Journal articles: 'Wavelet Gabor'

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BAHRI, MAWARDI, and ECKHARD S. M. HITZER. "CLIFFORD ALGEBRA Cl3,0-VALUED WAVELET TRANSFORMATION, CLIFFORD WAVELET UNCERTAINTY INEQUALITY AND CLIFFORD GABOR WAVELETS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 06 (November 2007): 997–1019. http://dx.doi.org/10.1142/s0219691307002166.

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In this paper, it is shown how continuous Clifford Cl3,0-valued admissible wavelets can be constructed using the similitude group SIM(3), a subgroup of the affine group of ℝ3. We express the admissibility condition in terms of a Cl3,0 Clifford Fourier transform and then derive a set of important properties such as dilation, translation and rotation covariance, a reproducing kernel, and show how to invert the Clifford wavelet transform of multivector functions. We invent a generalized Clifford wavelet uncertainty principle. For scalar admissibility constant, it sets bounds of accuracy in multivector wavelet signal and image processing. As concrete example, we introduce multivector Clifford Gabor wavelets, and describe important properties such as the Clifford Gabor transform isometry, a reconstruction formula, and an uncertainty principle for Clifford Gabor wavelets.

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Raja, K. Bommanna, M. Madheswaran, and K. Thyagarajah. "EVALUATION OF ULTRASOUND KIDNEY IMAGES USING DOMINANT GABOR WAVELET (DoM-GW) FOR COMPUTER ASSISTED DISORDER IDENTIFICATION AND CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 19, no. 06 (December 2007): 395–407. http://dx.doi.org/10.4015/s1016237207000501.

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A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for the automated diagnosis and classification of few important kidney categories namely normal, medical renal diseases and cortical cyst. The acquired images are initially preprocessed to retain the pixels of kidney region. Out of 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by estimating the similarity metrics between original and reconstructed Gabor image. The Gabor features are then evaluated for each image. These derived features are mapped onto 2D feature space using k-mean clustering algorithm to group the data of similar class. The decision boundaries are formulated using linear discriminant function between the data sets of three kidney categories. A k-NN classifier module is used to identify the query input US kidney image category. The results show that the proposed dominant Gabor wavelet provides the classification efficiency of 87.33% for NR, 76.66% for MRD and 83.33% for CC. The overall classification efficiency improves by 18.89% compared to the classifier trained with features obtained by considering all the Gabor wavelets. The outputs of the proposed decision support systems are validated with medical expert to measure the actual efficiency. Also the overall discriminating ability of the systems is accessed with performance evaluation measure – f-score. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably. Hence, the proposed method enhances the objective classification and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images.

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Nazarkevych, Mariya, Yaroslav Voznyi, and Sergiy Dmytryk. "WAVELET TRANSFORMATION ATEB-GABOR FILTERS TO BIOMETRIC IMAGES." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 115–30. http://dx.doi.org/10.28925/2663-4023.2020.7.115130.

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Biometric images were pre-processed and filtered in two ways, by wavelet- Gabor and wavelet Ateb-gabor filtration. Ateb-based Gabor filter is effective for filtration because it contains generalizations of trigonometric functions. The wavelet transform of Ateb-Gabor function was developed. The function dependence on seven parameters was shown, each of them significantly changes the filtering results of biometric images. The Ateb-Gabor wavelet research was performed. Graphic dependencies of the wavelet Gabor filter and the wavelet Ateb-Gabor filter were constructed. The appliance of wavelet transform makes it possible to reduce the complexity of calculating an Ateb-Gabor filter by simplifying function calculations and reducing filtering time. The complexities of algorithms for calculating the wavelet Gabor filter and the wavelet Ateb-Gabor filter have been evaluated. Ateb-Gabor filtration allows you to adjust the intensity of the entire image, and to change certain ranges, thereby changing certain areas of the image. Biometric images should have this property, on which the minucius should be contrasting and clear. Ateb functions have the property of changing two rational parameters, which will allow to make more flexible control of filtration. The properties of the Ateb function, as well as the possibility of changing the amplitude of the function, the oscillation frequency by the numerical values of the Ateb-Gabor filter, were investigated. By using the parameters of the Ateb function, you can get a much larger range of shapes and sizes, which expands the number of possible filtration options. You can also perform filtration once, taking into account the direction of the minucius and reliably determine the sharpness of the edges, rather than perform filtration many times. The reliability of results were tested using NIST Special Database 302 and good filtration results were shown. This is confirmed by the comparison experiment between the wavelet-Gabor filter and the wavelet Ateb-Gabor function based on the PSNR signal-to-noise ratio measurement.

