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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 (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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AMIN, M. ASHRAFUL, and HONG YAN. "AN EMPIRICAL STUDY ON THE CHARACTERISTICS OF GABOR REPRESENTATIONS FOR FACE RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (2009): 401–31. http://dx.doi.org/10.1142/s0218001409007181.
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This paper examines the classification capability of different Gabor representations for human face recognition. Usually, Gabor filter responses for eight orientations and five scales for each orientation are calculated and all 40 basic feature vectors are concatenated to assemble the Gabor feature vector. This work explores 70 different Gabor feature vector extraction techniques for face recognition. The main goal is to determine the characteristics of the 40 basic Gabor feature vectors and to devise a faster Gabor feature extraction method. Among all the 40 basic Gabor feature representations the filter responses acquired from the largest scale at smallest relative orientation change (with respect to face) shows the highest discriminating ability for face recognition while classification is performed using three classification methods: probabilistic neural networks (PNN), support vector machines (SVM) and decision trees (DT). A 40 times faster summation based Gabor representation shows about 98% recognition rate while classification is performed using SVM. In this representation all 40 basic Gabor feature vectors are summed to form the summation based Gabor feature vector. In the experiment, a sixth order data tensor containing the basic Gabor feature vectors is constructed, for all the operations.
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Yu, Qing Wen, Hong Wang, Hai Bin Zhao, Chong Liu, and Shi Yu Yan. "A Face Detection Method Based on Log-Gabor Filters." Advanced Materials Research 706-708 (June 2013): 1882–85. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1882.
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This paper introduces an effective face detection method based on log-Gabor filters. First, Information contained in the log-Gabor transformations is analyzed. Next, 5-scale and 8-orientation log-Gabor filters are constructed to extract feature vectors. Then, a three-layer feedforward network is created for feature classification. After training the network, we test 80 samples. From the experimental result, log-Gabor filters based method has comparable detection performance with Gabor filters based method. Therefore, log-Gabor filters can encode the images more efficiently.
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Rimiru, Richard M., Judy Gateri, and Micheal W. Kimwele. "GaborNet: investigating the importance of color space, scale and orientation for image classification." PeerJ Computer Science 8 (February 25, 2022): e890. http://dx.doi.org/10.7717/peerj-cs.890.
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Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval.
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Kusban, Muhammad, Aris Budiman, and Bambang Hari Purwoto. "Image enhancement in palmprint recognition: a novel approach for improved biometric authentication." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1299. http://dx.doi.org/10.11591/ijece.v14i2.pp1299-1307.
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Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
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Kusban, Muhammad, Aris Budiman, and Bambang Hari Purwoto. "Image enhancement in palmprint recognition: a novel approach for improved biometric authentication." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 2 (2024): 1299–307. https://doi.org/10.11591/ijece.v14i2.pp1299-1307.
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Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8 × 7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
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Li, Fenlan, and Kexin Xu. "Optimal Gabor Kernel's Scale and orientation selection for face classification." Optics & Laser Technology 39, no. 4 (2007): 852–57. http://dx.doi.org/10.1016/j.optlastec.2006.01.010.
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Li, Jian, Hai Fen Chen, Li Juan Wang, and Cheng Yan Zhang. "Face Recognition Method Based on Multi-Level Histogram Sequence of Gabor Fused Features." Advanced Materials Research 718-720 (July 2013): 2348–52. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2348.
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In this paper, the Gabor fused features are combined with multi-level histogram sequence to extract facial features in order to overcome the disadvantage of traditional Gabor filter bank, whose high-dimensional Gabor features are redundant and the global features representation capacity is poor. First, we get the standard face by face detection, eyes location, geometric normalization and illumination normalization. Second, to extract the multi-orientation information and reduce the dimension of the features, a fusion rule is proposed to fuse the original Gabor features of the same scale into a single feature, and then the fused image will be divided into multi-level changeable units, and the histogram of each unit is computed and combined as facial features. Experimental results on ORL via MATLAB show an encouraging performance for face recognition.
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Loxley, P. N. "The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images." Neural Computation 29, no. 10 (2017): 2769–99. http://dx.doi.org/10.1162/neco_a_00997.
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The two-dimensional Gabor function is adapted to natural image statistics, leading to a tractable probabilistic generative model that can be used to model simple cell receptive field profiles, or generate basis functions for sparse coding applications. Learning is found to be most pronounced in three Gabor function parameters representing the size and spatial frequency of the two-dimensional Gabor function and characterized by a nonuniform probability distribution with heavy tails. All three parameters are found to be strongly correlated, resulting in a basis of multiscale Gabor functions with similar aspect ratios and size-dependent spatial frequencies. A key finding is that the distribution of receptive-field sizes is scale invariant over a wide range of values, so there is no characteristic receptive field size selected by natural image statistics. The Gabor function aspect ratio is found to be approximately conserved by the learning rules and is therefore not well determined by natural image statistics. This allows for three distinct solutions: a basis of Gabor functions with sharp orientation resolution at the expense of spatial-frequency resolution, a basis of Gabor functions with sharp spatial-frequency resolution at the expense of orientation resolution, or a basis with unit aspect ratio. Arbitrary mixtures of all three cases are also possible. Two parameters controlling the shape of the marginal distributions in a probabilistic generative model fully account for all three solutions. The best-performing probabilistic generative model for sparse coding applications is found to be a gaussian copula with Pareto marginal probability density functions.
