scholarly journals Markerless tracking using Polar Correlation of camera optical flow

2010 ◽  
Author(s):  
Prince Gupta ◽  
Niels da Vitoria Lobo ◽  
Joseph J. Laviola
Keyword(s):  
Optical Flow ◽  
Markerless Tracking ◽  
Polar Correlation

Markerless tracking and gesture recognition using polar correlation of camera optical flow

2012 ◽  
Vol 24 (3) ◽  
pp. 651-666 ◽  
Author(s):  
Prince Gupta ◽  
Niels da Vitoria Lobo ◽  
Joseph J. Laviola
Keyword(s):  
Optical Flow ◽  
Gesture Recognition ◽  
Markerless Tracking ◽  
Polar Correlation

Mental Models "Midst the Optical Flow

1992 ◽  
Author(s):  
Jeremy M. A. Beer
Keyword(s):  
Optical Flow ◽  
Mental Models

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

Locating Method for Vehicle at Intersection Based on TPVM and Optical Flow

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Tao Chen ◽  
Linkun Fan ◽  
Xuchuan Li ◽  
Congshuai Guo ◽  
Miaomiao Qiao
Keyword(s):  
Optical Flow

Research of On-Road Obstacle Detection and Risk Degree Demonstration by Analyzing Difference between Model Flow Field and Observed Optical Flow Field

2009 ◽  
Vol 129 (5) ◽  
pp. 792-799
Author(s):  
Takashi Yamanaka ◽  
Masayuki Kashima ◽  
Kiminori Sato ◽  
Mutsumi Watanabe ◽  
Jun Ogata
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Obstacle Detection ◽  
Optical Flow Field ◽  
Model Flow ◽  
Risk Degree

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

Wavelet-based Optical Flow Velocimetry (wOFV) Applied to Tagging Velocimetry Data

2021 ◽  
Author(s):  
Tobin Gevelber ◽  
Bryan E. Schmidt ◽  
Muhammad A. Mustafa ◽  
David Shekhtman ◽  
Nick J. Parziale
Keyword(s):  
Optical Flow ◽  
Flow Velocimetry

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%

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