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A Geometric Model of Multi-scale Orientation Preference Maps via gabor functions

JOURNAL OF MATHEMATICAL IMAGING AND VISION 2018年第6期60卷 900-912页

作者： Baspinar, Emre Citti, Giovanna Sarti, Alessandro Univ Bologna Dipartimento Matemat Bologna Italy EHESS CNRS CAMS Paris France

In this paper we present a new model for the generation of orientation preference maps in the primary visual cortex (V1), considering both orientation and scale features. First we undertake to model the functional architecture of V1 by interpreting it as a principal fiber bundle over the 2-dimensional retinal plane by introducing intrinsic variables orientation and scale. The intrinsic variables constitute a fiber on each point of the retinal plane and the set of receptive profiles of simple cells is located on the fiber. Each receptive profile on the fiber is mathematically interpreted as a rotated gabor function derived from an uncertainty principle. The visual stimulus is lifted in a 4-dimensional space, characterized by coordinate variables, position, orientation and scale, through a linear filtering of the stimulus with gabor functions. Orientation preference maps are then obtained by mapping the orientation value found from the lifting of a noise stimulus onto the 2-dimensional retinal plane. This corresponds to a Bargmann transform in the reducible representation of the group. A comparison will be provided with a previous model based on the Bargmann transform in the irreducible representation of the group, outlining that the new model is more physiologically motivated. Then, we present simulation results related to the construction of the orientation preference map by using gabor filters with different scales and compare those results to the relevant neurophysiological findings in the literature.

关键词： Orientation maps Neurogeometry Differential geometry gabor functions Bargmann transform

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Sparse Dual Frames and Dual gabor functions of Minimal Time and Frequency Supports

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JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS 2013年第1期19卷 48-76页

作者： Li, Shidong Liu, Yulong Mi, Tiebin Renmin Univ China Sch Informat Beijing 100872 Peoples R China San Francisco State Univ Dept Math San Francisco CA 94132 USA

Exploitation of the optimality of (non-exact) frames from a sparse dual point of view is presented. Sparse dual frames and dual gabor functions of the minimal time and/or frequency supports are studied and constructed through the notion of sparse representations. Conditions on the sparsest dual frames and the dual gabor functions of the minimal time and/or frequency supports are discussed. Algorithms and examples are provided.

关键词： Frames Sparse dual frames gabor functions Sparse dual gabor functions Duality condition l(1)-Minimization

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NLTV-gabor-based models for image decomposition and denoising

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SIGNAL IMAGE AND VIDEO PROCESSING 2020年第2期14卷 305-313页

作者： Liu, Xinwu Chen, Yue Hunan Univ Sci & Technol Sch Math & Computat Sci Xiangtan 411201 Hunan Peoples R China Zhejiang Normal Univ Coll Math & Comp Sci Jinhua 321004 Zhejiang Peoples R China

By using the nonlocal total variation (NLTV) as the regularization and gabor functions as the fidelity, this paper proposes two novel models for image decomposition and denoising. The presented models closely incorporate the advantages of the NLTV and gabor wavelets-based methods. These improvements are aimed at overcoming the drawbacks of staircase artifacts and loss of edge details caused by the traditional variational frameworks. Furthermore, on the basis of Chambolle's projection algorithm, we introduce two extremely efficient numerical methods to solve the resulting optimization problems. Finally, compared with several popular and powerful numerical methods, this article confirms the superiorities of the developed strategies for image decomposition and denoising in terms of visual quality and quantitative assessments.

关键词： Image decomposition Image denoising Nonlocal total variation gabor functions Projection algorithm

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A sub-Riemannian model of the visual cortex with frequency and phase

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JOURNAL OF MATHEMATICAL NEUROSCIENCE 2020年第1期10卷 11页

作者： Baspinar, E. Sarti, A. Citti, G. INRIA Sophia Antipolis MathNeuro Valbonne France EHESS CAMS Paris France Univ Bologna Dept Math Bologna Italy

In this paper, we present a novel model of the primary visual cortex (V1) based on orientation, frequency, and phase selective behavior of V1 simple cells. We start from the first-level mechanisms of visual perception, receptive profiles. The model interprets V1 as a fiber bundle over the two-dimensional retinal plane by introducing orientation, frequency, and phase as intrinsic variables. Each receptive profile on the fiber is mathematically interpreted as rotated, frequency modulated, and phase shifted gabor function. We start from the gabor function and show that it induces in a natural way the model geometry and the associated horizontal connectivity modeling of the neural connectivity patterns in V1. We provide an image enhancement algorithm employing the model framework. The algorithm is capable of exploiting not only orientation but also frequency and phase information existing intrinsically in a two-dimensional input image. We provide the experimental results corresponding to the enhancement algorithm.

关键词： Sub-Riemannian geometry Neurogeometry Differential geometry gabor functions Visual cortex Image enhancement

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Magnetic Tile Surface Defect Detection Based on Texture Feature Clustering

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Journal of Shanghai Jiaotong university(Science) 2019年第5期24卷 663-670页

作者： LI Dan NIU Zhongbin PENG Dongxu College of Electrical Information and Engineering Anhui University of Technology

In the field of magnetic tile surface detection, artificial detection efficiency is low, and the traditional image segmentation algorithm cannot show good performance when the gray scale of the magnetic tile itself is small, or the image is affected by uneven illumination. In view of these questions, this paper puts forward a new clustering segmentation algorithm based on texture feature. This algorithm uses gabor function spectra to represent magnetic tile surface texture and then uses a user-defined local product coefficient to modify gabor energy spectra to get the center number of fuzzy C-means(FCM) clustering. Moreover, the user-defined gabor energy spectra image is segmented by clustering algorithm. Finally, it extracts the magnetic tile surface defects according to the changes of regional gray characteristics. Experiments show that the algorithm effectively overcomes the noise interference and makes a good performance on accuracy and robustness, which can effectively detect crack,damage, pit and other defects on the magnetic tile surface.