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Baspinar, Emre, Giovanna Citti, and Alessandro Sarti. "A Geometric Model of Multi-scale Orientation Preference Maps via Gabor Functions." Journal of Mathematical Imaging and Vision 60, no. 6 (2018): 900–912. http://dx.doi.org/10.1007/s10851-018-0803-3.
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Li, Xi, Zhangyong Li, Dewei Yang, Lisha Zhong, Lian Huang, and Jinzhao Lin. "Research on Finger Vein Image Segmentation and Blood Sampling Point Location in Automatic Blood Collection." Sensors 21, no. 1 (2020): 132. http://dx.doi.org/10.3390/s21010132.
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In the fingertip blood automatic sampling process, when the blood sampling point in the fingertip venous area, it will greatly increase the amount of bleeding without being squeezed. In order to accurately locate the blood sampling point in the venous area, we propose a new finger vein image segmentation approach basing on Gabor transform and Gaussian mixed model (GMM). Firstly, Gabor filter parameter can be set adaptively according to the differential excitation of image and we use the local binary pattern (LBP) to fuse the same-scale and multi-orientation Gabor features of the image. Then, finger vein image segmentation is achieved by Gabor-GMM system and optimized by the max flow min cut method which is based on the relative entropy of the foreground and the background. Finally, the blood sampling point can be localized with corner detection. The experimental results show that the proposed approach has significant performance in segmenting finger vein images which the average accuracy of segmentation images reach 91.6%.
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Li Mengwen, 李梦雯, 刘怀愚 Liu Huaiyu, 高向军 Gao Xiangjun та 孟欠欠 Meng Qianqian. "基于多尺度Gabor方向韦伯局部描述子的掌纹识别". Laser & Optoelectronics Progress 58, № 16 (2021): 1610018. http://dx.doi.org/10.3788/lop202158.1610018.
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Yao, Qiong, Chen Chen, Dan Song, Xiang Xu, and Wensheng Li. "A Novel Finger Vein Verification Framework Based on Siamese Network and Gabor Residual Block." Mathematics 11, no. 14 (2023): 3190. http://dx.doi.org/10.3390/math11143190.
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The evolution of deep learning has promoted the performance of finger vein verification systems, but also brings some new issues to be resolved, including high computational burden, massive training sample demand, as well as the adaptability and generalization to various image acquisition equipment, etc. In this paper, we propose a novel and lightweight network architecture for finger vein verification, which was constructed based on a Siamese framework and embedded with a pair of eight-layer tiny ResNets as the backbone branch network. Therefore, it can maintain good verification accuracy under the circumstance of a small-scale training set. Moreover, to further reduce the number of parameters, Gabor orientation filters (GoFs ) were introduced to modulate the conventional convolutional kernels, so that fewer convolutional kernels were required in the subsequent Gabor modulation, and multi-scale and orientation-insensitive kernels can be obtained simultaneously. The proposed Siamese network framework (Siamese Gabor residual network (SGRN)) embeds two parameter-sharing Gabor residual subnetworks (GRNs) for contrastive learning; the inputs are paired image samples (a reference image with a positive/negative image), and the outputs are the probabilities for accepting or rejecting. The subject-independent experiments were performed on two benchmark finger vein datasets, and the experimental results revealed that the proposed SGRN model can enhance inter-class discrepancy and intra-class similarity. Compared with some existing deep network models that have been applied to finger vein verification, our proposed SGRN achieved an ACC of 99.74% and an EER of 0.50% on the FV-USM dataset and an ACC of 99.55% and an EER of 0.52% on the MMCBNU_6000 dataset. In addition, the SGRN has smaller model parameters with only 0.21 ×106 Params and 1.92 ×106 FLOPs, outperforming some state-of-the-art FV verification models; therefore, it better facilitates the application of real-time finger vein verification.
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Prakash, B. V., and A. Rajiv Kannan. "An Efficient Approach to Detect Meningioma Brain Tumor Using Adaptive Neuro Fuzzy Inference System Method." Journal of Medical Imaging and Health Informatics 12, no. 2 (2022): 123–30. http://dx.doi.org/10.1166/jmihi.2022.3931.
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Detection of tumors in brain on time saves the patient life. The brain tumor detection is usually done in Magnetic Resonance Imaging (MRI) of the human brain. An automated model is framed to identify tumor pixels in method for detecting and image. This proposed method contains the following modules as enhancement, transformation, feature extraction, classifications and segmentation. The Oriented Local Histogram Equalization (OLHE) method is applied on the brain MRI images in order to enhance the pixel intensity in boundary regions. This enhanced brain image is transformed to multi orientation image using Gabor transform with respect to various scale and orientation of pixels. Then, set of features (Higher Order Spectra (HOS), Gradient, Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Curvelet) are extracted from this Gabor transformed image and these features are further trained and classified into benign or malignant using Adaptive Neuro Fuzzy Inference (ANFIS) classification approach. Finally, morphological algorithm is used for segmenting the tumor regions in the classified responses. MATLAB R2018 version is used in this paper to simulate the proposed algorithm for brain tumor detection. This proposed system achieves 98.6% of sensitivity, 99.5% of specificity and 99.4% of segmentation accuracy.
