In this paper, the face recognition system using a hybrid scale-invariant feature transform (SIFT) based on local binary pattern (LBP) has been implemented. The face recognition system is preprocessed to extract the f...
详细信息
In the time of increasing crime Face recognition is very significant with regards to computer vision, security, monitoring, reconnaissance, pattern checking, neural network, real-time video processing, etc. Face is no...
详细信息
In this paper, a novel local texture descriptor named Extended Adjacent local binary pattern (EALBP) for images is proposed. In this method, each image is subdivided into local regions of size 4*4. For each local regi...
详细信息
The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with the fixed-distance user cooperation. However, for t...
详细信息
In the hyperspectral classification, the combination of spectral information and spatial information has received more attention. Especially in the deep learning methods, massive spatial-spectral features which are he...
详细信息
In the hyperspectral classification, the combination of spectral information and spatial information has received more attention. Especially in the deep learning methods, massive spatial-spectral features which are helpful for improving the classification performance can be extracted. However, these methods suffer from time-consuming training process because of a great number of network parameters. In this paper, a novel architecture based on locality preserving projection (LPP), local binary pattern (LBP) and broad learning system (BLS) (LPP_LBP_BLS) for hyperspectral image (HSI) classi?cation is proposed, which mainly consists of three parts. First, LPP is applied to preserve the inherent local structure during dimensionality reduction of HSI in order to remove the redundant information in the spectral domain. Second, LBP is performed to extract the local grey-scale and rotation invariant texture features in each spectral reflectance band of the reduced-dimensional pixel in the spatial domain. It can fully utilize the spatial information of HSIs. Finally, BLS calculates the predictive sample labels according to the mapped feature nodes, enhancement nodes, and optimal connecting weights which are achieved through the normalized optimization of L2-norm solved by ridge regression approximation. LPP_LBP_BLS is beneficial for classification by combining spectral signatures with spatial information effectively. Experimental results demonstrate that the proposed architecture achieves above 99% classi?cation accuracy on Indian Pines dataset and Salinas dataset and above 97% classi?cation accuracy on Pavia University dataset, which outperforms other deep learning and traditional classification approaches.
local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its threshol...
详细信息
local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. To make LBP robust against noise, a directional thresholded LBP (DTLBP) is developed in this article which applies the directional neighboring pixels average values for thresholding. Applying this type of thresholding in addition to reducing noise, due to using the information of neighboring pixels with bigger radii, increases efficiency in extracting features. The DTLBP is able to be combined with other descriptors like completed LBP (CLBP) and local ternary pattern (LTP) which improves their functionality against noise. To evaluate the functionality of DTLBP, four known datasets including Outex (TC10), CUReT, UIUC and UMD are tested. Numerous and extensive experiments on these datasets with different kinds of noises indicate this newly developed descriptor's efficiency, with or without incremental white Gaussian and Gaussian blur noises. The proposed descriptor is compared with its available state of the art counterparts. The results show that the combination of DTLBP with CLBP descriptors provide the best classification accuracy in the experiments, which confirms the efficiency and robustness of the proposed descriptor when extracting features from noisy and raw images.
Multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. Nowadays, it has become an important research topic due to the applications in more and more sci...
详细信息
Multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. Nowadays, it has become an important research topic due to the applications in more and more scientific fields. However, preserving more information of the low-contrast area in the focus area and maintaining the edge information are two challenges for existing approaches. In this paper, we address these two challenges with presenting a simple yet efficient multi-focus fusion method based on local binary pattern (LBP). In our algorithm, we measure the clarity using the LBP metric and construct the initial weight map. And then we use the connected area judgment strategy (CADS) to reduce the noise in the initial map. Afterwards, the two source images are fused together by weighted arranging. The experimental results validate that the proposed algorithm outperforms state-of-the-art image fusion algorithms in both qualitative and quantitative evaluations, especially when dealing with low contrast regions and edge information. (C) 2018 Elsevier Ltd. All rights reserved.
Classification of tropical cyclone (TC) using cloud pattern is an evolving area of research. The classification result may use for intensity detection of a TC to mitigate the damage. TC classification using image proc...
详细信息
Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A m...
详细信息
Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel local binary pattern (MMLBP), which integrates the multi-channel localbinary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang's 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.
In this article, a novel pyramid and multi kernel based method is proposed to increased success of the local binary pattern (LBP). Signum, ternary and quaternary binary feature extraction functions are used together a...
详细信息
In this article, a novel pyramid and multi kernel based method is proposed to increased success of the local binary pattern (LBP). Signum, ternary and quaternary binary feature extraction functions are used together and these are utilized as mathematical kernel of the LBP. In order to extract features in depth, pyramid model is used. Texture images are resized in the 4 levels to create pyramid. Finally, 5120 features are extracted from each level. In the feature reduction phase, principle component analysis is considered and linear discriminant analysis is utilized as classifier. To obtain numerical results, UIUC, Outex and USPTex datasets were used. The proposed method was compared to the other state of art texture classification methods. The recognition rates were calculated as 96.10%, 89.90% and 97.30% for UIUC, Outex and USPTex respectively. The robustness tests were performed using the Gaussian and salt and pepper noises. The best accuracy rates of the noisy images were calculated as 79.5% and 94.3% respectively. The experimental results proved the success of the proposed method.
暂无评论