Wavelet-Based Artificial Light Receptor Model for Human Face Recognition

In general, the sewer inspection usually employs a great number of CCTV images to discover sewer failures by human interpretation. A computer-aided program remains to be developed due to human's fatigue and subjectivity. To enhance the efficiency of sewer inspection, this paper attends to apply artificial intelligence to extract the failure features of the sewer systems that is demonstrated on the sewer system in the eastern Taichung City, Taiwan. Wavelet transform and gray-level co-occurrence matrix, which have been widely applied in many texture analyses, are adopted in this research to generate extracted features, which are the most valuable information in pattern recognition of failures on CCTV images. Wavelet transform is capable of dividing an image into four sub-images including approximation sub-image, horizontal detail sub-image, vertical detail sub-image, and diagonal detail sub-image. The co-occurrence matrices of horizontal orientation, vertical orientation, and 45° and 135° orientations, respectively, were calculated for the horizontal, vertical, and diagonal detail sub-images. Subsequently, the features including angular second moment, entropy, contrast, homogeneity, dissimilarity, correlation, and cluster tendency, can be obtained from the co-occurrence matrices. However, redundant features either decrease the accuracy of texture description or increase the difficulty of pattern recognition. Thus, the correlations of the features are estimated to search the appropriate feature sets according to the correlation coefficients between the features. In addition, a discriminant analysis was used to evaluate the discriminability of the features for the pipe failure defection, and entropy, correlation, and cluster tendency were found to be the best features based on the discriminant accuracy through an error matrix analysis.

Section: Introductionmentioning

confidence: 96%

Feature Extraction of Sewer Pipe Defects Using Wavelet Transform and Co-Occurrence Matrix

Yang

Pan

et al. 2011

“…5 Further, the mean-squared error minimization is for the spectrum of speech, not its envelope, and the spectrum is highly sensitive to noise as well. 5 Wavelet-based features have been proposed for various pattern recognition applications in the field of biomedical signals, 6 microarray data classification, 7 face recognition, 8 and speech recognition application. [9][10][11][12][13] Wavelet-based features when compared to MFCC features have shown better recognition performance for phoneme recognition 11 isolated digit recognition task 12 and monophone recognition under stressed speech.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Sub-Band Based Temporal Features for Robust Hindi Phoneme Recognition

Farooq

Datta

Shrotriya

2010

This paper proposes the use of wavelet transform-based feature extraction technique for Hindi speech recognition application. The new proposed features take into account temporal as well as frequency band energy variations for the task of Hindi phoneme recognition. The recognition performance achieved by the proposed features is compared with the standard MFCC and 24-band admissible wavelet packet-based features using a linear discriminant function based classifier. To evaluate robustness of these features, the NOISEX database is used to add different types of noise into phonemes to achieve signal-to-noise ratios in the range of 20 dB to −5 dB. The recognition results show that under noisy background the proposed technique always achieves a better performance over MFCC-based features.

Feature Extraction of Motor Imagery Eeg Based on Wavelet Transform and Higher-Order Statistics

Yang

Song

Bao-guo

2010

Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.