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Chen, Li, Qing Zhu, Xiao Xie, Han Hu, and Haowei Zeng. "Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features." ISPRS International Journal of Geo-Information 7, no. 9 (2018): 362. http://dx.doi.org/10.3390/ijgi7090362.
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Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.
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Leonardo, Leonardo. "Penerapan Metode Filter Gabor Untuk Analisis Fitur Tekstur Citra Pada Kain Songket." Jurnal Sistem Komputer dan Informatika (JSON) 1, no. 2 (2020): 120. http://dx.doi.org/10.30865/json.v1i2.1942.
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Songket is a traditional Malay and Minangkabau woven fabric in Indonesia, Malaysia and Brunei. Songket is classified in the brocade woven family. Songket is hand woven with gold and silver threads generally worn at official occasions. The purpose of this study is to analyze the texture of songket fabric by using gabor filters. The results of the study stated that the texture output that appeared was strongly influenced by the magnitude of the frequency parameter values and the orientation of the image degrees. The greater the frequency value given, the results of the test use look brighter and blurry. Therefore the frequency value on the middle scale (f = 0.176) is considered the most appropriate for texture analysis. In addition to the frequency of orientation parameters are also able to show a tendency towards the texture in a certain direction. From the appearance of this fabric texture, the area suspected of having a good texture on the songket cloth.
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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 (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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Ayad, Hayder, Siti Norul Huda Sheikh Abdullah, and Azizi Abdullah. "Visual Object Categorization based on Gabor Filter Generalization Via K-Means Clustering." International Journal of Engineering & Technology 8, no. 1.2 (2019): 168–93. http://dx.doi.org/10.14419/ijet.v8i1.2.24900.
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Content based object recognition systems need informative image properties to obtain good performance results. Filter bank such as Gabor filters is believed to be one of the most popular methods for complete characterization of images by having some important properties such as selectivity to orientation, scale, frequency and smooth parameters. Furthermore, such properties are very effective for compact image description and analysis. However, these functions show a strong dependence on a certain number of different parameter values. Hence, the different filter parameters values used to construct the functions may give different filter responds or properties. Besides, the large number of these filters leads to expensive computation to create maps for feature extraction, thus it is necessary to reduce the number of candidates and identify subset of effective and discriminative filters to avoid overfitting and hinder generalization performance. In this paper, we first compute Gabor filters using a set of different values for filter parameters. After that, the k-means clustering algorithm is used to group these filter responds into k different clusters. Next the k different clusters are used to convolve images and the edge histogram then apply to the filter outputs for image description. After that, we combine all outputs of image descriptors using SVMs. Experiment results on 20 and 101 classes of the Caltect-101 object database show that the method significantly outperforms using the standard Gabor filter approach.Â
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Radouane, Mohamed, Nadia Idrissi Zouggari, Amine Amraoui, and Mounir Amraoui. "Fusion of Gabor filter and steerable pyramid to improve iris recognition system." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1460. http://dx.doi.org/10.11591/ijai.v11.i4.pp1460-1468.
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<span>Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (</span><span>Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.</span>
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Yao, Qiong, Dan Song, Xiang Xu, and Kun Zou. "Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition." Sensors 24, no. 18 (2024): 6097. http://dx.doi.org/10.3390/s24186097.
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Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed `VF-DCN’), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.
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Xie, Zhihua, Weigang Zhang, Lina Wang, Jianyong Zhou, and Zhiwei Li. "Optical and SAR Image Registration Based on the Phase Congruency Framework." Applied Sciences 13, no. 10 (2023): 5887. http://dx.doi.org/10.3390/app13105887.
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The improved phase congruency (PC) algorithms have been successfully applied to optical and synthetic aperture radar (SAR) image registration since they are insensitive to nonlinear radiometric and geometric differences. However, most of the algorithms are sensitive to large-scale differences and rotation differences between optical and SAR images. To tackle this, we propose a PC framework to register optical and SAR images. It is compatible with large-scale and rotation invariance. Firstly, a multi-scale Harris keypoint extraction method based on the maximum moment of PC (named PC-Harris) is proposed. The scale space is constructed by combining PC with the log-Gabor filter. Secondly, we propose a PC model to construct the feature descriptors. The orientation and amplitude responses are obtained based on the PC model. Meanwhile, the novel descriptor is constructed based on the polar coordinate system and thus can handle the scale and rotation differences between optical and SAR images. Finally, outliers are removed by the fast sample consensus (FSC). The experiments conducted on several optical and SAR images verify the effectiveness of the proposed framework.
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Landraud-Lamole, Anne M. "Principle of a Parallel Vision System Adapted to Textures." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 03 (1998): 355–78. http://dx.doi.org/10.1142/s0218001498000233.
