Multi-resolution local Gabor wavelets binary patterns for gray-scale texture description

Hadizadeh, Hadi

doi:10.1016/j.patrec.2015.07.038

Cited by 22 publications

(15 citation statements)

References 20 publications

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“…This paper presents the EMDNN model, which is mainly used to detect and reconstruct the weak signals in strong noise background, and reduce the problem of mode mixing [ 13 , 14 ] (when the signal is screened, some IMF components with different time scales will appear, which is called mode mixing). This method decomposes the modal function from high-frequency to low-frequency distribution [ 15 ], thereby reducing the loss of effective information.…”

Section: Weak Signal Reconstruction Methods Under Emdnn Modelmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

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Section: Weak Signal Reconstruction Methods Under Emdnn Modelmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

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“…Most of these methods were based on the structural or statistical properties of images, while some others were built on transformations or special models [12,46,72,73,85,96]. LBP, grey level co-occurrence matrix (GLCM), Gabor filter and wavelet transformation methods have been widely employed in the literature, in extracting the texture features [27] and the extracted features were classified by machine learning methods such as extreme learning machine (ELM) methods [8] and deep learning methods, which are very popular in recent years [4,5,16,17,19]. LBP is an efficient and simple statistical feature extraction method [55,56] and in LBP, each pixel was compared to its circular neighborhood pixels in order to extract local changes in an image [59].…”

Section: Related Literaturementioning

confidence: 99%

New local binary pattern approaches based on color channels in texture classification

Tekin

Ertuğrul

Kaya

2020

Multimed Tools Appl

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In this paper, four novel, simple and robust approaches, which are left to right local binary patterns (LBPL L2R), top to down local binary patterns (LBP T2D), cube surface local binary pattern (LBP Surfaces), and cube diagonal local binary pattern (LBP Diagonal), were proposed in order to exact texture features in color images. These approaches were based on the local binary pattern (LBP), which is an effective statistical texture descriptor and can be employed in gray images. Proposed approaches were evaluated and validated in four datasets, which are Outex, KTH_TIPS, KTH_TIPS2, and USPtex datasets. The images in these datasets are in RGB, HSV, YIQ, and YCbCr color formats. Achieved results by these approaches were compared with the obtained results by the classical LBP and literature findings. As a result, the proposed approaches performed better than the traditional LBP method and they found effective in the classification of color texture images, especially in images, which are in RGB and HSV formats. Furthermore, noise robustness and time complexity of the proposed approaches were validated.

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“…Experiments showed that the combination has significantly overcome the blur situation when representing faces. Hadizadeh [32] has proposed a combination of Gabor filter along with LBP in order to improve the texture classification. Both descriptors have been used to extract local and global features.…”

Section: Combined Texture Descriptorsmentioning

confidence: 99%

“…As shown in Figure 4, most of the studies that have examined the combination of Gabor and LBP were intended to extract more local and global features in order to overcome the specific issue. [31] Gabor + LBP + LPQ Blur (low-resolution) Face recognition Hadizadeh [32] Gabor + LBP Extending local and global features Texture classification Tao et al [33] Gabor + LBP Extending local and global features Face recognition Huang et al [34] Gabor + LBP + PCA Extending local and global features Object recognition Liu et al [35] 2D Gabor + LBP Extending local and global features Face recognition Khaleefah et al [36] Gabor + LBP Parameter tuning Paper fingerprinting Alharan et al [37] Gabor…”

Section: Combined Texture Descriptorsmentioning

confidence: 99%

A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems

Khaleefah

Mostafa

Samsudin

et al. 2020

IJEECS

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With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.

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Multi-resolution local Gabor wavelets binary patterns for gray-scale texture description

Cited by 22 publications

References 20 publications

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

New local binary pattern approaches based on color channels in texture classification

A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems

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