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Academic literature on the topic 'Wavelet Gabor'

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Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Wavelet Gabor"

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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Dissertations / Theses on the topic "Wavelet Gabor"

Klemo, Elios. "SYMLET AND GABOR WAVELET PREDICTION OF PRINT DEFECTS." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_theses/263.

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Recent studies have been done to create models that predict the response of the human visual system (HVS) based on how the HVS processes an image. The most widely known of these models is the Gabor model, since the Gabor patterns closely resemble the receptive filters in the human eye. The work of this thesis examines the use of Symlets to represent the HVS, since Symlets provide the benefit of orthogonality. One major problem with Symlets is that the energy is not stable in respective Symlet channels when the image patterns are translated spatially. This thesis addresses this problem by up sampling Symlets instead of down sampling, and thus creating shift invariant Symlets. This thesis then compares the representation of Gabor versus Symlet approach in predicting the response of the HVS to detecting print defect patterns such as banding and graining. In summary we noticed that Symlet prediction outperforms the Gabor prediction thus Symlets would be a good choice for HVS response prediction. We also concluded that for banding defect periodicity and size are important factors that affect the response of the HVS to the patterns. For graining defects we noticed that size does not greatly affect the response of the HVS to the defect patterns. We introduced our results using two set of performance metrics, the mean and median.

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Bishop, Shannon Renee Smith. "Gabor and wavelet analysis with applications to Schatten class integral operators." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33976.

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This thesis addresses four topics in the area of applied harmonic analysis. First, we show that the affine densities of separable wavelet frames affect the frame properties. In particular, we describe a new relationship between the affine densities, frame bounds and weighted admissibility constants of the mother wavelets of pairs of separable wavelet frames. This result is also extended to wavelet frame sequences. Second, we consider affine pseudodifferential operators, generalizations of pseudodifferential operators that model wideband wireless communication channels. We find two classes of Banach spaces, characterized by wavelet and ridgelet transforms, so that inclusion of the kernel and symbol in appropriate spaces ensures the operator is Schatten p-class. Third, we examine the Schatten class properties of pseudodifferential operators. Using Gabor frame techniques, we show that if the kernel of a pseudodifferential operator lies in a particular mixed modulation space, then the operator is Schatten p-class. This result improves existing theorems and is sharp in the sense that larger mixed modulation spaces yield operators that are not Schatten class. The implications of this result for the Kohn-Nirenberg symbol of a pseudodifferential operator are also described. Lastly, Fourier integral operators are analyzed with Gabor frame techniques. We show that, given a certain smoothness in the phase function of a Fourier integral operator, the inclusion of the symbol in appropriate mixed modulation spaces is sufficient to guarantee that the operator is Schatten p-class.

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Grip, Niklas. "Wavelet and gabor frames and bases : approximation, sampling and applications." Doctoral thesis, Luleå, 2002. http://epubl.luth.se/1402-1544/2002/49.

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Peng, Pai. "Automated defect detection for textile fabrics using Gabor wavelet networks." View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B38025966.

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Peng, Pai, and 彭湃. "Automated defect detection for textile fabrics using Gabor wavelet networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38766103.

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Subramaniam, Kumanan. "Vision based motion tracking and collision avoidance system for vehicle navigation." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246656.

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Soares, João Vitor Baldini. "Segmentação de vasos sangüíneos em imagens de retina usando wavelets e classificadores estatísticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-24072007-174800/.