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Nazarkevych, Mariya, Yaroslav Voznyi, and Hanna Nazarkevych. "DEVELOPMENT OF MACHINE LEARNING METHOD WITH BIOMETRIC PROTECTION WITH NEW FILTRATION METHODS." Cybersecurity: Education, Science, Technique 3, no. 11 (2021): 16–30. http://dx.doi.org/10.28925/2663-4023.2021.11.1630.

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Biometric images were processed and filtered by a newly developed Ateb-Gabor wavelet filter. Identification of biometric images was performed by machine learning methods. The Gabor filter based on Ateb functions is effective for filtering because it contains generalizations of trigonometric functions. Developed wavelet transform of Ateb-Gabor function. It is shown that the function depends on seven parameters, each of which makes significant changes in the results of filtering biometric images. A study of the wavelet Ateb-Gabor function was performed. The graphical dependences of the Gabor filter wavelet and the Ateb-Gabor filter wavelet are constructed. The introduction of wavelet transforms reduces the complexity of Ateb-Gabor filter calculations by simplifying function calculations and reducing filtering time. The complexity of the algorithms for calculating the Gabor filter wavelet and the Ateb-Gabor filter wavelet is evaluated. Ateb-Gabor filtering allows you to change the intensity of the entire image, and to change certain ranges, and thus change certain areas of the image. It is this property that biometric images should have, in which the minions should be contrasting and clear. Ateb functions have the ability to change two rational parameters, which, in turn, will allow more flexible control of filtering. The properties of the Ateb function are investigated, as well as the possibility of changing the amplitude of the function, the oscillation frequency to the numerical values of the Ateb-Gabor filter. By using the parameters of the Ateb function, you can get a much wider range of shapes and sizes, which expands the number of possible filtering options. You can also implement once filtering, taking into account the direction of the minutes and reliably determine the sharpness of the edges, rather than filtering batocrates. The reliability results were tested on the basis of NIST Special Database 302, and good filtration results were shown. This was confirmed by a comparison experiment between the Wavelet-Gabor filtering and the Ateb-Gabor wavelet function based on the measurement of the PSNR signal-to-noise ratio.

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Sun, Wenchang, and Xingwei Zhou. "Irregular wavelet/Gabor frames." Applied and Computational Harmonic Analysis 13, no. 1 (July 2002): 63–76. http://dx.doi.org/10.1016/s1063-5203(02)00002-7.

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Zhang, Le Juan, Lu Zhang, Zhi Ming LI, and Shi Yao Cui. "Study of Gabor Features and Heart Sound Signal Recognition by the Principal Component Analysis." Applied Mechanics and Materials 644-650 (September 2014): 4452–54. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4452.

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Simple cells Gabor wavelet transform and human visual system in the visual stimulus response very similar. It has the good characteristics of the local space in the extraction of target and frequency domain information. Although the Gabor wavelet does not of itself constitute orthogonal basis, but in the specific parameters can form a tight frame. Gabor wavelet is sensitive to the image edge, can provide good direction and scale selection characteristics, but also insensitive to illumination changes, can provide the illumination change good adaptability. These features make Gabor wavelet is widely used in visual information understanding. The two-dimensional Gabor wavelet transform is an important tool for signal analysis and processing in frequency domain in, the coefficient of wavelet transform with the visual characteristics and good biology background, so it is widely used in image processing, pattern recognition and other fields.

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Paul, Okuwobi Idowu, and Yong Hua Lu. "Facial Prediction and Recognition Using Wavelets Transform Algorithm and Technique." Applied Mechanics and Materials 666 (October 2014): 251–55. http://dx.doi.org/10.4028/www.scientific.net/amm.666.251.