关键词： defect detection of magnetic tile gabor functions local characteristics of gray scale fuzzy C-means(FCM) clustering

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A New TGV-gabor Model for Cartoon-Texture Image Decomposition

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IEEE SIGNAL PROCESSING LETTERS 2018年第8期25卷 1221-1225页

作者： Liu, Xinwu Hunan Univ Sci & Technol Sch Math & Computat Sci Xiangtan 411201 Peoples R China

Integrating the advantages of two recently developed total generalized variation (TGV) and gabor wavelets, this letter presents a new weighted TGV-gabor model for the challenging problem of cartoon-texture image decomposition. Computationally, by introducing two dual variables, we formulate a highly efficient numerical method based on the primal-dual framework in detail. At last, in comparison with several existing advanced variational models, experimental simulations clearly illustrate the outstanding performance of our proposed edge-preserving model, especially in separating the larger structural features from the smaller textural details completely and maintaining the sharp edges and weak contours simultaneously.

关键词： gabor functions Hilbert space image decomposition primal-dual method total generalized variation (TGV)

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RETRACTED: A novel real-time patient-specific seizure diagnosis algorithm based on analysis of EEG and ECG signals using spectral and spatial features and improved particle swarm optimization classifier (Retracted article. See vol. 43, pg. 229, 2013)

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COMPUTERS IN BIOLOGY AND MEDICINE 2012年第8期42卷 848-856页

作者： Nasehi, Saadat Pourghassem, Hossein Islamic Azad Univ Dept Elect Engn Najafabad Branch Esfahan Iran

This paper proposes a novel real-time patient-specific seizure diagnosis algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onset. In this algorithm, spectral and spatial features are selected from seizure and non-seizure EEG signals by gabor functions and principal component analysis (PCA). Furthermore, four features based on heart rate acceleration are extracted from ECG signals to form feature vector. Then a neural network classifier based on improved particle swarm optimization (IPSO) learning algorithm is developed to determine an optimal nonlinear decision boundary. This classifier allows to adjust the parameters of the neural network classifier, efficiently. This algorithm can automatically detect the presence of seizures with minimum delay which is an important factor from a clinical viewpoint. The performance of the proposed algorithm is evaluated on a dataset consisting of 154 h records and 633 seizures from 12 patients. The results indicate that the algorithm can recognize the seizures with the smallest latency and higher good detection rate (GDR) than other presented algorithms in the literature. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： gabor functions Seizure detection Epilepsy EEG ECG Improved particle swarm optimization (IPSO) Principal component analysis (PCA)

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AXON EXTRACTION FROM FLUORESCENT CONFOCAL MICROSCOPY IMAGES

AXON EXTRACTION FROM FLUORESCENT CONFOCAL MICROSCOPY IMAGES

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9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Mottini, Alejandro Descombes, Xavier Besse, Florence INRIA I3S IBDC Project Morpheme F-06903 Sophia Antipolis France

ISBN: (纸本)9781457718588

The morphological analysis of axonal trees is an important problem in neuroscience. The first step for such an analysis is the extraction of the axon. Due to the high volume of generated image data and the tortuous nature of the axons, manual processing is not feasible. Therefore, it is necessary to develop techniques for the automatic extraction of the neuronal structures. In this paper we present a new approach for the automatic extraction of axons from fluorescent confocal microscopy images. It combines algorithms for filament enhancement, binarization, skeletonization and gap filling in a pipeline capable of extracting the axons. The performance of the proposed method was evaluated on real images. Results support the potential use of this technique in helping biologists perform automatic extraction of axons from fluorescent confocal microscopy images.

关键词： axon extraction fluorescent confocal microscopy gabor functions tensor voting

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Nonlinearities in stereoscopic phase-differencing

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2008年第9期17卷 1672-1684页

作者： Monaco, James Peter Bovik, Alan Conrad Cormack, Lawrence K. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Univ Texas Austin Dept Psychol Austin TX 78712 USA

Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, these phase-differencing techniques suffer a significant impediment: phase nonlinearities. In regions of phase nonlinearity, the signals under consideration possess properties that invalidate the use of phase for disparity estimation. This paper uses the amenable properties of Gaussian white noise images to analytically quantify these properties. The improved understanding gained from this analysis enables us to better understand current methodologies for detecting regions of phase instability. Most importantly, we introduce a new, more effective means for identifying these regions based on the second derivative of phase.

关键词： disparity estimation gabor functions Gaussian random processes instantaneous frequency local correlation local phase stereopsis

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Feature extraction and face verification using gabor and Gaussian mixture models

Feature extraction and face verification using gabor and Gau...

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6th Mexican International Conference on Artificial Intelligence (MICAI 2007)

作者： Olivares-Mercado, Jesus Sanchez-Perez, Gabriel Nakano-Miyatake, Mariko Perez-Meana, Hector Inst Politecn Nacl ESIME Culhuacan Ava Santa 1000 Mexico City 04430 DF Mexico

ISBN: (纸本)9783540766308

This paper proposes a faces verification in which the feature extraction is carried out using the discrete gabor function (DGF), while the Gaussian Mixture Model (GMM) is used in the face verification stage. Evaluation results using standard data bases with different parameters, such as the number of mixtures and the number of face used for training show that proposed system provides better results that other proposed systems with a correct verification rate larger than 95%. Although, as happens in must face recognition systems, the verification rate decreases when the target faces present some rotation degrees.

关键词： Face verification gabor functions Gaussian mixture model

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