scholarly journals A Method of Effective Text Extraction for Complex Video Scene

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Guo ◽  
Yuan Li ◽  
Yi Wang ◽  
Shu Liu ◽  
Tao Lei ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Detection Accuracy ◽  
Text Extraction ◽  
Local Texture ◽  
Video Images ◽  
Harris Corner ◽  
Indexing And Retrieval ◽  
Video Scene ◽  
Different Types ◽  
Text Information

Text information contains important information for video analysis, indexing, and retrieval. Effective and efficient text extraction has been a challenging topic in recent years. Focusing on this issue, a text extraction method for complex video scene is proposed in this paper. Multiframe corner matching and heuristic rules are combined together to detect the text region candidates, which solves the issue of Harris corner filtration for complex video scene and also improves the detection accuracy using multiframe fusion. Local texture description is then used for similarity evaluation judged by SVM. Experimental results for 4 different types of 395-frame video images show the effectiveness of the proposed method compared with 5 existing text extraction methods.

A Review of Different Techniques of Age Estimation from Human Face

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Khemchandra Patel ◽  
Dr. Kamlesh Namdev
Keyword(s):  
Age Estimation ◽  
Real Life ◽  
Extraction Methods ◽  
Human Face ◽  
Age Changes ◽  
Missing Children ◽  
Different Types ◽  
Age Variation ◽  
Human Faces ◽  
Facial Age

Age changes cause major variations in the appearance of human faces. Due to many lifestyle factors, it is difficult to precisely predict how individuals may look with advancing years or how they looked with "retreating" years. This paper is a review of age variation methods and techniques, which is useful to capture wanted fugitives, finding missing children, updating employee databases, enhance powerful visual effect in film, television, gaming field. Currently there are many different methods available for age variation. Each has their own advantages and purpose. Because of its real life applications, researchers have shown great interest in automatic facial age estimation. In this paper, different age variation methods with their prospects are reviewed. This paper highlights latest methodologies and feature extraction methods used by researchers to estimate age. Different types of classifiers used in this domain have also been discussed.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

scholarly journals Detecting Toe-Off Events Utilizing a Vision-Based Method

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 329 ◽  
Author(s):  
Yunqi Tang ◽  
Zhuorong Li ◽  
Huawei Tian ◽  
Jianwei Ding ◽  
Bingxian Lin
Keyword(s):  
Wearable Sensors ◽  
Gait Pattern ◽  
Video Data ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Public Database ◽  
Video Frames ◽  
Different Types ◽  
Events Detection ◽  
Good Detection

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

scholarly journals Application of Extraction and Determination Based on Deep Eutectic Solvents in Different Types of Environmental Samples

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 46
Author(s):  
Yonghua Wang ◽  
Na Li ◽  
Shengnan Jiang ◽  
Xi Chen
Keyword(s):  
Extraction Efficiency ◽  
Deep Eutectic Solvents ◽  
Extraction Methods ◽  
Water Sources ◽  
Environmental Research ◽  
Environmental Matrices ◽  
Controllable Synthesis ◽  
Surrounding Environment ◽  
Target Analytes ◽  
Different Types

Water sources are an indispensable resource for human survival. Monitoring the pollution status of the surrounding environment is necessary to protect water sources. Research on the environmental matrix of deep eutectic solvents (DESs) has expanded rapidly because of their high extraction efficiency for various target analytes, controllable synthesis, and versatile structure. Following the synthesis of hydrophobic deep eutectic solvents (HDESs), their application in aqueous matrices broadened greatly. The present review conducted a survey on the pollutant extraction methods based DESs in environmental matrices from two aspects, application methods and matrix types; discussed the potential risk of DESs to the environment and future development trends; and provided some references for researchers to choose DES-based extraction methods for environmental research.

scholarly journals Evaluation of Biochar Nitrate Extraction Methods

2019 ◽  
Vol 9 (17) ◽  
pp. 3514 ◽  
Author(s):  
Jenna Walsh ◽  
Joseph Sanford ◽  
Rebecca Larson
Keyword(s):  
Extraction Method ◽  
Extraction Efficiency ◽  
Agricultural Soils ◽  
Extraction Methods ◽  
Multiple Methods ◽  
Soil Extraction ◽  
Future Studies ◽  
Different Types ◽  
Increasing Temperature ◽  
Biochar Amendment