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An efficient facial representation is a crucial step for successful and effective performance of cognitive tasks such as object recognition, fixation, facial recognition system, etc. This paper demonstrates the use of Gabor wavelets transform for efficient facial representation and recognition. Facial recognition is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Gabor wavelet transform is used for facial features vector construction due to its powerful representation of the behavior of receptive fields in human visual system (HVS). The method is based on selecting peaks (high-energized points) of the Gabor wavelet responses as feature points. This paper work introduces the use of Gabor wavelets transform for efficient facial representation and recognition. Compare to predefined graph nodes of elastic graph matching, the approach used in this paper has better representative capability for Gabor wavelets transform. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. Based on the experiment, the proposed method performs better compared to the graph matching and eigenface based methods. The feature points are automatically extracted using the local characteristics of each individual face in order to decrease the effect of occluded features. The proposed system is validated using four different face databases of ORL, FERRET, Purdue and Stirling database.

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Xu, Yajun, Fengmei Liang, Gang Zhang, and Huifang Xu. "Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7074–79. http://dx.doi.org/10.1166/jctn.2016.5673.

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This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

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Devi, Vaneeta, and M. L. Sharma. "Spectral Estimation of Noisy Seismogram using Time-Frequency Analyses." International Journal of Geotechnical Earthquake Engineering 7, no. 1 (January 2016): 19–32. http://dx.doi.org/10.4018/ijgee.2016010102.

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Time–Frequency analyses have the advantage of explaining the signal features in both time domain and frequency domain. This paper explores the performance of Time–Frequency analyses on noisy seismograms acquired from seismically active region in NW Himalayan. The Short Term Fourier Transform, Gabor Transform, Wavelet Transform and Wigner-Ville Distribution have been used in the present study to carry out Time-Frequency analyses. Parametric study has been carried out by varying basic parameters viz. sampling, window size and types. Wavelet analysis (Continuous Wavelet Transform) has been studied with different type of wavelets. The seismograms have been stacked in time-frequency domain using Gabor Transform and have been converted using Discrete Gabor Expansion techniques. The Spectrograms reveals better spectral estimation in time-frequency domain than Fourier Transform and hence recommended to estimate dominate frequency components, phase marking and timings of phase. The time of occurrence of frequency component corresponding to maximum energy burst can be identified on seismograms

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Wang, Hong Bo, and Guo Cheng Sheng. "Infrared Image Multi-Scale Recognition Based on Gabor Wavelet." Applied Mechanics and Materials 26-28 (June 2010): 1163–67. http://dx.doi.org/10.4028/www.scientific.net/amm.26-28.1163.

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The aim of this paper is to construct an image recongnition system based on Gabor wavelets for infrared image. An enhanced representation of the Gabor wavelets is proposed, in which the properties of Gaussian mask in Gabor wavelets is developed to enhance the envelope function, and simultaneously the parameters of the filter based on Gabor wavelets is designed depengding on the frequency response of the training images. Some experiments including infrared image recognitions are given. The good performances are verified through using the proposed scheme in this paper.

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Li, Zhi-Ming, Zheng-Hai Huang, and Ting Zhang. "Gabor-scale binary pattern for face recognition." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 05 (August 24, 2016): 1650035. http://dx.doi.org/10.1142/s0219691316500351.

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In this paper, a novel face descriptor, the Gabor-scale binary pattern (GSBP), is proposed to explore the neighboring relationship in spatial, frequency and orientation domains for the purpose of face recognition. In order to extract the GSBP feature, the Gabor-scale volume and the Gabor-scale vector are introduced by using a group of Gabor wavelet coefficients with a special orientation. Moreover, the Gabor-scale length pattern and the Gabor-scale ratio pattern are proposed. Compared with the existed methods, GSBP utilizes the deep relations between neighboring Gabor subimages instead of directly combining Gabor wavelet transform and local binary pattern. For estimating the performance of GSBP, we compare the proposed method with the related methods on several popular face databases, including LFW, FERET, AR, Yale and Extended YaleB databases. The experimental results show that the proposed method outperforms several popular face recognition methods.

