INVARIANT OBJECT RECOGNITION BASED ON A NEURAL NETWORK OF CASCADED RCE NETS

1993 ◽  
Vol 07 (04) ◽  
pp. 815-829 ◽  
Author(s):  
WEI LI ◽  
NASSER M. NASRABADI
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
Feature Vector ◽  
Recognition Rate ◽  
Feature Space ◽  
Recognition System ◽  
Coulomb Energy ◽  
Geometrical Shape ◽  
Shape Information ◽  
Shift Rotation

A neural network of cascaded Restricted Coulomb Energy (RCE) nets is constructed for the recognition of two-dimensional objects. A number of RCE nets are cascaded together to form a classifier where the overlapping decision regions are progressively resolved by a set of cascaded networks. Similarities among objects which have complex decision boundaries in the feature space are resolved by this multi-net approach. The generalization ability of an RCE net recognition system, referring to the ability of the system to correctly recognize a new pattern even when the number of learning exemplars is small, is increased by the proposed coarse-to-fine learning strategy. A feature extraction technique is used to map the geometrical shape information of an object into an ordered feature vector of fixed length. This feature vector is then used as an input to the neural network. The feature vector is invariant to object changes such as positional shift, rotation, scaling, illumination variance, variation of camera setup, perspective distortion, and noise distortion. Experimental results for recognition of several objects are also presented. A correct recognition rate of 100% was achieved for both the training and the testing input patterns.

Speaker Recognition With Normal and Telephonic Assamese Speech Using I-Vector and Learning-Based Classifier

2017 ◽  
pp. 256-280
Author(s):  
Mridusmita Sharma ◽  
Rituraj Kaushik ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Feed Forward Neural Network ◽  
Individual Behaviour ◽  
Behavioural Characteristics ◽  
Regional Languages ◽  
Optimal Feature

Speaker recognition is the task of identifying a person by his/her unique identification features or behavioural characteristics that are included in the speech uttered by the person. Speaker recognition deals with the identity of the speaker. It is a biometric modality which uses the features of the speaker that is influenced by one's individual behaviour as well as the characteristics of the vocal cord. The issue becomes more complex when regional languages are considered. Here, the authors report the design of a speaker recognition system using normal and telephonic Assamese speech for their case study. In their work, the authors have implemented i-vectors as features to generate an optimal feature set and have used the Feed Forward Neural Network for the recognition purpose which gives a fairly high recognition rate.

scholarly journals Recognizing Cursive Typewritten Text Using Segmentation-Free System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad S. Khorsheed
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Feature Space ◽  
Recognition System ◽  
Image Feature ◽  
Text Recognition ◽  
Statistical Features ◽  
Free System ◽  
Novel Method ◽  
Image Pixels

Feature extraction plays an important role in text recognition as it aims to capture essential characteristics of the text image. Feature extraction algorithms widely range between robust and hard to extract features and noise sensitive and easy to extract features. Among those feature types are statistical features which are derived from the statistical distribution of the image pixels. This paper presents a novel method for feature extraction where simple statistical features are extracted from a one-pixel wide window that slides across the text line. The feature set is clustered in the feature space using vector quantization. The feature vector sequence is then injected to a classification engine for training and recognition purposes. The recognition system is applied to a data corpus which includes cursive Arabic text of more than 600 A4-size sheets typewritten in multiple computer-generated fonts. The system performance is compared to a previously published system from the literature with a similar engine but a different feature set.

Design of Online Handwritten Numeral Recognition System

2012 ◽  
Vol 201-202 ◽  
pp. 329-332
Author(s):  
Yue Fen Chen ◽  
Jun Huan Lin ◽  
Guo Ping Li
Keyword(s):  
Neural Network ◽  
Memory Function ◽  
Recognition Rate ◽  
Binary Sequence ◽  
Hopfield Neural Network ◽  
Recognition System ◽  
Handwritten Numeral Recognition ◽  
Fast Recognition ◽  
Starting Point ◽  
Numeral Recognition

An effective online handwritten numeral recognition system is designed based on the Matlab GUI interface. The coordinate locations of the handwritten numerals are recorded, from which the stroke direction variations and the 2-dimensional distance between the starting point and ending point of the numeral are obtained as the features, which are encoded into 42 bits binary sequence, and then input to the Hopfield neural network. The associative memory function of the Hopfield neural network can implement the learning and recognition of the handwritten numeral. Testing results show that the designed system has high recognition rate and fast recognition speed.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

scholarly journals Identification betweenStephania tetrandraS. Moore andStephania cepharanthaHayata by CWT–FTIR–RBFNN

2008 ◽  
Vol 22 (5) ◽  
pp. 371-386 ◽  
Author(s):  
Chang-Jiang Zhang ◽  
Cun-Gui Cheng
Keyword(s):  
Neural Network ◽  
Feature Vector ◽  
Recognition Rate ◽  
Total Reflection ◽  
Chemical Components ◽  
Continuous Wavelet ◽  
Feature Extracting ◽  
Training Samples ◽  
The Difference ◽  
Stephania Tetrandra

