scholarly journals A Model-Based Approach of Foreground Region of Interest Detection for Video Codecs

2019 ◽  
Vol 9 (13) ◽  
pp. 2670
Author(s):  
Zhewei Zhang ◽  
Tao Jing ◽  
Bowen Ding ◽  
Meilin Gao ◽  
Xuejing Li
Keyword(s):  
Video Compression ◽  
Computing Time ◽  
Region Of Interest ◽  
Video Codecs ◽  
Model Based ◽  
Foreground Region ◽  
Wide Range ◽  
Interest Detection ◽  
Different Characteristics ◽  
Fine Tune

Detecting the Region of Interest (ROI) for video clips is a significant and useful technique both in video codecs and surveillance/monitor systems. In this paper, a new model-based detection method is designed which suits video compression codecs by proposing two models: an “inter” and “intra” model. The “inter” model exploits the motion information represented as blocks by global motion compensation approaches while the “intra” model extracts the objects details through objects filtering and image segmentation procedures. Finally, the detection results are formed through a new clustering with fine-tune approach from the “intra” model assisted with the “inter” model. Experimental results show that the proposed method fits well for real-time video codecs and it achieves a good performance both on detection precision and on computing time. In addition, the proposed method is versatile for a wide range of surveillance videos with different characteristics.

scholarly journals Flow-Process Foreground Region of Interest Detection Method for Video Codecs

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 16263-16276 ◽  
Author(s):  
Zhewei Zhang ◽  
Tao Jing ◽  
Jingning Han ◽  
Yaowu Xu ◽  
Xuejing Li
Keyword(s):  
Detection Method ◽  
Region Of Interest ◽  
Flow Process ◽  
Video Codecs ◽  
Foreground Region ◽  
Interest Detection

Spatial Interpolators for Intra-Frame Resampling of SAR Videos: A Comparative Study Using Real-Time HD, Medical and Radar Data

2020 ◽  
Vol 15 (2) ◽  
pp. 144-196 ◽  
Author(s):  
Mohammad R. Khosravi ◽  
Sadegh Samadi ◽  
Reza Mohseni
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Video Compression ◽  
Nearest Neighbor ◽  
Radar Data ◽  
Edge Preservation ◽  
Edge Information ◽  
Video Codecs ◽  
Comparative Results ◽  
Interesting Area

Background: Real-time video coding is a very interesting area of research with extensive applications into remote sensing and medical imaging. Many research works and multimedia standards for this purpose have been developed. Some processing ideas in the area are focused on second-step (additional) compression of videos coded by existing standards like MPEG 4.14. Materials and Methods: In this article, an evaluation of some techniques with different complexity orders for video compression problem is performed. All compared techniques are based on interpolation algorithms in spatial domain. In details, the acquired data is according to four different interpolators in terms of computational complexity including fixed weights quartered interpolation (FWQI) technique, Nearest Neighbor (NN), Bi-Linear (BL) and Cubic Cnvolution (CC) interpolators. They are used for the compression of some HD color videos in real-time applications, real frames of video synthetic aperture radar (video SAR or ViSAR) and a high resolution medical sample. Results: Comparative results are also described for three different metrics including two reference- based Quality Assessment (QA) measures and an edge preservation factor to achieve a general perception of various dimensions of the mentioned problem. Conclusion: Comparisons show that there is a decidable trade-off among video codecs in terms of more similarity to a reference, preserving high frequency edge information and having low computational complexity.

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

Hierarchical region-of-interest detection

2006 ◽  
Vol 45 (7) ◽  
pp. 077201 ◽  
Author(s):  
Huibao Lin
Keyword(s):  
Region Of Interest ◽  
Interest Detection

Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

Numerical Jet Atomization: Part II — Modeling Information and Comparison With DNS Results

2006 ◽  
Author(s):  
P. A. Beau ◽  
T. Me´nard ◽  
R. Lebas ◽  
A. Berlemont ◽  
S. Tanguy ◽  
...  
Keyword(s):  
Level Set ◽  
Volume Fraction ◽  
Computing Time ◽  
Mass Conservation ◽  
Liquid Volume ◽  
Liquid Density ◽  
Wide Range ◽  
The Mean ◽  
Break Up ◽  
Primary Break

The main objective of our work is to develop direct numerical simulation tools for the primary break up of a jet. Results can help to determine closure relation in the ELSA model [1] which is based on a single-phase Eulerian model and on the transport equation for the mean liquid/gas interface density in turbulent flows. DNS simulations are carried out to obtain statistical information in the dense zone of the spray where nearly no experimental data are available. The numerical method should describe the interface motion precisely, handle jump conditions at the interface without artificial smoothing, and respect mass conservation. We develop a 3D code [2], where interface tracking is ensured by Level Set method, Ghost Fluid Method [3] is used to capture accurately sharp discontinuities, and coupling between Level Set and VOF methods is used for mass conservation [4]. Turbulent inflow boundary conditions are generated through correlated random velocities with a prescribed length scale. Specific care has been devoted to improve computing time with MPI parallelization. The numerical methods have been applied to investigate physical processes that are involved in the primary break up of an atomizing jet. The chosen configuration is close as possible of Diesel injection (Diameter D = 0.1 mm, Velocity = 100m/s, Liquid density = 696kg/m3, Gas density = 25kg/m3). Typical results will be presented. From the injector nozzle, the turbulence initiates some perturbations on the liquid surface, that are enhanced by the mean shear between the liquid jet and the surrounding air. The interface becomes very wrinkled and some break-up is initiated. The induced liquid parcels show a wide range of shapes. Statistics are carried out and results will be provided for liquid volume fraction, liquid/gas interface density, and turbulent correlations.

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