scholarly journals Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks

2019 ◽  
Vol 9 (13) ◽  
pp. 2646 ◽  
Author(s):  
Christoph Wick ◽  
Alexander Hartelt ◽  
Frank Puppe
Keyword(s):  
Original Writing ◽  
Detection Algorithm ◽  
Accuracy Rate ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Novel Approach ◽  
12Th Century ◽  
Symbol Detection ◽  
Scanned Documents ◽  
Staff Line

Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th–12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F 1 -score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an F 1 -score of over 96%.

scholarly journals Staff, Symbol, and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks

2019 ◽  
Author(s):  
Christoph Wick ◽  
Alexander Hartelt ◽  
Frank Puppe
Keyword(s):  
Original Writing ◽  
Detection Algorithm ◽  
Accuracy Rate ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Novel Approach ◽  
12Th Century ◽  
Symbol Detection ◽  
Scanned Documents ◽  
Staff Line

Even today, the automatic digitisation of scanned documents in general but especially the automatic optical music recognition (OMR) of historical manuscripts still remain an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th-12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes, that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neume and pitch, that is melody information that can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm, based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F1-score of over 99% for both detecting lines and complete staves. For the music symbol detection we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm recognises these symbols with an F1-score of over 96% if the type is ignored and predicts the true symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%. If only the NCs without their respective connection to a neume, all clefs, and accidentals are of interest the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%.

Pedestrian detection algorithm based on dual-model fused fully convolutional networks(Invited)

2018 ◽  
Vol 47 (2) ◽  
pp. 203001
Author(s):  
罗海波 Luo Haibo ◽  
何 淼 He Miao ◽  
惠 斌 Hui Bin ◽  
常 铮 Chang Zheng
Keyword(s):  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Dual Model ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals An ECG Signal Classification Method Based on Dilated Causal Convolution

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hao Ma ◽  
Chao Chen ◽  
Qing Zhu ◽  
Haitao Yuan ◽  
Liming Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Signal Classification ◽  
Accuracy Rate ◽  
Ecg Signals ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Output Time

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent neural network and the nondisclosure of future information, the network is constructed with fully convolutional networks and causal convolution. To reduce the network depth and prevent gradient explosion or gradient disappearance, the dilated factor is introduced into the model, and the residual blocks are introduced into the model according to the shortcut connection idea. The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65%.

scholarly journals Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Meijun Yang ◽  
Xiaoyan Xiao ◽  
Zhi Liu ◽  
Longkun Sun ◽  
Wei Guo ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Dimensional Reconstruction ◽  
Feature Pyramid ◽  
The Mean ◽  
Accuracy Rates

Background. Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases. Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation. In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers. Method. RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time. RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task. Results. In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously. We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis. We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views. Our study shows that both classification and detection effects are noteworthy. The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively. The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively.

scholarly journals Small and Dim Ship Target Detection Based on Sea-Sky-Line

2019 ◽  
Vol 37 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Yaohui Hu ◽  
Ke Zhang ◽  
Chao Xing
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Sea Surface ◽  
Line Detection ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Noise Point ◽  
The Mean ◽  
Potential Area

In order to solve the problem of small and dim ship target detection under complex sea-sky background, we propose a target detection algorithm based on sea-sky line detection. Firstly, the paper locates the sea-sky-line based on fully convolutional networks, through which target potential area can be determined and disturbance can be excluded. Then the method based on the mean of four detection gradient is adopted to detect the small and dim ship target. The simulation results show that the method of sea-sky-line detection based on fully convolutional networks can overcome the disadvantages of the traditional methods and is suitable for complex background. The detection method proposed can filter the white noise point on the sea surface and thus can reduce false alarm, through which the detection of small and dim ship can be completed well.

scholarly journals Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jeremy M. Webb ◽  
Duane D. Meixner ◽  
Shaheeda A. Adusei ◽  
Eric C. Polley ◽  
Mostafa Fatemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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