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Shirasuna, Miyori, Zhong Zhang, Hiroshi Toda, and Tetsuo Miyake. "Design of Approximate Tight Wavelet Frames Using Gabor Wavelet." Journal of Signal Processing 20, no. 1 (2016): 41–53. http://dx.doi.org/10.2299/jsp.20.41.

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Nazarkevych, Mariia, Natalia Kryvinska, and Yaroslav Voznyi. "Applying Ateb–Gabor Filters to Biometric Imaging Problems." Symmetry 13, no. 4 (April 19, 2021): 717. http://dx.doi.org/10.3390/sym13040717.

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This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

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Willmore, Ben, Ryan J. Prenger, Michael C. K. Wu, and Jack L. Gallant. "The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform." Neural Computation 20, no. 6 (June 2008): 1537–64. http://dx.doi.org/10.1162/neco.2007.05-07-513.

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We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.

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Kaur, Amandeep, and Savita Gupta. "Texture Classification based on Gabor Wavelet." International Journal of Research in Computer Science 2, no. 4 (July 5, 2012): 39–44. http://dx.doi.org/10.7815/ijorcs.24.2012.038.

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Kumbhar, Mahesh, Manasi Patil, and Ashish Jadhav. "Facial Expression Recognition using Gabor Wavelet." International Journal of Computer Applications 68, no. 23 (April 18, 2013): 13–17. http://dx.doi.org/10.5120/11718-7290.

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Hoffman, D. K., G. W. Wei, D. S. Zhang, and D. J. Kouri. "Shannon–Gabor wavelet distributed approximating functional." Chemical Physics Letters 287, no. 1-2 (April 1998): 119–24. http://dx.doi.org/10.1016/s0009-2614(98)00130-4.

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Han, Deguang. "Approximations for Gabor and wavelet frames." Transactions of the American Mathematical Society 355, no. 8 (April 24, 2003): 3329–42. http://dx.doi.org/10.1090/s0002-9947-03-03047-2.

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Xie, Xudong, Wentao Liu, and Kin-Man Lam. "Pseudo-Gabor wavelet for face recognition." Journal of Electronic Imaging 22, no. 2 (June 21, 2013): 023029. http://dx.doi.org/10.1117/1.jei.22.2.023029.

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Casasent, David P. "Wavelet and Gabor transforms for detection." Optical Engineering 31, no. 9 (1992): 1893. http://dx.doi.org/10.1117/12.59913.

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Carrizo, Ivana, and Sergio Favier. "Perturbation of wavelet and Gabor frames." Analysis in Theory and Applications 19, no. 3 (September 2003): 238–54. http://dx.doi.org/10.1007/bf02835283.

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ABDEL KAREEM, WALEED, TAMER NABIL, SEIICHERIO IZAWA, and YU FUKUNISHI. "MULTIRESOLUTION AND NONLINEAR DIFFUSION FILTERING OF HOMOGENEOUS ISOTROPIC TURBULENCE." International Journal of Computational Methods 11, no. 01 (September 2, 2013): 1350054. http://dx.doi.org/10.1142/s0219876213500540.

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The multiresolution (Gabor and wavelet transforms) and nonlinear diffusion filtering (NDF) methods are investigated to extract the coherent and incoherent parts of a forced homogeneous isotropic turbulent field. The aim of this paper is to apply two different analyses to decompose the turbulent field into organized coherent and random incoherent parts. The first analysis filtering process (Gabor and wavelet transforms) is based on the frequency domain; however the second NDF filtering analysis is implemented in the spatial domain. The turbulent field is generated using the Lattice Boltzmann method (LBM) with a resolution of 1283, and the Q-identification method is used to extract the elongated vortical structures. The three filtering methods are applied against the scalar Q-field rather than a vector field (velocity or vorticity fields). The Gabor transform and the orthogonal wavelet with approximately symmetric basis are applied to filter out incoherent noise. Filtering in the Gabor domain is done in the highest quarter frequency values, whereas filtering in the wavelet domain is done using sub-band dependent thresholding. The NDF method is based on explicit finite-difference discretization in the spatial domain. Results indicate that the three filtering methods smoothly identify the coherent and incoherent parts. Although the NDF method isolates the incoherent part more smoothly, the cross sections of the vortices in the coherent part are changed. Also, the Gabor filtering method can remove few incoherent points from the flow field, compared with the other two methods. The wavelet method tends to identify the coherent vortices and remove the incoherent noise without any change in the physical structure of the turbulent field.