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A number of methods for classifying textures and for segmentation of textured images make use of multichannel filtering techniques because of their computational simplicity. On the other hand, biological experiments have shown the existence and the properties of visual channels and make it possible for us to select the "best" filter bank. In a performing artificial vision system, three important problems are to be solved: the rigorous sampling of the spatial frequencies, the scale related problem and parallel processing. This paper presents a mathematical solution to the logarithmic 1-D sampling of the spatial frequencies of a signal characterized by its energy spectrum. This solution obeys the signal theory. It is then extended to the 2-D sampling of the output energy image generated by what we call a "homothetic filter bank". While verifying the Shannon theorem, our system is compatible with the visual cells' sensitivities to frequencies and orientations. Scale problems are often encountered in computer vision, especially in methods using multichannel filtering. Our approach gives us a representation of an input textured image with a continuous multiresolution. With this representation, an interpretation of the information content of the image, invariant to scale and to orientation, is made possible. Any scale change in the image is represented by a simple translation along a logarithmic frequency-axis. In the same manner, a rotation corresponds to a translation along a linear orientation-axis. In our "homothetic visual-filter bank" (HFB) theory, it is shown that the frequency-filter function can be changed according to the application under consideration. In cases where texture discrimination is difficult, e.g. if two different textures have the same power spectrum, the solution may be to extract phase information using a classical complex Gabor function. But, we have shown that in order to design a parallel vision system, it is more pertinent to use the differences of Gaussian functions (DOG) which are real functions.
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Moorthy, C., and K. R. Aravind Britto. "An Efficient Framework for the Segmentation of Glioma Brain Tumor Using Image Fusion and Co-Active Adaptive Neuro Fuzzy Inference System Classification Method." Journal of Medical Imaging and Health Informatics 11, no. 12 (2021): 3133–40. http://dx.doi.org/10.1166/jmihi.2021.3915.
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The image segmentation of any irregular pixels in Glioma brain image can be considered as difficult. There is a smaller difference between the pixel intensity of both tumor and non-tumor images. The proposed method stated that Glioma brain tumor is detected in brain MRI image by utilizing image fusion based Co-Active Adaptive Neuro Fuzzy Inference System (CANFIS) categorization technique. The low resolution brain image pixels are improved by contrast through image fusion method. This paper uses two different wavelet transforms such as, Discrete and Stationary for fusing two brain images for enhancing the internal regions. The pixels in contrast enhanced image is transformed into multi scale, multi frequency and orientation format through Gabor transform approach. The linear features can be obtained from this Gabor transformed brain image and it is being used to distinguish the non-tumor Glioma brain image from the tumor affected brain image through CANFIS method in this paper. The feature extraction and its impacts are being assigned on the proposed Glioma detection method is also examined in terms of detection rate. Then, morphological operations are involved on the resultant of classified Glioma brain image used to address and segment the tumor portions. The proposed system performance is analyzed with respect to various segmentation approaches. The proposed work simulation results can be compared with different state-of-the art techniques with respect to various parameter metrics and detection rate.
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Chen, Guang, Hai Gang Sui, Liang Dong, and Hua Sun. "Semi-Automatic Extraction Method for Low Contrast Road Based on Gabor Filter and Simulated Annealing." Advanced Materials Research 989-994 (July 2014): 3644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3644.
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High-resolution satellite remote sensing image are mostly used for accurate updating of GIS data. As the primary GIS data, urban roads on the image show the rich geometric features and radiation characteristics, that edge detection and grouping becoming an important way to solve the road extraction. However, edge elements obtained from images are always discontinuous for interference of noise and weak contrast between road and background. What more, vehicles, plant, buildings and shadow blocking results in weak grouping relation of elements. In processing, insignificant candidate road may be weeded out as noise and lead to failure road extraction. This paper presents a semi-automated extraction method for low contrast road basing on statistical grouping of orientation texture feature. Multi-direction and multi-scale Gabor filters are employed to detect directions of road texture. Then same direction pixels are grouped under constraining of rectangle template and generate road base elements. Finally, simulated annealing algorithm is used to optimize elements connection. Experiment results show that proposed method was effective in accurate extraction of low contrast road.
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Seyedhosseini, Mojtaba, S. Shushruth, Tyler Davis, et al. "Informative features of local field potential signals in primary visual cortex during natural image stimulation." Journal of Neurophysiology 113, no. 5 (2015): 1520–32. http://dx.doi.org/10.1152/jn.00278.2014.
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The local field potential (LFP) is of growing importance in neurophysiology as a metric of network activity and as a readout signal for use in brain-machine interfaces. However, there are uncertainties regarding the kind and visual field extent of information carried by LFP signals, as well as the specific features of the LFP signal conveying such information, especially under naturalistic conditions. To address these questions, we recorded LFP responses to natural images in V1 of awake and anesthetized macaques using Utah multielectrode arrays. First, we have shown that it is possible to identify presented natural images from the LFP responses they evoke using trained Gabor wavelet (GW) models. Because GW models were devised to explain the spiking responses of V1 cells, this finding suggests that local spiking activity and LFPs (thought to reflect primarily local synaptic activity) carry similar visual information. Second, models trained on scalar metrics, such as the evoked LFP response range, provide robust image identification, supporting the informative nature of even simple LFP features. Third, image identification is robust only for the first 300 ms following image presentation, and image information is not restricted to any of the spectral bands. This suggests that the short-latency broadband LFP response carries most information during natural scene viewing. Finally, best image identification was achieved by GW models incorporating information at the scale of ∼0.5° in size and trained using four different orientations. This suggests that during natural image viewing, LFPs carry stimulus-specific information at spatial scales corresponding to few orientation columns in macaque V1.
