A texture descriptor: BackGround Local Binary Pattern (BGLBP)

Davarpanah, Seyyed Hashem; Khalid, Fatimah; Abdullah, Lili Nurliyana; Golchin, Maryam

doi:10.1007/s11042-015-2588-3

Cited by 20 publications

(5 citation statements)

References 22 publications

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“…For local binary patterns, BGLBP [103]and XCS-LBP [453] appear to be the best LBP variants for background modeling and foreground detection while ST-SILTP and CS-SILTP seem to be the best LTP variants. SILS [589], LSS [246], LSBP [41] and LRP [592] present a robust alternative to LBP.…”

Section: Discussion On Texture Featuresmentioning

confidence: 98%

“…-BackGround Local Binary Pattern (BGLBP): Davarpanah et al [103] developed a BackGround LBP (BGLBP) which has been designed to inherit the positive properties of Direction Local Binary Pattern (D-LBP), CS-LBP [484], ULBP [587], and RI-LBP [588]. Experimentals results on the I2R dataset [306] show that BGLBP outperforms several variants of LBP such as the original LBP, ULBP and RI-LBP in presence of illumination changes and dynamic backgrounds.…”

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

“…In summary, three main properties are required for a designed LBP version for the application of background modeling and foreground detection [103]: 1) It should be fast to compute, 2) the number of bins in the plotted histogram should be the least, and 3) It should be computed based on whole pixels values belonging to each block. Thus, the original LBP [191][190] and the LBP based on gradient [432] are not op-timal.…”

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

“…The library was tested successfully with OpenCV 2.4.10. The local texture pattern available are the following ones: original LBP [191], CS-LBP [484], SCS-LBP [561], ǫ-LBP [524], HCS-LBP [559], OCLBP [296], VARLBP [383], BG-LBP [103], XCS-LBP [453], SILTP [311] and CS-SILTP [547].…”

Section: Lbp Librarymentioning

confidence: 99%

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On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

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Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing efficient in robustly modeling the background and hence detecting moving objects in any visual scene. Although several background subtraction and foreground detection have been proposed recently, no traditional algorithm today still seem to be able to simultaneously address all the key challenges of illumination variation, dynamic camera motion, cluttered background and occlusion. This limitation can be attributed to the lack of systematic investigation concerning the role and importance of features within background modeling and foreground detection. With the availability of a rather large set of invariant features, the challenge is in determining the best combination of features that would improve accuracy and robustness in T. Bouwmans Lab. MIA,

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Section: Discussion On Texture Featuresmentioning

confidence: 98%

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

Section: -Window-based Lbp (Wb-lbp)mentioning

confidence: 99%

Section: Lbp Librarymentioning

confidence: 99%

See 2 more Smart Citations

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Bouwmans¹,

Silva²,

Marghes³

et al. 2016

Preprint

View full text Add to dashboard Cite

show abstract

“…As LBP have been successfully used to describe spatial relations between pixels, some works explored embedding temporal information on LBP for object detection and background removal [10], [26]- [30]. Finally, Local Binary Pattern Network (LBPNet) was introduced by Xi et al [31] as a preliminary attempt to embed Deep Learning concepts in LBP.…”

Section: Introductionmentioning

confidence: 99%

Deep Local Binary Patterns

Fernandes,

Cardoso

2017

Preprint

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Local Binary Pattern (LBP) is a traditional descriptor for texture analysis that gained attention in the last decade. Being robust to several properties such as invariance to illumination translation and scaling, LBPs achieved stateof-the-art results in several applications. However, LBPs are not able to capture high-level features from the image, merely encoding features with low abstraction levels. In this work, we propose Deep LBP, which borrow ideas from the deep learning community to improve LBP expressiveness. By using parametrized data-driven LBP, we enable successive applications of the LBP operators with increasing abstraction levels. We validate the relevance of the proposed idea in several datasets from a wide range of applications. Deep LBP improved the performance of traditional and multiscale LBP in all cases.

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Multi-scale Multi-block Covariance Descriptor for a Compact Face Texture Representation: Application to Kinship Verification

Moujahid

Dornaika

2021

Advances in Science, Technology &Amp; Innovation

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A texture descriptor: BackGround Local Binary Pattern (BGLBP)

Cited by 20 publications

References 22 publications

On the Role and the Importance of Features for Background Modeling and Foreground Detection

On the Role and the Importance of Features for Background Modeling and Foreground Detection

Deep Local Binary Patterns

Multi-scale Multi-block Covariance Descriptor for a Compact Face Texture Representation: Application to Kinship Verification

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