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Ma, Wenying. "Single Sample Discriminant Analysis Based on Gabor Transform." Traitement du Signal 38, no. 3 (June 30, 2021): 829–35. http://dx.doi.org/10.18280/ts.380329.

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To solve the small sample problem of biometric identification, this paper investigates the limiting case of the problem, i.e., the recognition of a single training sample, and proposes a single sample discriminant analysis method based on Gabor wavelet and KPCA-RBF (KPRC) classifier (kernel principal component analysis-radial basis function). The proposed method performs pixel-level fusion of face and palmprint images. Firstly, a face image and a palmprint image were subject to two-dimensional (2D) Gabor wavelet transform. The resulting Gabor face image and Gabor palmprint image were fused on the pixel level into a new fused image. Next, a new classifier called KPCA-RBF was designed to extract nonlinear discriminative features by KPCA, and classify objects with RBF. Based on AR database, FERET database, and palmprint database, the single sample discriminant analysis method was realized based on Gabor transform and KPCA-RBF classifier. Experimental results show that multimodal recognition methods clearly outshine single-modal recognition methods, and the GABOR-KPRC with pixel-level fusion achieves better recognition effect than other fusion methods. It was also demonstrated that Gabor transform and KPRC classifier can effectively improve the fusion effect, whether for pixel-level fusion or decision-level fusion.

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Qin, Shu, Zhengzhou Zhu, Yuhang Zou, and Xiaowei Wang. "Facial expression recognition based on Gabor wavelet transform and 2-channel CNN." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 02 (November 29, 2019): 2050003. http://dx.doi.org/10.1142/s0219691320500034.

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Facial expression recognition is one of the hotspots in the fields of computer vision and deep learning. It has very important applications in the domains of learning service recommendation, human–computer interaction and medical industry. Aiming at the problem that the traditional expression recognition method is not accurate, this paper proposes a method combining Gabor wavelet transform and convolutional neural network. Firstly, face positioning, cropping, histogram equalization and other preprocessing are performed on the expression image. Then we extract key frames of expression sequences. After that the Gabor wavelet transform is performed on the expression image to obtain magnitude and phase characteristics. Finally, we design a 2-channel CNN for training and classification. The experiment achieves an accuracy of 96.81% on the CK+ database and it has a certain improvement compared with the Gabor wavelet transform and the traditional CNN alone.

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Gong, Wei Yu, and Fang Xia Lu. "Research on Locality Preserving Discriminant Projection Algorithm Based on Gabor for Face Expression Recognition." Applied Mechanics and Materials 721 (December 2014): 766–70. http://dx.doi.org/10.4028/www.scientific.net/amm.721.766.

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For the problem of features extraction and dimensionality reduction of expression recognition, the paper proposes Gabor Locality Preserving Discriminant Projection (GLPDP) algorithm, which is based on Gabor Wavelet. Firstly, we use Gabor wavelet transform to have an expression feature extraction. Secondly, we improved the locality preserving projection (LPP) algorithm, introducing scatter difference in the LPP objective function to increase divergence constraints among the sample classes and extracts more discriminated features while having the dimensionality reduction. Finally, we use the nearest neighbor classifier to have a classification for expression category. The effectiveness of the proposed methods is validated through the experimental results on JAFFE and Cohn-Kanade Facial expression databases.

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Wang, Hai Feng, Kun Zhang, and Hong E. Ren. "A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm." Advanced Materials Research 811 (September 2013): 430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.811.430.

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In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

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Shen, Yong Jun, Guang Ming Zhang, Shao Pu Yang, and Hai Jun Xing. "Two De-Noising Methods Based on Gabor Transform." Advanced Engineering Forum 2-3 (December 2011): 176–81. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.176.