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Bassel, A. El-Azab. "Detection and Classification of Weft Knitted Fabrics Defects Using Gabor Wavelet." International Journal of Advances in Scientific Research and Engineering (ijasre) 6, no. 2 (2020): 85–96. https://doi.org/10.31695/IJASRE.2020.33718.
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<em>The globalization of competition, the complexity of the economy and the plethora of information available today place companies in a more than shifting context with which they must cope. Accelerating change becomes a constant feature of business life. Companies need methodological help to process a lot of information, In today's competitive world, customers are demanding better quality products with fast and reliable deliveries. To meet this demand, new manufacturing technologies are developing rapidly, resulting in new products and improvements in manufacturing processes.</em> <em>Today’s challenging world demands minimum loss and waste from industries. Moreover, it has to ensure the required quantity and quality with customer delivery lead time. A Circular weft knitting machine contains different parts such as needles, cams, sinkers, Fabric takedown mechanism, creel, a yarn metering and storage device, yarn breakage indicator, feeders and lubrication system. All those machine parts are responsible to increase or decrease the productivity of weft knit fabric production as well as the fabric quality. The Circular weft Knitting Machine has to stop when defects occurred and then faults are corrected, which results in a loss in time and efficiency in order to be ready to meet customer requirements; the goal is to quickly provide products that combine quality and competitive price. In this sense, effective monitoring is required to avoid defects and maintain high productivity and customer required quality. The purpose of this study is to identify and analyze weft knitted fabric defects on the weft circular knitting machine of knitting industries.</em> <em>This paper describes a computer vision-based fabric inspection system implemented on a weft circular knitting machine to detect defection and classification of the weft-knitted fabric defects under construction. We using Gabor Wavelets that have been successfully applied to various machine vision applications such as Texture segmentation, Edge detection, and Boundary detection, a multi-scale and multi orientation Gabor filter scheme simulates the human eye and that’s applied to the weft-knitted fabric under construction. On-line weft knitted fabric defect detection was tested automatically by analyzing fabric images captured by a digital camera using Gabor wavelets and classification (Identification) these fabric defects to known classes. We succeeded to detect weft knitted fabric defects and classify defect’s on the weft circular knitting machine at the same time the machine stops to correct the fabric defect to achieve customer required quantity and quality. As well as we cancel the fabric inspection process that means, saving money, time, manpower which leads to reducing production lead time and cost.</em>
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Zhi-Yong Tao, Zhi-Yong Tao, Meng Wang Zhi-Yong Tao, Xin-Ru Zhou Meng Wang, Jie Li Xin-Ru Zhou, and Sen Lin Jie Li. "FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding." 電腦學刊 34, no. 1 (2023): 013–27. http://dx.doi.org/10.53106/199115992023023401002.
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<p>To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we utilize the block-based Fisher Linear Discriminant method to reduce the dimension. Finally, the similarity components are calculated by the cosine distance and fused for the final finger-vein recognition results. We evaluate our proposed method on two publicly available datasets and one self-built dataset, i.e., Malaysian Polytechnic University (FV-USM), the Group of Machine Learning and Applications of Shandong University (SDUMLA-HMT), and our team, Signal and Information Processing Laboratory (FV-SIPL). On average, the proposed method achieved high recognition accuracy, i.e., 99.30%, and 1.10% equal error rates (EER). Overall, the proposed method performs better than most classical and state-of-the-art finger-vein recognition methods.</p> <p>&nbsp;</p>
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Liu, Ruoyang, Wenquan Zhu, and Xinyi Yang. "Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification." Remote Sensing 15, no. 24 (2023): 5747. http://dx.doi.org/10.3390/rs15245747.
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The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.
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Suresh Kumar, R., and A. R. Mahesh Balaji. "Land use land cover classification using local multiple pattern from very high resolution satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 971–76. http://dx.doi.org/10.5194/isprsarchives-xl-8-971-2014.
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The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.
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Wang, Qing Wei, and Zi Lu Ying. "Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation." Applied Mechanics and Materials 511-512 (February 2014): 433–36. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.433.
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This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.
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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 (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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Galvis, Laura Viviana, Reinel Corzo Rueda, and Henry Arguello. "Caving Depth Classification by Feature Extraction in Cuttings Images." Earth Sciences Research Journal 18, no. 2 (2015): 157–63. http://dx.doi.org/10.15446/esrj.v18n2.39716.
