Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition

Yuan, Feiniu; Shi, Jinting; Xia, Xue; Zhang, Lin; Li, Shuying

doi:10.1016/j.cviu.2018.10.008

Cited by 19 publications

(17 citation statements)

References 50 publications

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“…We compared the proposed industrial smoke detection method with other LBP variant methods. These methods include PCLBP and scale space pairwise comparing local binary pattern (SPCLBP) method in [7], the texture feature extraction method based on LBP and LBPV image pyramid in the literature [8], the method of extracting texture features in multiple colour channels combined with LBP and grey level co‐occurrence matrix in the literature [12], and the method of LBP and LBPV which work independently. Owing to the different sizes of the sub‐images and the existence of zero pixels in the sub‐images, the extracted texture feature vectors are normalised according to the method shown in (12).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In this sense, static functions are more reliable and widely used than dynamic functions. Therefore, most visual smoke detections identify smoke areas in a single image by extracting static features [7]. Smoke areas that take slow changes or even stay static usually will form when the smoke is spreading.…”

Section: Introductionmentioning

confidence: 99%

“…Also, only the co‐occurrence matrix with a horizontal step size of 1 is extracted, so the performance is not very good in industrial smoke segmentation. In [7], Yuan et al measured and encoded the relationship between two pairs of LBP codes at the same position, applied a Gaussian filter to generate the scale‐space of the image, and the original LBP code is extracted on each scale. They also encoded the hamming distance metric pairwise comparing local binary pattern (PCLBP) and connect aligned PCLBP and LBP histograms as the descriptions of features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm

Liu

Zhang

et al. 2020

IET image process

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm

Liu

Zhang

et al. 2020

IET image process

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“…It is difficult to accurately detect the appearance of mentioned regions from images due to large variations of color intensities and texture. Although, many research works confirmed that texture features play a very important role in smoke and fire detection [ 3 , 4 ]. A wide recent work demonstrated that the multi-scale based techniques play an important role in smoke and texture classification [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig¹,

Bouchouicha

Tlig

et al. 2020

Sensors

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Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

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“…Thus it can acquire higher stability and recognition rate under complex illumination condition. Some classical local feature extraction algorithms mainly include Local Graphic Structure (LGS) [13], Scale Invariant Feature Transform (SIFT) [14], Local Phase Quantization (LPQ) [15], Local Derivative Pattern (LDP) [16], weighted Local Gabor (LG) [17], Local Gabor Binary Pattern (LGBP) [18], Local Differential Binary (LDB) [19], Local Linear Directional Pattern (LLDP) [20], Local Binary Pattern (LBP) [21], [22], Local Ternary Pattern (LTP) [23], Weber Local Descriptor (WLD) [24], local adapted ternary pattern (LATP) [23], [25], and centrosymmetric LTP with adaptive threshold (CS-LTPAT) [26] and more [27]- [38]. In fact, human perceptions of images depends not only on the absolute stimulus intensity, but also on the relative stimulus strength.…”

Section: Introductionmentioning

confidence: 99%

A Novel Illumination-Insensitive Feature Extraction Method

Gao

Zhang

et al. 2020

IEEE Access

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Image description is a great challenge in the field of computer vision under complex illumination condition. The complex illumination condition is usually unavoidable and unpredictable in real application. In this paper, we propose a novel generalized image descriptor, named as Anisotropy Weber Adapted Symmetric Ternary Pattern (AWASTP), which can overcome the directional inseparability of Weber Local Descriptor (WLD) and invariant threshold of Local Ternary Pattern (LTP). More particularly, we heighten the discriminative effectiveness of image description under complex illumination condition in several ways to restrain the effect of illumination variation. Firstly, a novel selection scheme for scale and angle is proposed. Based on this, an improved anisotropic Laplacian of Gaussian(ALOG) operator model is established by introducing the scale and angle parameter, moreover, an Anisotropic Weber Local Descriptor (AWLD) is presented, which can achieve more rich detailed information of illumination-insensitive feature. Secondly, an Adaptive Symmetric Ternary Pattern (ASTP) algorithm is proposed based on Weber criterion to generate more accurate threshold judgment according to the region characteristics. Thirdly, a twodimensional AWASTP histogram is created to enhance the discriminative power and represent illuminationinsensitive feature description. We conduct many experiments on benchmark databases, such as CMUPIE, FERET, PhoTex, RawFoot, and etc. Experimental results demonstrate the effectiveness of the proposed approach under different illumination conditions.

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Encoding pairwise Hamming distances of Local Binary Patterns for visual smoke recognition

Cited by 19 publications

References 50 publications

Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm

Target segmentation of industrial smoke image based on LBP Silhouettes coefficient variant (LBPSCV) algorithm

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

A Novel Illumination-Insensitive Feature Extraction Method

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