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Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

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ZHAN, YONGZHAO, JINGFU YE, DEJIAO NIU, and PENG CAO. "FACIAL EXPRESSION RECOGNITION BASED ON GABOR WAVELET TRANSFORMATION AND ELASTIC TEMPLATES MATCHING." International Journal of Image and Graphics 06, no. 01 (January 2006): 125–38. http://dx.doi.org/10.1142/s0219467806002112.

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Facial expression recognition technology plays an important role in research areas such as psychological studies, image understanding and virtual reality etc. In order to achieve subject-independent facial expression recognition and obtain robustness against illumination variety and image deformation, facial expression recognition methods based on Gabor wavelet transformation and elastic templates matching are presented in this paper. First given a still image containing facial expression information, preprocessors are executed which include gray and scale normalization. Secondly, Gabor wavelet filters are adopted to extract expression features. Then the elastic graph for expression features is constructed. Finally, elastic templates matching algorithm and K-nearest neighbors classifier are used to recognize facial expression. Experiments show that expression features can be extracted effectively by Gabor wavelet transformation, which is insensitive to illumination variety and individual difference, and high recognition rate can be obtained using elastic templates matching algorithm, which is subject-independent.

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MU, MEIRU, and QIUQI RUAN. "MEAN AND STANDARD DEVIATION AS FEATURES FOR PALMPRINT RECOGNITION BASED ON GABOR FILTERS." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (June 2011): 491–512. http://dx.doi.org/10.1142/s0218001411008750.

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The two-dimensional (2D) Gabor function has been recognized as a very useful tool in feature extraction of image, due to its optimal localization properties in both spatial and frequency domain. This paper presents a novel palmprint feature extraction method based on the statistics of decomposition coefficients of the Gabor wavelet transform. It is experimentally found that the magnitude coefficients of the Gabor wavelet transform within each subband uniformly to approximate the Lognormal distribution. Based on this fact, we create the palmprint representation using two simple statistics (mean and standard deviation) as feature components after applying the logarithmic transformation of Gabor filtered magnitude coefficients for each subband with different orientations and scales. The optimum setting of the number of Gabor filters and orientation of each Gabor filter is experimentally determined. For palmprint recognition, the popularly used Fisher Linear Discriminant (FLD) analysis is further applied on the constructed feature vectors to extract discriminative features and reduce dimensionality. All experiments are both executed over the CCD-based HongKong PolyU Palmprint Database of 7752 images and the scanner-based BJTU_PalmprintDB (V1.0) of 3460 images. The results demonstrate the effectiveness of the proposed palmprint representation in achieving the improved recognition performance.

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Kar, Arindam, Debotosh Bhattacharjee, Dipak Kumar Basu, Mita Nasipuri, and Mahantapas Kundu. "Human Face Recognition using Gabor Based Kernel Entropy Component Analysis." International Journal of Computer Vision and Image Processing 2, no. 3 (July 2012): 1–20. http://dx.doi.org/10.4018/ijcvip.2012070101.

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In this paper, the authors present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance. Firstly, from the Gabor wavelet transformed images the most important discriminative desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes were derived. After that KECA, relating to the Renyi entropy is extended to include cosine kernel function. The KECA with the cosine kernels is then applied on the extracted most important discriminating feature vectors of facial images to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having positive entropy contribution. Finally, these real KECA features are used for image classification using the L1, L2 distance measures; the Mahalanobis distance measure and the cosine similarity measure. The feasibility of the Gabor based KECA method with the cosine kernel has been successfully tested on both frontal and pose-angled face recognition, using datasets from the ORL, FRAV2D, and the FERET database.

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LI, Ning, and De XU. "2D Log-Gabor Wavelet Based Action Recognition." IEICE Transactions on Information and Systems E92-D, no. 11 (2009): 2275–78. http://dx.doi.org/10.1587/transinf.e92.d.2275.

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Hamid Parvin, Moslem Mohammadi, and Zahra Rezaei. "Face Identification Based on Gabor-Wavelet Features." International Journal of Digital Content Technology and its Applications 6, no. 1 (January 31, 2012): 247–55. http://dx.doi.org/10.4156/jdcta.vol6.issue1.30.

