scholarly journals Physical Integrity Attack Detection of Surveillance Camera with Deep Learning based Video Frame Interpolation

2019 ◽  
Author(s):  
Jonathan Pan
Keyword(s):  
Deep Learning ◽  
Attack Detection ◽  
Video Frame ◽  
Frame Interpolation ◽  
Surveillance Camera ◽  
Physical Integrity

scholarly journals Analysis of Coronary Angiography Video Interpolation Methods to Reduce X-ray Exposure Frequency Based on Deep Learning

2021 ◽  
Author(s):  
Dong-xue Liang
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Heart Surgery ◽  
Frame Rate ◽  
Video Frame ◽  
X Rays ◽  
Frame Interpolation ◽  
X Ray ◽  
Video Frame Rate

Cardiac coronary angiography is a major technique that assists physicians during interventional heart surgery. Under X-ray irradiation, the physician injects a contrast agent through a catheter and determines the coronary arteries’ state in real time. However, to obtain a more accurate state of the coronary arteries, physicians need to increase the frequency and intensity of X-ray exposure, which will inevitably increase the potential for harm to both the patient and the surgeon. In the work reported here, we use advanced deep learning algorithms to find a method of frame interpolation for coronary angiography videos that reduces the frequency of X-ray exposure by reducing the frame rate of the coronary angiography video, thereby reducing X-ray-induced damage to physicians. We established a new coronary angiography image group dataset containing 95,039 groups of images extracted from 31 videos. Each group includesthree consecutive images, which are used to train the video interpolation network model. We apply six popular frameinterpolation methods to this dataset to confirm that the video frame interpolation technology can reduce the video frame rate and reduce exposure of physicians to X-rays.

scholarly journals Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging

2021 ◽  
Author(s):  
Martin Priessner ◽  
David C.A. Gaboriau ◽  
Arlo Sheridan ◽  
Tchern Lenn ◽  
Jonathan R. Chubb ◽  
...  
Keyword(s):  
Deep Learning ◽  
Light Exposure ◽  
Super Resolution ◽  
Single Particle Tracking ◽  
Video Frame ◽  
Learning Networks ◽  
Frame Interpolation ◽  
Cellular Processes ◽  
Image Pairs ◽  
Content Aware

The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFI's performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disc confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.

scholarly journals Video Frame Interpolation via Deformable Separable Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 10607-10614 ◽  
Author(s):  
Xianhang Cheng ◽  
Zhenzhong Chen
Keyword(s):  
State Of The Art ◽  
Video Frame ◽  
Kernel Size ◽  
Frame Interpolation ◽  
Interpolation Methods ◽  
Video Frames ◽  
Convolution Process ◽  
Strong Performance ◽  
Existing Frames ◽  
Better Than

Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical flow. However, when scene motion is larger than the pre-defined kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional flow-based methods are specific instances of the proposed DSepConv. Experimental results demonstrate that our method significantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.

Coordinated data falsification attack detection in the domain of distributed generation using deep learning

2022 ◽  
Vol 134 ◽  
pp. 107345
Author(s):  
Narayan Bhusal ◽  
Mukesh Gautam ◽  
Raj Mani Shukla ◽  
Mohammed Benidris ◽  
Shamik Sengupta
Keyword(s):  
Deep Learning ◽  
Distributed Generation ◽  
Attack Detection
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