With the explosive growth of digital transactions, the security of personal identity presents a serious challenge in our world today. It has become necessary to provide reliable and robust recognition systems. To over...
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A newly emerged technique known as Digital Rock Physics demonstrates an ability to characterize properties of the porous media. This technique is based on the imaging of rock micro-structure using a micro-CT scanner. ...
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ISBN:
(纸本)9781510841987
A newly emerged technique known as Digital Rock Physics demonstrates an ability to characterize properties of the porous media. This technique is based on the imaging of rock micro-structure using a micro-CT scanner. Images are segmented based on their grayscale values to extract pore network from the solid phase. Then, rock properties are estimated using extracted pore network and numerical simulations. Porosity and absolute permeability are two essential properties that can be derived from grayscale images. These properties represent storage and flow capacity of the rock. Some rock samples, particularly carbonate rocks have complex micro-structures at several length scales. Due to limited image resolution, 3D images of carbonate rock may not have top-bottom pore connectivity. In this case, one unable to simulate fluid flow throughout the images. Therefore, permeability is computed on small image sub-volumes, where pore connectivity is revealed locally. Such approach requires long simulation runs. In this paper, a new approach is proposed to estimate permeability from 3D carbonate rock images, where pore connectivity is not revealed from top to bottom. In this approach, first a number of texture classes that represents various textures in the 3D image are identified. For each identified texture class, several sub-volumes from the 3D image are extracted. These sub-volumes are representative of the identified textures and have local pore connectivity. To simulate fluid flow through the pore network of extracted sub-volumes Lattice Boltzmann method (LBM) is used. Permeability is then calculated using Darcy's equation. After determining permeability ranges of each textural class, the 3D image is divided into sub-volumes. Then, each sub-volume is classified into one of the specified texture classes using a modified version of the texture classification algorithm, local binary pattern (LBP). In this study, traditional 2D LBP texture feature vector is adopted to handle 3D
In the digital age, some places have been installed with a surveillance camera, especially in a parking lot. In order to make use of that camera, a parking space detection is developed to help users find empty spaces....
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This paper study about variants object detection by using local binary pattern. local binary pattern is one of the famous method in object detection field because of its success used in object detection. The objective...
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ISBN:
(纸本)9781467387804
This paper study about variants object detection by using local binary pattern. local binary pattern is one of the famous method in object detection field because of its success used in object detection. The objective of object detection is to differentiate between object and background. However, LBP also has its own weaknesses in object detection. LBP modification that been proposed by a lot of researchers can overcome the weaknesses. In this paper, variants of local binary pattern method and modification has been study and analyze. All those local binary pattern modification has been extract its feature in term of object detection. The modifications are Non-Redundant local binary pattern (NRLBP), Integral local binary pattern (INTLBP), Multi-scale Block local binary pattern (MBLBP) and Discriminative Robust local binary pattern (DRLBP).
In today's digital era, secret data hiding has become important part of information security. local binary pattern (LBP) operator which exploits the local intensity relationship of a coordinate with its neighborho...
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ISBN:
(纸本)9781509020287
In today's digital era, secret data hiding has become important part of information security. local binary pattern (LBP) operator which exploits the local intensity relationship of a coordinate with its neighborhood has been successfully implemented in texture classification and image retrieval. This paper proposes a novel steganographic technique based on LBP operator in wavelet domain for embedding and extraction of secret information. Proposed technique has been implemented in Matlab. Image quality metrics PSNR and SSIM are used to compare proposed technique with LSB substitution method.
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named local binary pattern Network (LBPNet), is proposed to ...
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ISBN:
(纸本)9781467399616
Deep learning is well known as a method to extract hierarchical representations of data. In this paper a novel unsupervised deep learning based methodology, named local binary pattern Network (LBPNet), is proposed to efficiently extract and compare high-level over-complete features in multilayer hierarchy. The LBPNet retains the same topology of Convolutional Neural Network (CNN) - one of the most well studied deep learning architectures - whereas the trainable kernels are replaced by the off-the-shelf computer vision descriptor (i.e., LBP). This enables the LBPNet to achieve a high recognition accuracy without requiring any costly model learning approach on massive data. Through extensive numerical experiments using the public benchmarks (i.e., FERET and LFW), LBPNet has shown that it is comparable to other unsupervised methods.
This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (TK). We focus on popular local binary patterns (LPBs) that are frequ...
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To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting ...
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To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases （the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases）. By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the twodimensional linear discriminant analysis （2DLDA） and local binary pattern （LBP） are proposed in this paper to heighten the recognition rates. In addition, partitioning a face nonuniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.
The LBP is a technique used to extract textures of an image, especially in facial image analysis. As we are intended to extract more fine texture details of an image to increase the rate of facial similarity, we combi...
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local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective fea...
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local binary pattern (LBP) is widely used to extract image features as well as motion features in various visual recognition tasks. LBP is formulated in quite a simple form and thus enables us to extract effective features with a low computational cost. There, however, are some limitations mainly regarding sensitivity to noise and loss of image contrast information. In this paper, we propose a novel LBP-based feature extraction method to remedy those drawbacks without degrading the simplicity of the original LBP formulation. LBP is built upon encoding local pixel intensities into binarypatterns which can be regarded as separating them into two modes (clusters). We introduce Fisher discriminant criterion to optimize the LBP coding for exploiting binarypatterns more stably and discriminatively with robustness to noise. Besides, image contrast information is incorporated in a unified way by leveraging the discriminant score as a weight on the binarypattern;therefore, the prominent patterns, such as around edges, are emphasized. The proposed method is applicable to extract not only image features but also motion features by both efficiently decomposing a XYT volume patch into 2-D patches and employing the effective thresholding strategy based on the volume patch. In the experiments on various visual recognition tasks, the proposed method exhibits superior performance compared to the ordinary LBP and the other methods.
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