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Mak, K. L., P. Peng, K. F. C. Yiu, and L. K. Li. "Multi-dimensional complex-valued Gabor wavelet networks." Mathematical and Computer Modelling 58, no. 11-12 (December 2013): 1755–68. http://dx.doi.org/10.1016/j.mcm.2013.02.011.

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Dagher, Issam, and Samir Abujamra. "Combined wavelet and Gabor convolution neural networks." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (November 2019): 1950046. http://dx.doi.org/10.1142/s0219691319500462.

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Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques.

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Zhang, H., B. Zhang, W. Huang, and Q. Tian. "Gabor Wavelet Associative Memory for Face Recognition." IEEE Transactions on Neural Networks 16, no. 1 (January 2005): 275–78. http://dx.doi.org/10.1109/tnn.2004.841811.

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M.R, Jisha. "Multichannel Image Registration using Gabor Wavelet Transform." IOSR Journal of Electronics and Communication Engineering 8, no. 3 (2013): 31–36. http://dx.doi.org/10.9790/2834-0833136.

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Tian, Yu, and YunZhang Li. "Subspace dual super wavelet and Gabor frames." Science China Mathematics 60, no. 12 (August 10, 2017): 2429–46. http://dx.doi.org/10.1007/s11425-016-9091-4.

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Shi, Yan, Long Wu Wang, Hui Ying Lan, and Guang Zeng. "Extraction of the Palm Vein Texture Features Based on Gabor Wavelet Transforms." Applied Mechanics and Materials 511-512 (February 2014): 429–32. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.429.

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A texture feature extraction method based on Gabor wavelet transform is presented to avoid the limitations of the shape structure characteristics extraction in the palm vein image. First acquire the palm vein images by independent structures of vein acquire device then extracts ROI area of vein images by determining the centerline of the middle finger and ring finger interphalangeal. Correcting the vein image by Gamma operator will improve the contrast of the processing image. Finally, transform the image based on Gabor wavelet to extract texture feature.

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Margrave, Gary F., Michael P. Lamoureux, and David C. Henley. "Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data." GEOPHYSICS 76, no. 3 (May 2011): W15—W30. http://dx.doi.org/10.1190/1.3560167.

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We have extended the method of stationary spiking deconvolution of seismic data to the context of nonstationary signals in which the nonstationarity is due to attenuation processes. As in the stationary case, we have assumed a statistically white reflectivity and a minimum-phase source and attenuation process. This extension is based on a nonstationary convolutional model, which we have developed and related to the stationary convolutional model. To facilitate our method, we have devised a simple numerical approach to calculate the discrete Gabor transform, or complex-valued time-frequency decomposition, of any signal. Although the Fourier transform renders stationary convolution into exact, multiplicative factors, the Gabor transform, or windowed Fourier transform, induces only an approximate factorization of the nonstationary convolutional model. This factorization serves as a guide to develop a smoothing process that, when applied to the Gabor transform of the nonstationary seismic trace, estimates the magnitude of the time-frequency attenuation function and the source wavelet. By assuming that both are minimum-phase processes, their phases can be determined. Gabor deconvolution is accomplished by spectral division in the time-frequency domain. The complex-valued Gabor transform of the seismic trace is divided by the complex-valued estimates of attenuation and source wavelet to estimate the Gabor transform of the reflectivity. An inverse Gabor transform recovers the time-domain reflectivity. The technique has applications to synthetic data and real data.

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Tay, Nuo Wi, Chu Kiong Loo, and Mitja Perus. "Application of Gabor Wavelet in Quantum Holography for Image Recognition." International Journal of Nanotechnology and Molecular Computation 2, no. 1 (January 2010): 44–61. http://dx.doi.org/10.4018/jnmc.2010010104.

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Gabor wavelet is considered the best mathematical descriptor for receptive fields in the striate cortex. As a basis function, it is suitable to sparsely represent natural scenes due to its property in maximizing information. It is argued that Gabor-like receptive fields emerged by the sparseness-enforcing or infomax method, with sparseness-enforcing being more biologically plausible. This paper incorporates Gabor over-complete representation into Quantum Holography for image recognition tasks. Correlations are performed using sampled result from all frequencies as well as the optimum frequency. Correlation is also performed using only those points of least activity, which shows improvements in recognition. Analysis on the use of conjugation in reconstruction is provided. The authors also suggest improvements through iterative methods for reconstruction.

