scholarly journals Research on Low-Resolution Pattern Coding Recognition Method Based on Hu-DBN

2020 ◽  
Vol 38 (6) ◽  
pp. 1218-1224
Author(s):  
Tianfan Zhang ◽  
Zhe Li ◽  
Xiao Jing ◽  
Bin Hu ◽  
Yahui Zhu
Keyword(s):  
Recognition Rate ◽  
Visual Navigation ◽  
Motion Blur ◽  
The Body ◽  
Low Resolution ◽  
Recognition Method ◽  
Image Code ◽  
Characteristic Image ◽  
Motion Speed ◽  
Characteristic Signal

The feature image code represented by the two-dimensional code is the key reference for global positioning in the visual navigation of mobile robots. Although reducing the acquired low-resolution image helps to reduce the real-time performance of the algorithm, the acquired feature image is more susceptible to motion blur-based interference and affects the accuracy of recognition, which causes the positioning failure of the whole multi-intelligence, in which the body control system is invalid. In this paper, an optimized low-resolution feature image code recognition method is proposed. In the preprocessing part, the characteristic image is converted into the characteristic signal matrix of Hu invariant moments, and then the characteristic image is added to the characteristic signal matrix as a characteristic component, and then the Hu-DBN neural network signal classifier is used to construct the signal matrix so as to achieve accurate recognition of low-resolution custom image signature images under high motion tolerance conditions. It not only avoids the problem of classical pattern recognition relying on model experience and poor adaptability of the scene, but also avoids the problem of high computational complexity and recognition efficiency of directly deep learning methods such as YOLO. The deployment of the mobile robot instance deployment test shows that the average recognition rate is of 96.3% at a resolution of 640×480@Pixs and motion speed of 0.5 m/s, which proves the effectiveness of the present method.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

scholarly journals A Practical Method for the Automatic Recognition of Rock Structures in Panoramic Borehole Image during Deep-Hole Drilling Engineering

2021 ◽  
Vol 11 (21) ◽  
pp. 10490
Author(s):  
Xianjian Zou ◽  
Chuanying Wang ◽  
Huajun Zhang ◽  
Shuangyuan Chen
Keyword(s):  
Recognition Rate ◽  
Automatic Recognition ◽  
Characteristic Functions ◽  
Hole Drilling ◽  
Time Cost ◽  
Recognition Method ◽  
Structural Plane ◽  
Drilling Engineering ◽  
Borehole Image ◽  
Borehole Imaging

Digital panoramic borehole imaging technology has been widely used in the practice of drilling engineering. Based on many high-definition panoramic borehole images obtained by the borehole imaging system, this paper puts forward an automatic recognition method based on clustering and characteristic functions to perform intelligent analysis and automatic interpretation researches, and successfully applied to the analysis of the borehole images obtained at the Wudongde Hydropower Station in the south-west of China. The results show that the automatic recognition method can fully and quickly automatically identify most of the important structural planes and their position, dip, dip angle and gap width and other characteristic parameter information in the entire borehole image. The recognition rate of the main structural plane is about 90%. The accuracy rate is about 85%, the total time cost is about 3 h, and the accuracy deviation is less than 4% among the 12 boreholes with a depth of about 50 m. The application of automatic recognition technology to the panoramic borehole image can greatly improve work efficiency, reduce the time cost, and avoid the interference caused by humans, making it possible to automatically recognize the structural plane parameters of the full-hole image.

scholarly journals Recognizing Friend and Foe: Biometrics, Veridiction, and the Iraq War

2014 ◽  
Vol 12 (4) ◽  
pp. 516-527 ◽  
Author(s):  
Stefka Hristova
Keyword(s):  
Iraq War ◽  
War On Terror ◽  
Data Gathering ◽  
The State ◽  
The Body ◽  
Recognition Method ◽  
Unique Identity ◽  
Scientific Expertise ◽  
A Site ◽  
Identification Practices

In analyzing the deployment of biomertics in Iraq, argue that whereas the body was seen as a site of verification in 20th century surveillance and identification practices, in the ongoing War on Terror, and the Iraq War more specifically, it became a site of veridiction - a site in which the truth about the security of the state can be analyzed (Foucault 2008:32). The body thus became the basis for determining not so much one’s unique identity but one’s friendliness to the normative state order. Enemies could thus be identified and confined as a group, and in this process the state could be secured. In the ongoing of the War on Terror, the visual regime of veridiction has been further articulated to the logic of digital technologies in order to categorize an unfamiliar diverse population into a binary simplistic schema consistent of true and false, therefore friend or foe, and thus “go” - allowed to move through the country or “no go” - destined to be detained. In other words, the digitization of veridiction as the primary goal of biometrics is evident in the automation of the recognition method, the conversion of the archive into database, the transition away from the anthropological station onto mobile dispersed data-gathering enterprise, and replacement of scientific expertise with easy-to-use automated intelligence.

