scholarly journals PERFORMANCE EVALUATION OF SELF-QUOTIENT IMAGE METHODS

2020 ◽  
Vol 2 (1) ◽  
pp. 8-14
Author(s):  
V. O. Parubochyi ◽  
◽  
R. Ya. Shuvar ◽  
Keyword(s):  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Evaluation Framework ◽  
Support Vector ◽  
Negative Effects ◽  
Support Vector Classifier ◽  
Normalization Methods ◽  
Filter Kernel ◽  
The Difference

Lighting Normalization is an especially important issue in the image recognitions systems since different illumination conditions can significantly change the recognition results, and the lighting normalization allows minimizing negative effects of various illumination conditions. In this paper, we are evaluating the recognition performance of several lighting normalization methods based on the Self-Quotient ImagE(SQI) method introduced by Haitao Wang, Stan Z. Li, Yangsheng Wang, and Jianjun Zhang. For evaluation, we chose the original implementation and the most perspective latest modifications of the original SQI method, including the Gabor Quotient ImagE(GQI) method introduced by Sanun Srisuk and Amnart Petpon in 2008, and the Fast Self-Quotient ImagE(FSQI) method and its modifications proposed by authors in previous works. We are proposing an evaluation framework which uses the Cropped Extended Yale Face Database B, which allows showing the difference of the recognition results for different illumination conditions. Also, we are testing all results using two classifiers: Nearest Neighbor Classifier and Linear Support Vector Classifier. This approach allows us not only to calculate recognition accuracy for each method and select the best method but also show the importance of the proper choice of the classification method, which can have a significant influence on recognition results. We were able to show the significant decreasing of recognition accuracy for un-processed (RAW) images with increasing the angle between the lighting source and the normal to the object. From the other side, our experiments had shown the almost uniform distribution of the recognition accuracy for images processed by lighting normalization methods based on the SQI method. Another showed but expected result represented in this paper is the increasing of the recognition accuracy with the increasing of the filter kernel size. However, the large filter kernel sizes are much more computationally expensive and can produce negative effects on output images. Also, we were shown in our experiments, that the second modification of the FSQI method, called FSQI3, is better almost in all cases for all filter kernel sizes, especially, if we use Linear Support Vector Classifier for classification.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3306 ◽  
Author(s):  
Tan Guo ◽  
Xiao-Ping Lu ◽  
Yong-Xiong Zhang ◽  
Keping Yu
Keyword(s):  
Spectral Data ◽  
Nearest Neighbor ◽  
Surface Composition ◽  
Rapid Development ◽  
Feature Learning ◽  
Component Analysis ◽  
Classification Performance ◽  
Support Vector ◽  
Negative Effects ◽  
Learning Machine

With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 μm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM).

Condensed Nearest Neighbor Decision Rule Input Weight Sequential Feed-Forward Neural Networks

2014 ◽  
Vol 574 ◽  
pp. 712-717 ◽  
Author(s):  
Shu Xia Lu ◽  
Yang Fan Zhou ◽  
Bin Liu
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Decision Rule ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Feed Forward ◽  
Optimization Constraints ◽  
Feed Forward Neural Networks ◽  
The Difference ◽  
Learning Machine

This paper proposes a new approach is referred to as condensed nearest neighbor decision rule (CNN) input weight sequential feed-forward neural networks (CIW-SFFNS). In this paper, it is firstly shown that the difference of optimization constraints between the extreme learning machine (ELM) and constrained-optimization-based extreme learning machine. For the second time, this paper proposes a method that using CNN to select the hidden-layer weights from example. Moreover, we compare error minimized extreme learning machines (EM-ELM), support vector sequential feed-forward neural networks (SV-SFFNS) and CIW-SFFNS from two aspects:test accuracy and the number of hidden nodes. We present the result of an experimental study on 10 classification sets. The CIW-SFFNS algorithm has a statistically significant improvement in generalization performance than EM-ELM and SV-SFFNS.

scholarly journals The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6291
Author(s):  
Lin Meng ◽  
Jun Pang ◽  
Ziyao Wang ◽  
Rui Xu ◽  
Dong Ming
Keyword(s):  
Data Fusion ◽  
Lower Limb ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Window Size ◽  
Linear Acceleration ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Acceleration Data

