Detection of sharp objects using deep neural network based object detection algorithm

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

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Deep Neural Network Based Salient Object Detection with Image Enhancement

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-04212-7_39 ◽

2018 ◽

pp. 444-453

Author(s):

Lecheng Zhou ◽

Xiaodong Gu

Keyword(s):

Neural Network ◽

Object Detection ◽

Image Enhancement ◽

Deep Neural Network ◽

Salient Object Detection ◽

Salient Object

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Object Detection to Assist Visually Impaired People: A Deep Neural Network Adventure

Advances in Visual Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-030-03801-4_44 ◽

2018 ◽

pp. 500-510 ◽

Cited By ~ 5

Author(s):

Fereshteh S. Bashiri ◽

Eric LaRose ◽

Jonathan C. Badger ◽

Roshan M. D’Souza ◽

Zeyun Yu ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Visually Impaired ◽

Deep Neural Network ◽

Visually Impaired People ◽

Impaired People

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UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-163-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 163-167

Author(s):

W. Yuan ◽

Z. Fan ◽

X. Yuan ◽

J. Gong ◽

R. Shibasaki

Keyword(s):

Neural Network ◽

Object Detection ◽

Image Matching ◽

Deep Neural Network ◽

Aerial Images ◽

Surface Model ◽

Learning Technology ◽

Ground Sample ◽

Volume Calculation ◽

Dense Image

Abstract. Dense image matching is essential to photogrammetry applications, including Digital Surface Model (DSM) generation, three dimensional (3D) reconstruction, and object detection and recognition. The development of an efficient and robust method for dense image matching has been one of the technical challenges due to high variations in illumination and ground features of aerial images of large areas. Nowadays, due to the development of deep learning technology, deep neural network-based algorithms outperform traditional methods on a variety of tasks such as object detection, semantic segmentation and stereo matching. The proposed network includes cost-volume computation, cost-volume aggregation, and disparity prediction. It starts with a pre-trained VGG-16 network as a backend and using the U-net architecture with nine layers for feature map extraction and a correlation layer for cost volume calculation, after that a guided filter based cost aggregation is adopted for cost volume filtering and finally the soft Argmax function is utilized for disparity prediction. The experimental conducted on a UAV dataset demonstrated that the proposed method achieved the RMSE (root mean square error) of the reprojection error better than 1 pixel in image coordinate and in-ground positioning accuracy within 2.5 ground sample distance. The comparison experiments on KITTI 2015 dataset shows the proposed unsupervised method even comparably with other supervised methods.

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