High' and 'Low' by realizing the presence of vehicle on road from nighttime in‐vehicle camera image using deep learning. In general, weights of deep learning model are pre‐trained on a large dataset which are then used as initial weights for other tasks such as image recognition. However, it has been reported that even if the pre‐trained weights are used as the initial weights, it is not a factor of accuracy improvement when domain differs. It is important to pre‐train a model on a dataset that is suitable for target domain. Therefore, we propose a method to generate a pre‐training dataset that can be easily created by using visual explanation which represents where a deep learning model is looking at when performs a task. Then, we applied the proposed pre‐training dataset on the headlight beam classification in the nighttime in‐vehicle camera image and verified the effectiveness of our method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper proposes a new fish bone detection technique using a convolutional neural network (CNN) and synthetic image generation. Semantic segmentation CNNs with supervised learning generally require a large number of training images and their pixel‐wise teaching signals. In fish bone detection, there are two problems with using semantic segmentation CNNs. One is the manual annotations of fish bones and the other is the difficulty of sampling all variations of fish bones with various lengths, angles, and thicknesses. The proposed method, however, generates them by drawing virtual fish bones on X‐ray images. This technique is very useful for reducing the cost of collecting and annotating a dataset. Experimental results have shown that the average F‐measure for the proposed method is 0.747, while that for a normal training method is 0.493. In the proposed method, the CNN successfully detected actual fish bones despite its training only with virtual fish bones. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN), as one of the mainstream deep learning models, has achieved great success in image recognition. All neurons used in CNN are based on the McCulloch‐Pitts model, which is over‐simplified. To further improve CNN's learning capacity, this paper proposes a novel dendritic CNN (DCNN), which considers the nonlinear information processing functions of dendrites in a single neuron. The superiority of DCNN is confirmed based on four widely used image recognition tasks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This letter presents several combinations of a convolutional neural network (CNN) and its learning method for rotation‐invariant digit recognition. Rotation data augmentation is widely used for improving rotation invariance. Data augmentation commonly assigns the same label to all augmented images of the same source. However, this learning method causes some collisions between original and rotated digits. Thus, this letter presents three types of rotation‐invariance learning methods and applies them to five popular CNN architectures. Experimental results indicate that multi‐task learning on ResNet‐50 is the best combination. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This study was aimed at detecting flower areas in Convallaria keiskei colonies by using unmanned‐aerial‐vehicle (UAV) images. To estimate the number of Convallaria keiskei colonies, we focused on the effective features obtained from the information of flower color and location in the image obtained from the UAV. We proposed a method for estimating the number of colonies by combining image processing and machine learning based on the characteristics of Convallaria keiskei flowers. To evaluate the new effect of the proposed method, we compared the results obtained from the proposed method with those obtained from the comparison method that uses image processing focusing only on brightness and saturation. The results suggest that the proposed method that combines image processing and machine learning is effective for estimating the number of Convallaria keiskei colonies. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]