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Esta dissertação apresenta o desenvolvimento e avaliação de um método para a segmentação de vasos sangüíneos em imagens de retina, em que se usa a transformada wavelet contínua bidimensional combinada com classificação supervisionada. A segmentação dos vasos é a etapa inicial para a análise automática das imagens, cujo objetivo é auxiliar a comunidade médica na detecção de doenças. Entre outras doenças, as imagens podem revelar sinais da retinopatia diabética, uma das principais causas de cegueira em adultos, que pode ser prevenida se detectada em um diagnóstico precoce. A abordagem apresentada consiste na geração de segmentações pela classificação supervisionada de pixels nas classes \"vaso\" e \"não vaso\". As características usadas para classificação são obtidas através da transformada wavelet contínua bidimensional usando a wavelet de Gabor. Resultados são avaliados nos bancos públicos DRIVE e STARE de imagens coloridas através da análise ROC (\"receiver operating characteristic\", ou característica de operação do receptor). O método atinge áreas sob curvas ROC de 0.9614 e 0.9671 nos bancos DRIVE e STARE, respectivamente, ligeiramente superiores àquelas apresentadas por outros métodos do estado da arte. Apesar de bons resultados ROC, a análise visual revela algumas dificuldades do método, como falsos positivos ao redor do disco óptico e de patologias. A wavelet de Gabor mostra-se eficiente na detecção dos vasos, superando outros filtros lineares. Bons resultados e uma classificação rápida são obtidos usando o classificador bayesiano em que as funções de densidade de probabilidade condicionais às classes são descritas por misturas de gaussianas. A implementação do método está disponível na forma de \"scripts\" código aberto em MATLAB para pesquisadores interessados em detalhes de implementação, avaliação ou desenvolvimento de métodos.
This dissertation presents the development and evaluation of a method for blood vessel segmentation in retinal images which combines the use of the two-dimensional continuous wavelet transform with supervised classification. Segmentation of the retinal vasculature is the first step towards automatic analysis of the images, aiming at helping the medical community in detecting diseases. Among other diseases, the images may reveal signs of diabetic retinopathy, a leading cause of adult blindness, which can be prevented if identified early enough. The presented approach produces segmentations by supervised classification of each image pixel as \"vessel\" or \"nonvessel\", with pixel features derived using the two-dimensional continuous Gabor wavelet transform. Results are evaluated on publicly available DRIVE and STARE color image databases using ROC (receiver operating characteristic) analysis. The method achieves areas under ROC curves of 0.9614 and 0.9671 on the DRIVE and STARE databases, respectively, being slightly superior than that presented by state-of-the-art approaches. Though good ROC results are presented, visual inspection shows some typical difficulties of the method, such as false positives on the borders of the optic disc and pathologies. The Gabor wavelet shows itself efficient for vessel enhancement, outperforming other linear filters. Good segmentation results and a fast classification phase are obtained using the Bayesian classifier with class-conditional probability density functions described as Gaussian mixtures. The method\'s implementation is available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods.

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Hammarqvist, Ulf. "Audio editing in the time-frequency domain using the Gabor Wavelet Transform." Thesis, Uppsala universitet, Centrum för bildanalys, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-153634.

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Visualization, processing and editing of audio, directly on a time-frequency surface, is the scope of this thesis. More precisely the scalogram produced by a Gabor Wavelet transform is used, which is a powerful alternative to traditional techinques where the wave form is the main visual aid and editting is performed by parametric filters. Reconstruction properties, scalogram design and enhancements as well audio manipulation algorithms are investigated for this audio representation.The scalogram is designed to allow a flexible choice of time-frequency ratio, while maintaining high quality reconstruction. For this mean, the Loglet is used, which is observed to be the most suitable filter choice. Re-assignmentare tested, and a novel weighting function using partial derivatives of phase is proposed. An audio interpolation procedure is developed and shown to perform well in listening tests.The feasibility to use the transform coefficients directly for various purposes is investigated. It is concluded that Pitch shifts are hard to describe in the framework while noise thresh holding works well. A downsampling scheme is suggested that saves on operations and memory consumption as well as it speeds up real world implementations significantly. Finally, a Scalogram 'compression' procedure is developed, allowing the caching of an approximate scalogram.

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Porter, Robert Mark Stefan. "Texture classification and segmentation." Thesis, University of Bristol, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389032.

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Dahale, Radhika. "Optoelectronic Multifractal Wavelet Analysis for Fast and Accurate Detection of Rainfall in Weather Radar Images." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/97.

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In this thesis we propose an automated process for the removal of non-precipitation echoes present in weather radar signals and accurate detection of rainfall. The process employs multifractal analysis using directional Gabor wavelets for accurate detection of the rain events. An optoelectronic joint transform correlator is proposed to provide ultra fast processing and wavelet analysis. Computer simulations of the proposed system show that the proposed algorithm is successful in the detecting rainfall accurately in radar images. The accuracy of the algorithms proposed are compared to accurate results that were generated under expert supervision. Results of the proposed system are also compared to results of QC algorithm for the ground validation software (GVS) used by TRMM ground validity Project and a previous QC algorithm. Several statistical measures computed for different reflectivity ranges show that the proposed algorithm gives accuracy as high as 98.95%, which exceed the 97.46% maximum accuracy for the GVS results. Also, the minimum error rate obtained by the proposed algorithm for different dB ranges decreases to 1.09% whereas the GVS results show a minimum error rate of 1.80%. The rain rate accumulation confirms the success of the proposed algorithm in the accurate removal of nonprecipitation echoes and a higher precision in rain accumulation estimates.

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Books on the topic "Wavelet Gabor"

Wen-Liang, Hwang, and Torrésani Bruno, eds. Practical time-frequency analysis: Gabor and wavelet transforms with an implementation in S. San Diego: Academic Press, 1998.

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Song, Goh Say, Ron Amos, and Shen Zuowei, eds. Gabor and wavelet frames. New Jersey: World Scientific, 2007.

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Goh, Say Song, Amos Ron, and Zuowei Shen. Gabor and Wavelet Frames. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/6541.

