scholarly journals Building Recognition System Based on Improved SIFT Algorithm and Positioning Information

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Tao Liu ◽  
◽  
Feng Jiang ◽  
Yu Gao ◽  
◽  
...  
Keyword(s):  
General Problem ◽  
Recognition Rate ◽  
Response Speed ◽  
Recognition System ◽  
Small Extent ◽  
Sift Algorithm ◽  
Gps Positioning ◽  
Building Recognition ◽  
Processing Mechanism

In order to solve the general problem, that is, the accurate recognition rate is low in a small extent or when the image resources are few and scattered. This article puts forward a building recognition system that combines GPS positioning information with an improved SIFT algorithm, and adds a pre-processing mechanism to predict the possibility of the building existence in the system, which further reduces mismatch and improves response speed. The final verification shows that this research is actually effective.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

scholarly journals Application of portrait recognition system for emergency evacuation in mass emergencies

2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu
Keyword(s):  
Detection Rate ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Evacuation ◽  
Shopping Mall ◽  
Single Shot ◽  
Data Set ◽  
Linear Discriminant ◽  
Adaboost Algorithm

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

Design of Gait Recognition System Based on FPGA and DSP

2014 ◽  
Vol 687-691 ◽  
pp. 3861-3868
Author(s):  
Zheng Hong Deng ◽  
Li Tao Jiao ◽  
Li Yan Liu ◽  
Shan Shan Zhao
Keyword(s):  
Monitoring System ◽  
Nearest Neighbor ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Digital Signal ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Zernike Moment ◽  
Intelligent Monitoring ◽  
The One

According to the trend of the intelligent monitoring system, on the basis of the study of gait recognition algorithm, the intelligent monitoring system is designed based on FPGA and DSP; On the one hand, FPGA’s flexibility and fast parallel processing algorithms when designing can be both used to avoid that circuit can not be modified after designed; On the other hand, the advantage of processing the digital signal of DSP is fully taken. In the feature extraction and recognition, Zernike moment is selected, at the same time the system uses the nearest neighbor classification method which is more mature and has good real-time performance. Experiments show that the system has high recognition rate.

scholarly journals Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition

2021 ◽  
Vol 10 (3) ◽  
pp. 185
Author(s):  
Chenyang Zhang ◽  
Qingli Shi ◽  
Li Zhuo ◽  
Fang Wang ◽  
Haiyan Tao
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Remote Sensing Data ◽  
Tensor Decomposition ◽  
Decomposition Algorithm ◽  
Sensing Data ◽  
Building Recognition ◽  
Planning Decisions ◽  
Mixed Use ◽  
The Status

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.

A Prototype for Brazilian Bankcheck Recognition

1997 ◽  
Vol 11 (04) ◽  
pp. 549-569 ◽  
Author(s):  
Luan L. Lee ◽  
Miguel G. Lizarraga ◽  
Natanael R. Gomes ◽  
Alessandro L. Koerich
Keyword(s):  
Information Extraction ◽  
Character Recognition ◽  
Type I Error ◽  
Recognition Rate ◽  
Simulated Data ◽  
Recognition System ◽  
Signature Verification ◽  
Type I ◽  
Verification Algorithm ◽  
Commercial Applications

This paper describes a prototype for Brazilian bankcheck recognition. The description is divided into three topics: bankcheck information extraction, digit amount recognition and signature verification. In bankcheck information extraction, our algorithms provide signature and digit amount images free of background patterns and bankcheck printed information. In digit amount recognition, we dealt with the digit amount segmentation and implementation of a complete numeral character recognition system involving image processing, feature extraction and neural classification. In signature verification, we designed and implemented a static signature verification system suitable for banking and commercial applications. Our signature verification algorithm is capable of detecting both simple, random and skilled forgeries. The proposed automatic bankcheck recognition prototype was intensively tested by real bankcheck data as well as simulated data providing the following performance results: for skilled forgeries, 4.7% equal error rate; for random forgeries, zero Type I error and 7.3% Type II error; for bankcheck numerals, 92.7% correct recognition rate.

scholarly journals Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

2021 ◽  
Vol 22 (1) ◽  
pp. 187
Author(s):  
Arjun Benagatte Channegowda ◽  
H N Prakash
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Research Work ◽  
Recognition Rate ◽  
Recognition System ◽  
Multimodal Biometrics ◽  
Histograms Of Oriented Gradients ◽  
Multi Level ◽  
Deep Learning Neural Network ◽  
Hidden Neurons

Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

scholarly journals Practical Recognition System for Text Printed on Clear Reflected Material

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Khader Mohammad ◽  
Sos Agaian
Keyword(s):  
Processing Time ◽  
Recognition Rate ◽  
Recognition System ◽  
Experimental Results ◽  
Research Fields ◽  
Average Recognition Rate ◽  
Clear Plastic ◽  
Novel Methods ◽  
Processor Speed ◽  
Background Contrast

Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields. While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of images, (c) dotted text printed on curved reflective material, and/or (d) touching characters. Methods were evaluated using a total of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10 seconds (using MATLAB R2008A on an HP 8510 W with 4 G of RAM and 2.3 GHz of processor speed), and experimental results yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development.

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