Research on Adaptive Face Gender Recognition Based on Compressive Sensing

2014 ◽  
Vol 989-994 ◽  
pp. 4187-4190 ◽  
Author(s):  
Lin Zhang
Keyword(s):  
Feature Extraction ◽  
Compressive Sensing ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Low Frequency ◽  
Face Image ◽  
Gender Recognition ◽  
Recognition Method ◽  
Frequency Information ◽  
Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

Computer Intelligent Nursing Care of Critically III Patients Based on Improved Patient Facial Expression Recognition Method

2014 ◽  
Vol 543-547 ◽  
pp. 2350-2353
Author(s):  
Xiao Yan Wan
Keyword(s):  
Facial Expression ◽  
Critically Ill ◽  
Nursing Care ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Appearance Model ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

In order to extract the expression features of critically ill patients, and realize the computer intelligent nursing, an improved facial expression recognition method is proposed based on the of active appearance model, the support vector machine (SVM) for facial expression recognition is taken in research, and the face recognition model structure active appearance model is designed, and the attribute reduction algorithm of rough set affine transformation theory is introduced, and the invalid and redundant feature points are removed. The critically ill patient expressions are classified and recognized based on the support vector machine (SVM). The face image attitudes are adjusted, and the self-adaptive performance of facial expression recognition for the critical patient attitudes is improved. New method overcomes the effect of patient attitude to the recognition rate to a certain extent. The highest average recognition rate can be increased about 7%. The intelligent monitoring and nursing care of critically ill patients are realized with the computer vision effect. The nursing quality is enhanced, and it ensures the timely treatment of rescue.

Establishment of Recommended TB Suspected Cases from Hospital system to TB Dispensary

2020 ◽  
Vol 11 (SPL4) ◽  
pp. 787-791
Author(s):  
Ganga Raju G ◽  
Subbarao K ◽  
Naveen P ◽  
Ramakrishnan V
Keyword(s):  
Community Health ◽  
Recognition Rate ◽  
The Body ◽  
Community Health Care ◽  
Medical Clinic ◽  
Treatment Needs ◽  
Social Contacts ◽  
Hospital System ◽  
The People ◽  
Latent Tb

2000 NPS appeared; just 12% of common Tuberculosis (TB) cases discovered were analyzed at TB dispensaries, whereas community health suppliers analyzed 88% of them. Unfortunately, just 13% of pervasive cases determined to have TB by community health care suppliers were named to TB dispensaries. To build a case recognition rate through fortified reference arrangement of TB cases and suspects from the medical clinic framework to TB dispensary. Through this venture, a component of the reference framework has been created; it is accessible and maintainable, particularly in poor and remote region. The TB is spread from one individual to next through the air while people who have dynamic TB in their spit, lungs hack, speak, or sneeze. The people with dormant TB don't spread sickness. The dynamic contamination occurs regularly in individuals with HIV/AIDS and individuals who smoke. Analysis of dynamic TB relied on chest X-beams, microscopic assessment and body liquids culture. The latent TB analysis relied on “tuberculin skin test (TST)” or blood tests. The TB usually affects the lungs, and nevertheless might affect diverse parts of the body. The most contamination display no manifestations; where case it is recognized as inert tuberculosis. Those at high danger incorporate workplace, household, and social contacts of individuals with dynamic TB. The treatment needs the usage of numerous antimicrobial over an extensive stretch of time.

scholarly journals Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2738
Author(s):  
Haiyan Jin ◽  
Le Xie ◽  
Zhaolin Xiao ◽  
Ting Zhou
Keyword(s):  
Visual Stimulation ◽  
Motion Blur ◽  
The Body ◽  
High Tech ◽  
Gravity Center ◽  
Balance Principle ◽  
Theoretical Support ◽  
Balance Ability ◽  
Human Balance ◽  
Differential Algorithm

The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients.

scholarly journals Optical Flow Filtering-Based Micro-Expression Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2056
Author(s):  
Junjie Wu ◽  
Jianfeng Xu ◽  
Deyu Lin ◽  
Min Tu
Keyword(s):  
Optical Flow ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Recognition Method ◽  
Video Clips ◽  
Low Degree ◽  
Micro Expression

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.

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