Multi-scale region perpendicular local binary pattern: an effective feature for interest region description

Nguyen, Thao-Ngoc; Miyata, Kazunori

doi:10.1007/s00371-014-0934-5

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…-Several features have been used in other applications and none in background modeling and foreground detection such several variants of LBP (Multi-scale Region Perpendicular LBP (MRP-LBP) [374], Scale-and Orientation Adaptive LBP (SOA-LBP) [189]). Furthermore, statistical or fuzzy version of crisp feature could be investigated such as histograms of fuzzy oriented gradients [421].…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…The LBP is among the most widely used intensity based feature due to its computational simplicity [70], with applications in face recognition [71], [72], texture recognition or classification [73]- [77], video or receptive detection [78], [79], interest region description [80]- [82], and information retrieval [83]- [85]. LBP need to compare the graylevel intensity of a pixel with that of k of its neighbors at a pixel distance of r according to the LBP features for a given image patch, and LBP can obtain a binary vector expressing the relationship between the gray level intensity at the point of interest to each of its neighbors from the comparisons [60].…”

Section: B Intensity-based Methodsmentioning

confidence: 99%

Local Feature Descriptor for Image Matching: A Survey

Leng

Zhang

et al. 2019

IEEE Access

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Image registration is an important technique in many computer vision applications such as image fusion, image retrieval, object tracking, face recognition, change detection and so on. Local feature descriptors, i.e., how to detect features and how to describe them, play a fundamental and important role in image registration process, which directly influence the accuracy and robustness of image registration. This paper mainly focuses on the variety of local feature descriptors including some theoretical research, mathematical models, and methods or algorithms along with their applications in the context of image registration. The existing local feature descriptors are roughly classified into six categories to demonstrate and analyze comprehensively their own advantages. The current and future challenges of local feature descriptors are discussed. The major goal of the paper is to present a unique survey of the state-of-the-art image matching methods based on feature descriptor, from which future research may benefit. INDEX TERMS Local feature descriptor, image matching, point pattern matching, pattern recognition.

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Joint-scale LBP: a new feature descriptor for texture classification

Sun

2015

Vis Comput

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Multi-scale region perpendicular local binary pattern: an effective feature for interest region description

Cited by 10 publications

References 26 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

Local Feature Descriptor for Image Matching: A Survey

Joint-scale LBP: a new feature descriptor for texture classification

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