scholarly journals Silicone-Based Molding Technique for Optical Flow Analysis in Transparent Models of Fluidic Components

2018 ◽  
Vol 8 (4) ◽  
pp. 512
Author(s):  
Mark Schult ◽  
Christoph Drobek ◽  
Hermann Seitz
Keyword(s):  
Optical Flow ◽  
Flow Analysis

scholarly journals Intelligent Sports Video Classification Based on Deep Neural Network (DNN) Algorithm and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoping Guo
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Transfer Learning ◽  
Deep Neural Network ◽  
Mutation Detection ◽  
Video Retrieval ◽  
Flow Analysis ◽  
Key Frame Extraction ◽  
Sports Video ◽  
Key Frame

Traditional text annotation-based video retrieval is done by manually labeling videos with text, which is inefficient and highly subjective and generally cannot accurately describe the meaning of videos. Traditional content-based video retrieval uses convolutional neural networks to extract the underlying feature information of images to build indexes and achieves similarity retrieval of video feature vectors according to certain similarity measure algorithms. In this paper, by studying the characteristics of sports videos, we propose the histogram difference method based on using transfer learning and the four-step method based on block matching for mutation detection and fading detection of video shots, respectively. By adaptive thresholding, regions with large frame difference changes are marked as candidate regions for shots, and then the shot boundaries are determined by mutation detection algorithm. Combined with the characteristics of sports video, this paper proposes a key frame extraction method based on clustering and optical flow analysis, and experimental comparison with the traditional clustering method. In addition, this paper proposes a key frame extraction algorithm based on clustering and optical flow analysis for key frame extraction of sports video. The algorithm effectively removes the redundant frames, and the extracted key frames are more representative. Through extensive experiments, the keyword fuzzy finding algorithm based on improved deep neural network and ontology semantic expansion proposed in this paper shows a more desirable retrieval performance, and it is feasible to use this method for video underlying feature extraction, annotation, and keyword finding, and one of the outstanding features of the algorithm is that it can quickly and effectively retrieve the desired video in a large number of Internet video resources, reducing the false detection rate and leakage rate while improving the fidelity, which basically meets people’s daily needs.

scholarly journals Optical flow analysis reveals that Kinesin-mediated advection impacts the orientation of microtubules in the Drosophila oocyte

2020 ◽  
Vol 31 (12) ◽  
pp. 1246-1258 ◽  
Author(s):  
Maik Drechsler ◽  
Lukas F. Lang ◽  
Layla Al-Khatib ◽  
Hendrik Dirks ◽  
Martin Burger ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Cytoplasmic Streaming ◽  
Flow Analysis ◽  
Flow Motion ◽  
A Cell

Here we introduce an optical flow motion estimation approach to study microtubule (MT) orientation in the Drosophila oocyte, a cell displaying substantial cytoplasmic streaming. We show that MT polarity is affected by the regime of these flows and, furthermore, that the presence of flows is necessary for MTs to adopt their proper polarity.

Video Object Detection Base on RGB and Optical Flow Analysis

2019 ◽  
Author(s):  
Shunyao Zhang ◽  
Tian Wang ◽  
Chuanyun Wang ◽  
Yan Wang ◽  
Guangcun Shan ◽  
...  
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Flow Analysis ◽  
Video Object

scholarly journals Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity

NeuroImage ◽  
2017 ◽  
Vol 153 ◽  
pp. 58-74 ◽  
Author(s):  
Navvab Afrashteh ◽  
Samsoon Inayat ◽  
Mostafa Mohsenvand ◽  
Majid H. Mohajerani
Keyword(s):  
Optical Flow ◽  
Optical Imaging ◽  
Brain Activity ◽  
Flow Analysis ◽  
Spatiotemporal Dynamics

Optical Flow and Prediction Residual Based Hybrid Forensic System for Inter-Frame Tampering Detection

2017 ◽  
Vol 26 (07) ◽  
pp. 1750107 ◽  
Author(s):  
Raahat Devender Singh ◽  
Naveen Aggarwal
Keyword(s):  
Optical Flow ◽  
Flow Analysis ◽  
Digital Content ◽  
Digital Videos ◽  
Brightness Gradient ◽  
Gradient Component ◽  
Quantitative Results ◽  
Prediction Residual ◽  
Detection And Localization ◽  
Inter Frame

In the wake of widespread proliferation of inexpensive and easy-to-use digital content editing software, digital videos have lost the idealized reputation they once held as universal, objective and infallible evidence of occurrence of events. The pliability of digital content and its innate vulnerability to unobtrusive alterations causes us to become skeptical of its validity. However, in spite of the fact that digital videos may not always present a truthful picture of reality, their usefulness in today’s world is incontrovertible. Therefore, the need to verify the integrity and authenticity of the contents of a digital video becomes paramount, especially in critical scenarios such as defense planning and legal trials where reliance on untrustworthy evidence could have grievous ramifications. Inter-frame tampering, which involves insertion/removal/replication of sets of frames into/from/within a video sequence, is among the most un-convoluted and elusive video forgeries. In this paper, we propose a potent hybrid forensic system that detects inter-frame forgeries in compressed videos. The system encompasses two forensic techniques. The first is a novel optical flow analysis based frame-insertion and removal detection procedure, where we focus on the brightness gradient component of optical flow and detect irregularities caused therein by post-production frame-tampering. The second component is a prediction residual examination based scheme that expedites detection and localization of replicated frames in video sequences. Subjective and quantitative results of comprehensive tests on an elaborate dataset under diverse experimental set-ups substantiate the effectuality and robustness of the proposed system.

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