Locomotion recognition and prediction is essential for real-time human–machine interactive control. The integration of electromyography (EMG) with mechanical sensors could improve the performance of locomotion recognition. However, the potential of EMG in motion prediction is rarely discussed. This paper firstly investigated the effect of surface EMG on the prediction of locomotion while integrated with inertial data. We collected EMG signals of lower limb muscle groups and linear acceleration data of lower limb segments from ten healthy participants in seven locomotion activities. Classification models were built based on four machine learning methods—support vector machine (SVM), k-nearest neighbor (KNN), artificial neural network (ANN), and linear discriminant analysis (LDA)—where a major vote strategy and a content constraint rule were utilized for improving the online performance of the classification decision. We compared four classifiers and further investigated the effect of data fusion on the online locomotion classification. The results showed that the SVM model with a sliding window size of 80 ms achieved the best recognition performance. The fusion of EMG signals does not only improve the recognition accuracy of steady-state locomotion activity from 90% (using acceleration data only) to 98% (using data fusion) but also enables the prediction of the next steady locomotion (∼370 ms). The study demonstrates that the employment of EMG in locomotion recognition could enhance online prediction performance.

scholarly journals Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

2019 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Aditya Surya Wijaya ◽  
Nurul Chamidah ◽  
Mayanda Mega Santoni
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Zone Method ◽  
Handwritten Character ◽  
Testing Data ◽  
Handwritten Recognition

Handwritten characters are difficult to be recognized by machine because people had various own writing style. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is worked by preprocessing handwritten image, segmentation to obtain separate single characters, feature extraction, and classification. Features extraction is done by utilizing Zone method that will be used for classification by splitting this features data to training data and testing data. Training data from extracted features reduced by K-Support Vector Nearest Neighbor (K-SVNN) and for recognizing handwritten pattern from testing data, we used K-Nearest Neighbor (KNN). Testing result shows that reducing training data using K-SVNN able to improve handwritten character recognition accuracy.

scholarly journals Evaluation of surface EMG-based recognition algorithms for decoding hand movements

2019 ◽  
Vol 58 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Sara Abbaspour ◽  
Maria Lindén ◽  
Hamid Gholamhosseini ◽  
Autumn Naber ◽  
Max Ortiz-Catalan
Keyword(s):  
Processing Time ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Likelihood Estimation ◽  
Surface Emg ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Prosthetic Hands ◽  
Logarithmic Energy

AbstractMyoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

scholarly journals Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4566 ◽  
Author(s):  
Zhe Li ◽  
Yongpeng Xu ◽  
Xiuchen Jiang
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Partial Discharge ◽  
Back Propagation ◽  
Training Sample ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Set Size ◽  
Sample Set

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

Automatic Recognition of Legal Amounts on Indian Bank Cheques

2020 ◽  
Vol 10 (4) ◽  
pp. 54-73
Author(s):  
Mohammad Idrees Bhat ◽  
B. Sharada
Keyword(s):  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Local Binary Patterns ◽  
Automatic Recognition ◽  
Support Vector ◽  
Histogram Of Oriented Gradients ◽  
Data Set ◽  
Feature Representations ◽  
Decision Level ◽  
Effective Representation

Holistic-based approaches attempt to represent an entire handwritten word as an indivisible entity by representing it with feature representations. Despite the presence of various feature representations, it still remains a challenge to get the effective representation for Devanagari Legal amounts. In this paper, an attempt is made to represent legal amounts with histogram of oriented gradients (HOG) and local binary patterns (LBP) for their characterization. Thereafter, two fusion-based models are proposed. In the first model, HOG and LBP are fused at feature level and, in second, at decision level. Later, recognition is performed with the nearest neighbor and support vector machine classifiers. For corroboration of the efficacy of the proposed models several experiments have been conducted on ICDAR ' 11 Devanagari Legal amount dataset. Experimental results demonstrate that fusion based approaches are effective by achieving significant improvement in recognition accuracy as compared to individual feature representations and other contemporary approaches employed on the data set.

scholarly journals Comparison of convolutional neural network and bag of features for multi-font digit recognition

2019 ◽  
Vol 15 (3) ◽  
pp. 1322
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Nur Syafiqah Mohd Nur Hidayah ◽  
Norasiah Mohammad ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Method ◽  
Digit Recognition ◽  
Bag Of Features

<span>This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text.  BoF is a popular machine learning method while CNN is a popular deep learning method.  Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.</span>

Decision Fusion Gait Recognition Based on Bayesian Rule and Support Vector Machine

2013 ◽  
Vol 411-414 ◽  
pp. 1287-1290 ◽  
Author(s):  
Hong Zheng ◽  
Kai Zhang
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
National Laboratory ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Decision Fusion ◽  
Support Vector ◽  
Correct Recognition Rate

To distinguish people’s identities, the information is normally included in one gait periodic sequence image. First, the gait energy image for feature extraction of wavelet moments was constructed. After boundary unwrapping, the gait silhouette boundary was extracted and principal component analysis (PCA) was use to obtain its compressed contour features. Then nearest neighbor classifier and support vector machines were applied for classification of these two features. Finally, support vector machine (SVM) on Bayesian rule were used to complete gait recognition with information fusion of different features. The method is evaluated on the National Laboratory of Pattern Recognition (NLPR) gait database and the correct recognition rate is relatively high. The experimental results show that the proposed method has good recognition performance.

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