Classification of Esophageal and Bolus Movements for Evaluation of Swallowing Function by Ultrasound Video Processing Using Optical Flow and Stable Extremal Region Method

2019 ◽  
Vol 139 (5) ◽  
pp. 603-608 ◽  
Author(s):  
Yutaka Suzuki ◽  
Kyosuke Hatsushika ◽  
Keisuke Masuyama ◽  
Osamu Sakata ◽  
Morimasa Tanimoto ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Swallowing Function ◽  
Region Method

scholarly journals Classification of esophageal and bolus movements for evaluation of swallowing function by ultrasound video processing using optical flow and stable extremal region method

2019 ◽  
Vol 102 (9) ◽  
pp. 19-26 ◽  
Author(s):  
Yutaka Suzuki ◽  
Kyosuke Hatsushika ◽  
Keisuke Masuyama ◽  
Osamu Sakata ◽  
Morimasa Tanimoto ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Swallowing Function ◽  
Region Method

scholarly journals End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

2021 ◽  
Vol 7 (5) ◽  
pp. 90
Author(s):  
Slim Hamdi ◽  
Samir Bouindour ◽  
Hichem Snoussi ◽  
Tian Wang ◽  
Mohamed Abid
Keyword(s):  
Anomaly Detection ◽  
Optical Flow ◽  
Video Processing ◽  
Background Subtraction ◽  
Video Stream ◽  
Learning Method ◽  
End To End ◽  
Spatio Temporal ◽  
Uav Images

In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl), the generation loss (Gl) and the compactness loss (Cl) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3.

Connectionist Temporal Classification Model for Dynamic Hand Gesture Recognition using RGB and Optical flow Data

2020 ◽  
Vol 17 (4) ◽  
pp. 497-506
Author(s):  
Sunil Patel ◽  
Ramji Makwana
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Classification Model ◽  
Hand Gesture ◽  
Flow Data ◽  
Dynamic Hand Gesture Recognition ◽  
Connectionist Temporal Classification

Automatic classification of dynamic hand gesture is challenging due to the large diversity in a different class of gesture, Low resolution, and it is performed by finger. Due to a number of challenges many researchers focus on this area. Recently deep neural network can be used for implicit feature extraction and Soft Max layer is used for classification. In this paper, we propose a method based on a two-dimensional convolutional neural network that performs detection and classification of hand gesture simultaneously from multimodal Red, Green, Blue, Depth (RGBD) and Optical flow Data and passes this feature to Long-Short Term Memory (LSTM) recurrent network for frame-to-frame probability generation with Connectionist Temporal Classification (CTC) network for loss calculation. We have calculated an optical flow from Red, Green, Blue (RGB) data for getting proper motion information present in the video. CTC model is used to efficiently evaluate all possible alignment of hand gesture via dynamic programming and check consistency via frame-to-frame for the visual similarity of hand gesture in the unsegmented input stream. CTC network finds the most probable sequence of a frame for a class of gesture. The frame with the highest probability value is selected from the CTC network by max decoding. This entire CTC network is trained end-to-end with calculating CTC loss for recognition of the gesture. We have used challenging Vision for Intelligent Vehicles and Applications (VIVA) dataset for dynamic hand gesture recognition captured with RGB and Depth data. On this VIVA dataset, our proposed hand gesture recognition technique outperforms competing state-of-the-art algorithms and gets an accuracy of 86%

scholarly journals Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

2019 ◽  
Vol 8 (11) ◽  
pp. 1887-1892
Keyword(s):  
Optical Flow ◽  
Video Sequence ◽  
Human Action ◽  
Video Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Video Frames ◽  
Integral Role ◽  
Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

Massively parallel Lucas Kanade optical flow for real-time video processing applications

2014 ◽  
Vol 11 (4) ◽  
pp. 713-730 ◽  
Author(s):  
Aurélien Plyer ◽  
Guy Le Besnerais ◽  
Frédéric Champagnat
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Video Processing ◽  
Massively Parallel

Automatic classification of kinematic flap variants using ultrasound and optical flow

2020 ◽  
Vol 148 (4) ◽  
pp. 2655-2655
Author(s):  
Matthew Faytak ◽  
Connor Mayer ◽  
Jennifer Kuo ◽  
G. Teixeira ◽  
Z. L. Zhou
Keyword(s):  
Optical Flow ◽  
Automatic Classification

scholarly journals Measuring Surface Velocity of Water Flow by Dense Optical Flow Method

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2320
Author(s):  
Wu ◽  
Zhao ◽  
Gan ◽  
Ma
Keyword(s):  
Optical Flow ◽  
Water Flow ◽  
Video Processing ◽  
Processing Technology ◽  
Surface Velocity ◽  
Flow Method ◽  
Optical Flow Method ◽  
Dense Optical Flow ◽  
Measuring Surface ◽  
Relative Errors

Recent advances in video processing technology have provided a new approach to measuring the surface velocity of water flow (SVWF). However, most of the previous researches using video processing technology depended on tracers for target tracing, requiring spraying tracers in the measurement process. These methods are not convenient for velocity measurement. In this study, a dense optical flow method (Farneback optical flow method) was used to process the water flow video to get the estimated SVWFs. The estimated SVWFs were verified by the actual SVWFs measured by a portable propeller velocimeter. The regression analyses between the estimated SVWFs and the measured SVWFs were conducted. The coefficient of determinations (R2) of the estimated and the measured SVWFs in different test regions are between 0.81 and 0.85. The average relative errors of the estimated and the measured SVWFs in all test regions are no more than 6.5%. The results indicate that the method had a good accuracy in estimating the SVWF and is a feasible and promising approach to analyzing the surface velocity distribution of water flow.

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