Biochar amendment to soil is a method used to mitigate losses of nitrogen leaching through agricultural soils. Multiple methods for extraction of nitrogen have been used, and recent studies have indicated that traditional soil extraction methods underestimate biochar nitrate. This study evaluated the nitrate extraction efficiency of a KCl extraction method under different temperature (20 and 50 °C) and duration (24 and 96 h) conditions. Increasing the duration of extraction from 24 to 96 h did not have a significant impact on extraction efficiency. However, increasing temperature resulted in nitrate extraction efficiencies above 90%. Rinsing the biochar once with deionized (DI) water following filtration after extraction increased the extraction efficiency significantly, but any subsequent rinses were not significant. This study recommends extracting nitrate from biochar using 2 M KCl at 50 °C for a period of 24 h with one additional rinse to increase nitrate recovery above 90%. However, future studies should evaluate this procedure for different types of biochar produced from alternative biomasses and at varying temperatures.

scholarly journals Extending Appearance Based Gait Recognition with Depth Data

2019 ◽  
Vol 9 (24) ◽  
pp. 5529
Author(s):  
Kristijan Lenac ◽  
Diego Sušanj ◽  
Adnan Ramakić ◽  
Domagoj Pinčić
Keyword(s):  
Information Fusion ◽  
Recognition Accuracy ◽  
Gait Recognition ◽  
Computational Cost ◽  
Video Stream ◽  
Depth Data ◽  
Video Images ◽  
Extraction Step ◽  
Different Types ◽  
Subject Identification

Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.

Text Extraction from Complex Background Images

2013 ◽  
Vol 765-767 ◽  
pp. 975-979
Author(s):  
Chao Liu ◽  
Fei Peng Da ◽  
Chen Xing Wang
Keyword(s):  
High Performance ◽  
Rapid Development ◽  
Corner Detection ◽  
Internet Technology ◽  
Digital Information ◽  
Text Extraction ◽  
Text Understanding ◽  
Complex Background ◽  
Text Information

The rapid development of internet technology leads to an effective way to share ideas with digital information. Extracting text information from digital information especially complex background images has become increasingly important due to the demand of text understanding. In this paper, we propose a k-means clustering based method for text extraction. After locating text regions with the corner detection, an improved k-means algorithm is adopted to extract text from complex background. Experiment results show the high performance of the presented method.

Fall Detection and Daily Living Activity Recognition Logic Regression

2020 ◽  
Vol 17 (8) ◽  
pp. 3520-3525
Author(s):  
J. Refonaa ◽  
Bandaru Suhas ◽  
B. V. S. Bhaskar ◽  
S. L. JanyShabu ◽  
S. Dhamodaran ◽  
...  
Keyword(s):  
Daily Living ◽  
Detection System ◽  
Fall Detection ◽  
Learning System ◽  
Detection Accuracy ◽  
Logic Regression ◽  
Daily Living Activity ◽  
Average Value ◽  
Different Types ◽  
Angular Velocities

It is a must to bring the fall detection system in to use with the increasing number of elder people in the world, because the most of them tend live voluntarily and at risk of injuries. Falls are dangerous in a few cases and could even lead to deadly injuries. A very robust fall detection system must be built in order to counter this problem. Here, we establish fall detection and recognition of daily live behavior through machine learning system. In order to detect different types of activities, including the detection of falls and day to-day activities, We use 2 shared archives for the accelerating and lateral speed data during this development. Logistic regression is used to determine motions such as drop, walk, climb, sit, stand and lie bases on the accelerating data and data on angular velocities. More specifically, the triaxial acceleration average value is used to achieve fall detection accuracy.

scholarly journals Flame Recognition in Video Images with Color and Dynamic Features of Flames

2019 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Jiaqing Chen ◽  
Xiaohui Mu ◽  
Yinglei Song ◽  
Menghong Yu ◽  
Bing Zhang
Keyword(s):  
Bp Neural Network ◽  
Color Model ◽  
False Alarms ◽  
Detection Accuracy ◽  
Dynamic Features ◽  
Network Testing ◽  
Video Images ◽  
Important Approach ◽  
Flame Detection ◽  
Hierarchical Features

Recently, video based flame detection has become an important approach for early detection of fire under complex circumstances. However, the detection accuracy of most existing methods remains unsatisfactory. In this paper, we develop a new algorithm that can significantly improve the accuracy of flame detection in video images. The algorithm segments a video image and obtains areas that may contain flames by combining a two-step clustering based approach with the RGB color model. A few new dynamic and hierarchical features associated with the suspected regions, including the flicker frequency of flames, are then extracted and analyzed. The algorithm determines whether a suspected region contains flames or not by processing the color and dynamic features of the area altogether with a BP neural network. Testing results show that this algorithm is robust and efficient, and is able to significantly reduce the probability of false alarms.

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