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(Editor), Say Song Goh, Amos Ron (Editor), and Zuowei Shen (Editor), eds. Gabor And Wavelet Frames (Lecture Notes, Institute for Mathematical Sciences, National University of Singapore) (Lecture Notes, Institute for Mathematical Sciences, National University of Singapore). World Scientific Publishing Company, 2007.

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Practical Time-Frequency Analysis - Gabor and Wavelet Transforms with an Implementation in S. Elsevier, 1998. http://dx.doi.org/10.1016/s1874-608x(98)x8024-6.

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Book chapters on the topic "Wavelet Gabor"

Heil, Christopher, and David Walnut. "Gabor and Wavelet Expansions." In Recent Advances in Fourier Analysis and Its Applications, 441–54. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0665-5_25.

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Teolis, Anthony. "Continuous Wavelet and Gabor Transforms." In Modern Birkhäuser Classics, 59–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65747-9_4.

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Teolis, Anthony. "Continuous Wavelet and Gabor Transforms." In Computational Signal Processing with Wavelets, 59–88. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-4142-3_4.

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Krüger, Volker, and Gerald Sommer. "Gabor Wavelet Networks for Object Representation." In Multi-Image Analysis, 115–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45134-x_9.

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Krüger, Volker, and Gerald Sommer. "Gabor Wavelet Networks for Object Representation." In Informatik aktuell, 309–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59802-9_39.

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Antoine, Jean-Pierre, and Fabio Bagarello. "Localization Properties and Wavelet-Like Orthonormal Bases for the Lowest Landau Level." In Advances in Gabor Analysis, 223–58. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0133-5_10.

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Yu, Weichuan, Gerald Sommer, and Kostas Daniilidis. "Skewness of Gabor Wavelets and Source Signal Separation." In Wavelet Analysis and Its Applications, 269–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45333-4_34.

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Debnath, Lokenath. "The Gabor Transform and Time-Frequency Signal Analysis." In Wavelet Transforms and Their Applications, 257–306. Boston, MA: Birkhäuser Boston, 2002. http://dx.doi.org/10.1007/978-1-4612-0097-0_4.

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Debnath, Lokenath, and Firdous Ahmad Shah. "The Gabor Transform and Time–Frequency Signal Analysis." In Wavelet Transforms and Their Applications, 243–86. Boston, MA: Birkhäuser Boston, 2014. http://dx.doi.org/10.1007/978-0-8176-8418-1_4.

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Jeon, In Ja, Mi Young Nam, and Phill Kyu Rhee. "Adaptive Gabor Wavelet for Efficient Object Recognition." In Lecture Notes in Computer Science, 308–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_41.

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Conference papers on the topic "Wavelet Gabor"

Tanaka, Masaru, Takashi Watanabe, and Taketoshi Mishima. "Q-Gabor wavelet." In International Symposium on Optical Science and Technology, edited by Longin J. Latecki, David M. Mount, Angela Y. Wu, and Robert A. Melter. SPIE, 2001. http://dx.doi.org/10.1117/12.447272.

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Shirasuna, Miyori, Zhong Zhang, Hiroshi Toda, and Tetsuo Miyake. "Approximate tight wavelet frame using Gabor wavelet." In 2015 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2015. http://dx.doi.org/10.1109/icwapr.2015.7295934.

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Quach, Kha Gia, Chi Nhan Duong, Khoa Luu, and Hoai Bac Le. "Gabor Wavelet-Based Appearance Models." In Communication Technologies, Research, Innovation, and Vision for the Future (RIVF). IEEE, 2012. http://dx.doi.org/10.1109/rivf.2012.6169865.

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Cai-Xia Deng, Zuo-Xian Fu, and Xiao-Jian Ma. "The properties of Gabor wavelet transform." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4421688.

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Fujita, Keiko. "Gabor transformation on the circle." In 2014 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2014. http://dx.doi.org/10.1109/icwapr.2014.6961302.

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Huang, Lin, and Wenjing Zhang. "Face Recognition Using Multiscale Gabor Wavelet." In 2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/pacrim.2007.4313229.

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Caliskan, A., and B. Ergen. "Palmprint recognition using Gabor wavelet transform." In 2013 21st Signal Processing and Communications Applications Conference (SIU). IEEE, 2013. http://dx.doi.org/10.1109/siu.2013.6531378.

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Ghanem, Taraggy M., Mohamed N. Moustafa, and Hussein I. Shahein. "Gabor wavelet based automatic coin classsification." In 2009 16th IEEE International Conference on Image Processing ICIP 2009. IEEE, 2009. http://dx.doi.org/10.1109/icip.2009.5414515.

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Tsao, Thomas R., and Victor C. Chen. "Gabor wavelet pyramid for depth recovery." In SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, edited by Harold H. Szu. SPIE, 1994. http://dx.doi.org/10.1117/12.170020.

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Kumar, B. Vinay, and D. R. Sai Sharan. "Pattern Recognition with Localized Gabor Wavelet Grids." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.330.

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