scholarly journals An SVM-Based Scheme for Automatic Identification of Architectural Line Features and Cracks

2020 ◽  
Vol 10 (15) ◽  
pp. 5077
Author(s):  
Mahshid Zeighami Moghaddam ◽  
Gessica Umili ◽  
Vito Messina ◽  
Sabrina Bonetto ◽  
Anna Maria Ferrero ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Crack Detection ◽  
Vertical Gradient ◽  
Support Vector ◽  
Automatic Identification ◽  
Morphological Approach ◽  
Surface Image ◽  
Map Generation ◽  
Local Gradient ◽  
Connectivity Rule

This research investigates fundamental problems in object recognition in earthen heritage and addresses the possibility of an automatic crack detection method for rammed earth images. We propose and validate a straightforward support vector machine (SVM)-based bidirectional morphological approach to automatically generate crack and texture line maps through transforming a surface image into an intermediate representation. Rather than relying on the application of the eight connectivity rule to a combination of horizontal and vertical gradient to extract edges, we instruct an edge classifier in the form of a support vector machine from features computed on each direction separately. The model couples a bidirectional local gradient and geometrical characteristics. It constitutes of four elements: (1) bidirectional edge maps; (2) bidirectional equivalent connected component maps; (3) SVM-based classifier and (4) crack and architectural line feature map generation. Relevant details are discussed in each part. Finally, the efficiency of the proposed algorithm is verified in a set of simulations that is satisfactorily conforming to labeled data provided manually for surface images of earthen heritage.

scholarly journals Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

2009 ◽  
Vol 27 (No. 6) ◽  
pp. 393-402 ◽  
Author(s):  
H. Lin ◽  
J. Zhao ◽  
Q. Chen ◽  
J. Cai ◽  
P. Zhou
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Crack Detection ◽  
Support Vector Machine Model ◽  
Acoustic Resonance ◽  
Support Vector ◽  
Machine Model ◽  
Nearest Neighbours

A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

Eggshell Crack Detection and Egg Classification Using Resonance and Support Vector Machine Methods

2019 ◽  
Vol 35 (1) ◽  
pp. 23-30
Author(s):  
Ching-Wei Cheng ◽  
Pei-Hsuan Feng ◽  
Jun-Hong Xie ◽  
Yu-Kai Weng
Keyword(s):  
Support Vector Machine ◽  
Characteristic Frequency ◽  
Crack Detection ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Identification Accuracy ◽  
Support Vector ◽  
Training Set ◽  
Processed Products ◽  
Resonance Detection

Abstract. Cracks in eggshells not only affect the egg preservation time but also reduce the success rate for the end-processed products. This study was based on the theory of resonant inspection (RI). The use of the support vector machine (SVM) method as a means of more accurate eggshell crack detection was evaluated. The results revealed that comparing the resonant frequency and amplitude by using a microphone as a sensor allowed non-cracked eggs to be distinguished from cracked eggs. The characteristic frequency of a non-cracked egg was between 4130 and 5500 Hz, and its amplitude was between 0.16 and 0.20 V. The spectrum of a cracked egg was fuzzy, with no obvious characteristic frequency, and the maximum amplitude was approximately 0.06 V. The identification accuracy was 99% and 98% for the SVM training set and testing set, respectively. These results prove that the resonance detection method is effective for identifying eggs with cracked shells. Keywords: Eggshells, Resonant inspection, Fast Fourier transform, Support vector machine.

Eggshell crack detection using a wavelet-based support vector machine

2010 ◽  
Vol 70 (1) ◽  
pp. 135-143 ◽  
Author(s):  
Xiaoyan Deng ◽  
Qiaohua Wang ◽  
Hong Chen ◽  
Hong Xie
Keyword(s):  
Support Vector Machine ◽  
Crack Detection ◽  
Support Vector

Crack detection and segmentation in CT images using Hessian matrix and support vector machine

2021 ◽  
Vol 29 (10) ◽  
pp. 2517-2527
Author(s):  
Yong-ning ZOU ◽  
◽  
Zhi-bin ZHANG ◽  
Qi LI ◽  
Hao-song YU ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Crack Detection ◽  
Hessian Matrix ◽  
Ct Images ◽  
Support Vector

scholarly journals AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS

2010 ◽  
Vol 20 (02) ◽  
pp. 159-176 ◽  
Author(s):  
OLIVER FAUST ◽  
U. RAJENDRA ACHARYA ◽  
LIM CHOO MIN ◽  
BERNHARD H. C. SPUTH
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Gaussian Mixture ◽  
Power Density Spectrum ◽  
Estimation Methods ◽  
Support Vector ◽  
Automatic Identification ◽  
Spectrum Estimation ◽  
Classification Rate ◽  
Density Spectrum

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%.

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