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Garcés, Milton A. "Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures." Entropy 22, no. 9 (August 26, 2020): 936. http://dx.doi.org/10.3390/e22090936.

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Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

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Li, Qi, Peng Ge, Hua Jun Feng, and Zhi Hai Xu. "Image Displacement Detection under Low Illumination Using Joint Transform Correlator with Wavelet Denoising." Applied Mechanics and Materials 128-129 (October 2011): 602–6. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.602.

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Since joint transform correlator (JTC) cannot directly detect the displacement between reference and target images without adequate exposure, an image displacement detection method using JTC based on log-Gabor wavelet denoising is proposed. The method uses a log-Gabor wavelet transform to denoise the reference and the target image obtained in the condition lack of enough exposure, preserving the phase information of them. Results show that the method can successfully accomplish the motion detection, RMSE of displacement measurement using JTC with wavelet denoising could be within 0.3 pixels under 1/80 of normal exposure. The method improved the detection ability of JTC in the condition of low illumination and low contrast, and has great application prospect under these circumstances.

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Agarwal, Megha, and Rudra Prakash Maheshwari. "Content Based Image Retrieval Based on Log Gabor Wavelet Transform." Advanced Materials Research 403-408 (November 2011): 871–78. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.871.

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This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

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Ahmed, Sulayman, Mondher Frikha, Taha Darwassh Hanawy Hussein, and Javad Rahebi. "Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning." BioMed Research International 2021 (March 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/6621540.

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In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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Li, Li Sai, Zi Lu Ying, and Bin Bin Huang. "Facial Expression Recognition Based on Gabor Texture Features and Centre Binary Pattern." Applied Mechanics and Materials 742 (March 2015): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amm.742.257.

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This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

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Liu, Xiaojie, Lin Shen, and Honghui Fan. "Face recognition algorithm based on Gabor wavelet and locality preserving projections." Modern Physics Letters B 31, no. 19-21 (July 27, 2017): 1740041. http://dx.doi.org/10.1142/s0217984917400413.

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In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

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Abu Nawas, Khairul Amrizal, Mahfuzah Mustafa, Rosdiyana Samad, Dwi Pebrianti, and Nor Rul Hasma Abdullah. "K-NN Classification of Brain Dominance." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2494. http://dx.doi.org/10.11591/ijece.v8i4.pp2494-2502.

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<span>The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one’s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.</span>

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Li, Q. Q., C. S. Zhou, X. Q. Lv, H. Y. Yang, and K. Zhang. "Design Patent Retrieval Based on Gabor Wavelet and LBP." Applied Mechanics and Materials 743 (March 2015): 503–9. http://dx.doi.org/10.4028/www.scientific.net/amm.743.503.

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Due to the diversity and complexity of design patent images, it is difficult to retrieve well if extracting features from images directly. A design patent image retrieval method based on Gabor filter and LBP is proposed in the paper. Firstly, doing low-pass filtering to the normalized images with Gabor filter to amplify the images’ details, then extracting image’s texture feature with LBP algorithm, calculating images’ similarity according to the distance formula after feature vectors’ internal normalization, finally return several similar images. The experimental results show that this retrieval method get better retrieval accuracy and correct rate.

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Wang, Xin, Can Tang, Ji Li, Peng Zhang, and Wei Wang. "Image Target Recognition via Mixed Feature-Based Joint Sparse Representation." Computational Intelligence and Neuroscience 2020 (August 10, 2020): 1–8. http://dx.doi.org/10.1155/2020/8887453.

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An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.

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Chowdhary, Chiranji Lal, Tapan Kumar Das, Vijaykumar Gurani, and Abhishek Ranjan. "An Improved Tumour Identification with Gabor Wavelet Segmentation." Research Journal of Pharmacy and Technology 11, no. 8 (2018): 3451. http://dx.doi.org/10.5958/0974-360x.2018.00637.6.

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Journal articles on the topic 'Wavelet Gabor'

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