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<p>The estimation of caving depth is of particular interest in the oil industry. During the drilling process, the rock classification problem is studied to analyze the concentration of cuttings at the vibrating shale shakers through the classification of caving images. To date, depth estimation based on caving rock images has not been treated in the literature. This paper presents a new depth caving estimation system based on the classification of caving images through feature extraction. To extract the texture descriptors, the cutting images are first mapped on a common space where they can be easily compared. Then, textural features are obtained by applying a multi-scale and multi-orientation approach through the use of Gabor transformations. Two different depth classifiers are developed; the first separates the textural features by using a soft decision based on the Euclidean distance, and the second performs a hard decision classification by applying a thresholding procedure. A detailed mathematical formulation of the developed classifiers is presented.<br />The developed estimation system is verified using real data from rock cutting images in petroleum wells. Several simulations illustrate the performance of the proposed model using real images from a wellbore in a Colombian basin. The correct classification rate of a database containing 17 depth estimates is 91.2%.</p><p> </p><p><strong>Resumen</strong></p><p>La estimación de la profundidad de la que provienen los derrumbes que usualmente se presentan en las caras del pozo o también llamados cavings es de gran interés en la industria petrolera. Durante el proceso de perforación de un pozo, el problema de clasificación de rocas ha sido estudiado con el fin de analizar la concentración de recortes o ripios de perforación en las zarandas vibratorias a través de la clasificación de imágenes de cavings. Sin embargo, la estimación de la profundidad de los derrumbes basada en la utilización de imágenes de los mismos no ha sido tratada en la literatura. Este artículo presenta un nuevo modelo para la estimación de la profundidad de derrumbes a través de extracción de características. Para la extracción de estas características o descriptores de textura, imágenes de recortes son transformadas en un espacio común, el cual permite su comparación. Luego, las características se obtienen aplicando la transformación de Gabor, un enfoque que se caracteriza por proporcionar un análisis multi-escala y multi-orientación. Se desarrollaron dos clasificadores, el primero separa las características de textura usando un enfoque basado en la norma Euclideana y el segundo basado en decisiones por umbral. La formulación matemática detallada de los clasificadores desarrollados se presenta en este artículo.<br />El sistema de estimación desarrollado se evalúa usando datos reales de imágenes de derrumbes pertenecientes a un pozo petrolero. Simulaciones muestran el rendimiento del modelo propuesto usando imágenes reales de un pozo perteneciente a una cuenca Colombiana. La correcta clasificación para una base de datos de imágenes que contiene 17 clases o profundidades es de 91.2%.</p><p><strong><br /></strong></p>
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Li, Bin, Kaili Cheng, and Zhezhou Yu. "Histogram of Oriented Gradient Based Gist Feature for Building Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6749325.
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We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.
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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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Wenger, Michael, Stephanie Rhoten, Lisa De Stefano, Amy Barnett, and Laili Boozary. "Your Brain Knows if You're Iron Deficient: Distinct Brain Dynamics in Iron Deficient vs. Sufficient Females in a Visual Category Learning Task." Current Developments in Nutrition 5, Supplement_2 (2021): 929. http://dx.doi.org/10.1093/cdn/nzab049_042.
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Abstract Objectives The present study sought to determine the possibility of identifying someone as either iron deficient or sufficient based solely on brain activity, and, if possible, how quickly (based on processing time on a cognitive task) this could be done. Methods Both iron sufficient(IS) and iron deficient non-anemic (IDNA) females (mean age 21.1 y) learned two visual categorization tasks while concurrent EEG was acquired. Both tasks involved classifying gray-scale gabor patches on the basis of spatial frequency and orientation; one task used an easily-verbalized rule (rule-based, RB), the other required complex integration of the information (II). Moving windows (20 ms width) of EEG data from 100 electrodes were used to predict the participant's iron status using logistic regression; model form was determined using stepwise variable selection. The outcome variable was the area under the curve (AUC) of the receiver operating characteristic for the classification. We set a criterion of AUC ≥ 0.80 for successful classification performance. Results For both tasks, successful classification was possible before 200 ms of processing on the basis of fewer than 12 electrodes. Classification in the RB task suggested some early right lateralization in the selection of electrodes, which became more central as processing proceeded. Classification in the II task did not suggest lateralization, although there was some change to more central electrodes as processing proceeded. At each 20 ms time window, for each of the selected set of electrodes, a measure of neural efficiency was calculated as the ratio of the hazard function of the reaction time distribution and the global field power of the selected set of electrodes. This ratio can be interpreted as the amount of work accomplished per unit of energy expended. In all cases, neural efficiency for the IS females exceeded that for the IDNA females, suggesting that the efficiency with which neural energy is expended in cognitive work differs as a function of iron status. Conclusions Iron deficiency without anemia results in distinct patterns of brain activity early in processing that reflect reduced levels of neural efficiency, relative to females who are iron sufficient. Funding Sources OU Office of the Vice President for Research.
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Lei, Song Ze, and Qiang Zhu. "Human Ear Recognition Based on Phase Congruency and Kernel Discriminant Analysis." Applied Mechanics and Materials 241-244 (December 2012): 1614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1614.