Horizontal attenuation total reflection–Fourier transform infrared spectroscopy (HATR–FTIR) is used to measure the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. Because they belong to the same family and the same genus Chinese traditional medicinal materials, their chemical components are very similar. In order to extrude the difference betweenStephania tetrandraS. Moore andStephania cepharanthaHayata, continuous wavelet transform (CWT) is used to decompose the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. Three main scales are selected as the feature extracting space in the CWT domain. According the distribution of FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata, three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to the radius basis function neural network (RBFNN) to train so as to accurately classify theStephania tetrandraS. Moore andStephania cepharanthaHayata. 128 couples of FTIR are used to train and test the proposed method, where 78 couples of data are used as training samples and 50 couples of data are used as testing samples. Experimental results show that the accurate recognition rate betweenStephania tetrandraS. Moore andStephania cepharanthaHayata is respectively 99.8 and 99.9% by using the proposed method.

scholarly journals A Convolutional Neural Network Algorithm for the Optimization of Emergency Nursing Rescue Efficiency for Critical Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xueyan Chen ◽  
Xiaofei Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Nursing ◽  
Network Algorithm ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Critical Patients

In order to help pathologists quickly locate the lesion area, improve the diagnostic efficiency, and reduce missed diagnosis, a convolutional neural network algorithm for the optimization of emergency nursing rescue efficiency of critical patients was proposed. Specifically, three convolution layers and convolution kernels of different sizes are used to extract the features of patients’ posture behavior, and the classifier of patients’ posture behavior recognition system is used to learn the feature information by capturing the nonlinear relationship between the features to achieve accurate classification. By testing the accuracy of patient posture behavior feature extraction, the recognition rate of a certain action, and the average recognition rate of all actions in the patient body behavior recognition system, it is proved that the convolution neural network algorithm can greatly improve the efficiency of emergency nursing. The algorithm is applied to the patient posture behavior detection system, so as to realize the identification and monitoring of patients and improve the level of intelligent medical care. Finally, the open source framework platform is used to test the patient behavior detection system. The experimental results show that the larger the test data set is, the higher the accuracy of patient posture behavior feature extraction is, and the average recognition rate of patient posture behavior category is 97.6%, thus verifying the effectiveness and correctness of the system, to prove that the convolutional neural network algorithm has a very large improvement of emergency nursing rescue efficiency.

A Hybrid Approach For Automatic Licence Plate Recognition System

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Sharma ◽  
Pawan Kumar Dahiya ◽  
Baldev Raj Marwah
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Support Vector ◽  
Network Support ◽  
Recognition Systems ◽  
Licence Plate Recognition

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

scholarly journals Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4782
Author(s):  
Huan-Chung Li ◽  
Telung Pan ◽  
Man-Hua Lee ◽  
Hung-Wen Chiu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Speech Emotion Recognition ◽  
Single Observation ◽  
Artificial Neural

In recent years, many types of research have continued to improve the environment of human speech and emotion recognition. As facial emotion recognition has gradually matured through speech recognition, the result of this study provided more accurate recognition of complex human emotional performance, and speech emotion identification will be derived from human subjective interpretation into the use of computers to automatically interpret the speaker’s emotional expression. Focused on use in medical care, which can be used to understand the current feelings of physicians and patients during a visit, and improve the medical treatment through the relationship between illness and interaction. By transforming the voice data into a single observation segment per second, the first to the thirteenth dimensions of the frequency cestrum coefficients are used as speech emotion recognition eigenvalue vectors. Vectors for the eigenvalue vectors are maximum, minimum, average, median, and standard deviation, and there are 65 eigenvalues in total for the construction of an artificial neural network. The sentiment recognition system developed by the hospital is used as a comparison between the sentiment recognition results of the artificial neural network classification, and then use the foregoing results for a comprehensive analysis to understand the interaction between the doctor and the patient. Using this experimental module, the emotion recognition rate is 93.34%, and the accuracy rate of facial emotion recognition results can be 86.3%.

scholarly journals Investigation on Intelligent Recognition System of Instrument Based on Multi-step Convolution Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 185-192
Author(s):  
Feng Shan ◽  
◽  
Hui Sun ◽  
Xiaoyun Tang ◽  
Weiwei Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Rate ◽  
Test Site ◽  
Recognition System ◽  
Image Features ◽  
Convolution Neural Network ◽  
Digital Display ◽  
Industrial Control ◽  
Intelligent Recognition

Digital instruments are widely used in industrial control, traffic, equipment displays and other fields because of the intuitive characteristic of their test data. Aiming at the character recognition scene of digital display Vernier caliper, this paper creatively proposes an intelligent instrument recognition system based on multi-step convolution neural network (CNN). Firstly, the image smples are collected from the Vernier caliper test site, and their resolution and size are normalized. Then the CNN model was established to train the image smples and extract the features. The digital display region in the image smples were extracted according to the image features, and the numbers in the Vernier caliper were cut out. Finally, using the MINIST datas set of Vernier caliper is established, and the CNN model is used to recognize it. The test results show that the overall recognition rate of the proposed CNN model is more than 95%, and has good robustness and generalization ability.

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