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To solve the multi-pose ear recognition problem under the different illumination condition, a novel method which combines phase congruency with kernel discriminant analysis (KDA) is proposed. The phase congruency of ear image is first calculated using Log-Gabor filter with 5 scales and 8 orientations, and then the phase congruency of different orientation is constructed as high dimensional vector including ample information. The high dimensional vector is mapped to kernel space to acquire discriminant feature. Experimental results show that the proposed method obtains higher recognition rate compared with the other related methods. The method of the phase congruency can eliminate the influence of illumination and phase congruency with KDA is effective to multi-pose ear recognition.
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Gao, Xiaojing, Heru Xue, Xin Pan, Xinhua Jiang, Yanqing Zhou, and Xiaoling Luo. "Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (2017): 1757009. http://dx.doi.org/10.1142/s0218001417570099.
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In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.
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Lei, Song Ze, and Qiang Zhu. "Human Ear Recognition Using Hybrid Filter and Supervised Locality Preserving Projection." Advanced Materials Research 529 (June 2012): 271–75. http://dx.doi.org/10.4028/www.scientific.net/amr.529.271.
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To solve the difficult problem of human ear recognition caused by variety of ear angle, a novel method which combines hybrid filter with supervised locality preserving projection (SLPP) is proposed. The ear image is firstly filtered by Log-Gabor filter which is constructed with 5 scales and 8 orientations. The important parameters of Log-Gabor filter are selected through experiments. To form effective and discriminative feature too many Log-Gabor coefficients are reduced by discrete cosine transform. Lastly feature is constructed by SLPP to discovery geometrical rules. Experimental results show that compared with the traditional methods, the proposed method obtains higher recognition rate, and is robust to multi-pose of ear recognition.
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Li, Liangliang, Hongbing Ma, Xueyu Zhang, Xiaobin Zhao, Ming Lv, and Zhenhong Jia. "Synthetic Aperture Radar Image Change Detection Based on Principal Component Analysis and Two-Level Clustering." Remote Sensing 16, no. 11 (2024): 1861. http://dx.doi.org/10.3390/rs16111861.
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Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.
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Moyya, Priscilla Dinkar, and Mythili Asaithambi. "Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 12 (2022): 106–22. http://dx.doi.org/10.3991/ijoe.v18i12.32501.
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In this work, an attempt has been made to quantify the treatment response due to Neoadjuvant Chemotherapy (NACT) on the publicly available QIN-Breast of TCIA database (N = 25) using Gabor filter derived radiomic features. The Gabor filter bank is constructed using 5 different scales and 7 different orientations. Different radiomic features were extracted from Gabor filtered Dynamic Contrast Enhanced Magnetic Resonance images (DCE-MRI) of patients having 3 different visits (Visit 1: before, Visit 2: after 1st cycle, and Visit 3: the last cycle of NACT). The extracted radiomic features were analyzed statistically and Area Under Receiver Operating Characteristic (AUROC) has been calculated. Results show that the Gabor derived radiomic features could differentiate the pathological differences among all three visits. Energy has shown a significant difference between all the three orientations particularly between Visits 2 & 3. However, Entropy from λ=2 and θ=300 between Visit 2 & 3, Skewness from λ=2 and θ=1200 between Visit 1 & 3 could differentiate the treatment response with high statistical significance of p=0.006 and 0.001 respectively. From the ROC analysis, the better predictors were Short Run Emphasis (SRE), Short Zone Emphasis (SZE), and Energy between Visit 1 & 3 by achieving an AUROC of 76.38%, 75.16%, and 71.10% respectively. Further, the results suggest that the radiomic features are capable of quantitatively compare the breast NACT prognosis that varies across multi-oriented Gabor filters.
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Wang, Shu Yi, Jing Ling Wang, and Chuan Zhen Li. "Facial Expression Recognition Based on Multi-Channel Fusion of Gabor Features." Applied Mechanics and Materials 427-429 (September 2013): 1963–67. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1963.
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This paper presents a facial expression recognition algorithm based on multi-channel integration of Gabor feature. First, a Gabor wavelet filter extracts facial features with 5 scales and 8 orientations, and then transform the 40 channels into 13 channels according to the maximum rule presented in this paper. Second, we reduce the dimension of expression features by the method of PCA+LDA. At last, expression features are classified using the nearest neighbor method. The experiments involve two databases and show that the proposed algorithm can recognize facial expression in high rate.
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Alaabedi, Yasir A. F. "Gabor wavelet and neural network face detection." BIO Web of Conferences 97 (2024): 00100. http://dx.doi.org/10.1051/bioconf/20249700100.
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One of the most difficult tasks in image processing is facial area detection. This study introduces a new face detection method. To improve detection rates, the system incorporates two facial detection algorithms. Gabor wavelets and neural networks are the two algorithms. Convolutional face images undergo initial transformation using Gabor wavelets, with 8 orientations and 5 scales chosen to extract the grey characteristics of the facial region. When added to the original photos, these 40 Gabor wavelets reveal the full extent of the response. We use a second feedforward neural network specifically designed for facial detection. The neural network is trained by backpropagation using the training set of faces and non-faces. Our experiments show that the suggested Gabor wavelet faces, when combined with the neural network feature space classifier, provide very respectable results. Comparing our proposed system to other face detection systems reveals that it performs better in terms of detection and false negative rates.
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Gnanaprakash V, Et al. "Novel MobileNet based Multipath Convolutional Neural Network for defect detection in fabrics." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2417–23. http://dx.doi.org/10.17762/ijritcc.v11i9.9308.
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Automatic fabric defect detection and classification is the most important process in the textile industry to ensure the fabric quality. In the existing systems, a learning based method is used for detecting defects in plain weave fabrics. In this paper, a novel MobileNet based Multipath Convolutional Neural Network (MMPCNN) architecture is proposed for detection and classification of simple and complex patterned fabric defects. In the proposed MMPCNN architecture, MobileNet model is used in the first path. In this, Gabor filter bank is used instead of conventional filters in the first convolution layer. A simple convolutional neural network architecture with Gray Level Co-occurrence Matrix (GLCM) features as an input is used in the second path of the MMPCNN architecture. Gabor filters are more useful for analyzing the texture with different orientations and scales. Each Gabor filter parameter has its own impact on analyzing the texture and extracting the information from the texture. Therefore, in this paper, the use of Gabor filter parameters in MMPCNN architecture is analyzed. The proposed model is experimented on the TILDA textile image database and it is able to achieve 100% accuracy with reduced trainable parameters for fabric defect detection and classification.
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XU, GANG, HUCHUAN LU, and ZUNYI WANG. "FACE RECOGNITION BASED ON GPPBTF AND LBP WITH CLASSIFIER FUSION." International Journal of Image and Graphics 12, no. 02 (2012): 1250011. http://dx.doi.org/10.1142/s0219467812500118.
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Robust face recognition is a challenging problem, due to facial appearance variations in illumination, pose, expression, aging, partial occlusions and other changes. This paper proposes a novel face recognition approach, where face images are represented by Gabor pixel-pattern-based texture feature (GPPBTF) and local binary pattern (LBP), and null pace-based kernel Fisher discriminant analysis (NKFDA) is applied to the two features independently to obtain two recognition results which are eventually combined together for a final identification. To get GPPBTF, we first transform an image into Gabor magnitude maps of different orientations and scales, and then use pixel-pattern-based texture feature to extract texture features from Gabor maps. In order to improve the final performance of the classification, this paper proposes a multiple NKFDA classifiers combination approach. Extensive experiments on FERET face database demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also achieves more robust result and higher recognition accuracy.
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Qiao, Motong, Wei Wang, and Michael Ng. "Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance." Communications in Computational Physics 15, no. 5 (2014): 1480–500. http://dx.doi.org/10.4208/cicp.061212.111013a.
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AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.
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Yu, Guorong, and Shuangming Zhao. "A New Feature Descriptor for Multimodal Image Registration Using Phase Congruency." Sensors 20, no. 18 (2020): 5105. http://dx.doi.org/10.3390/s20185105.
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Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.
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Li, G. X., and Y. F. Li. "Defect Detection Study of Fabric Based on Gabor Filter Algorithm." Materials Science Forum 697-698 (September 2011): 491–94. http://dx.doi.org/10.4028/www.scientific.net/msf.697-698.491.
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This thesis exploits a multichannel Gabor filters detection algorithm. Analysis filtering images from different orientations and scales, then fuses the multichannel data. Finally, a threshold iterative algorithm and mathematical morphology post-processing is used to achieve the fabric defect detection. The experiment selects five types of fabric defect image. Experimental results suggest that this algorithm can effectively identify blob-shaped, linear and planar defect and has well real-time character.
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Sun, Ling Jun, Zhi Wei Ji, and Hang Jun Wang. "A New Wood Recognition Method Based on Texture Analysis." Applied Mechanics and Materials 58-60 (June 2011): 613–17. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.613.
Full textAbstract:
A novel and efficient wood recognition method based on texture analysis is presented in this paper. Firstly, the sample images are divided into several regions after cutting from wood stereogram images. Then, more features are extracted by Gabor Wavelets through five scales and eight orientations. For getting the key points of these Gabor features, clustering and sifting operation are used to dislodge the dimmed features that extracted from the noise regions, such as cleavage region, resin canal region and so on. Finally, the Earth Mover’s Distance is used as the distance measure to compare these features by nearest neighbor classifier. The experiment on 24 species and 480 sample images shows that the recognition rate can even up to 97.5 percents, which gives a satisfactory classification performance compared with the current state-of-the-art methods.
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Yu, Wei Wei. "Face Detection Based on Gabor Wavelet Transform and Statistical Cluster Model." Applied Mechanics and Materials 236-237 (November 2012): 1172–77. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.1172.
Full textAbstract:
A gray-image face detection algorithm was proposed, in which feature vectors were Gabor wavelet coefficients at 4 scales and 6 orientations. Firstly, feature vectors of training samples and negative samples were separately clustered. Secondly, these clusters were reduced by discriminate analysis. Finally, distribution model of feature vectors was built and average probability was calculated to determine if the testing image was similar to face samples. The experimental results show the algorithm is effective.
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Wang, Mingwei, Lang Gao, Xiaohui Huang, Ying Jiang, and Xianjun Gao. "A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer." Applied Sciences 9, no. 11 (2019): 2173. http://dx.doi.org/10.3390/app9112173.
Full textAbstract:
Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.