An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos

Silva, Caroline; Bouwmans, Thierry; Frélicot, Carl

doi:10.5220/0005266303950402

Cited by 105 publications

(46 citation statements)

References 15 publications

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“…LBP is a texture descriptor proposed by Ojala et al [23]. LBP describes the texture in the images by using the histogram of label values that obtained the result from the thresholding between the neighborhood pixels with the center pixel [24,25]. Although the LBP operator was initially meant for the texture descriptor, it applicability has been extended and applied in various recognition task by some modification [26].…”

Section: Related Workmentioning

confidence: 99%

“…Al-Hammadi et al [24] improved the LBP detection by using the Curvelet transform while Abdulrahman et al [27] had used Gabor Wavelet and Principal Component Analysis (PCA). Though there are many version variations of local descriptor based on LBP method [25], the LBP shows some weaknesses especially for rotation, translation and scale object even for specific LBP rotation invariant version [28,29]. Thus, Papakostas et al [26] introduced Moment-Based Local Binary Patterns to improve the invariant behavior of LBP method towards rotation, translation and scaling conditions.…”

Section: Related Workmentioning

confidence: 99%

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Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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“…However, most of them are either computationally expensive or result in long histograms. In (Silva et al, 2015) a comparison of these methods is provided. In this paper, we propose a new feature descriptor named STCS-LDP (Spatio-Temporal Center Symmetric Local Derivative Patterns) which is an optimized and enhanced version of an LBP variant proposed in (Xue et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance

Jmal

Souidene

Attia

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Nowadays, more attention is being focused on background subtraction methods regarding their importance in many computer vision applications. Most of the proposed approaches are classified as pixel-based due to their low complexity and processing speed. Other methods are considered as spatiotemporal-based as they consider the surroundings of each analyzed pixel. In this context, we propose a new texture descriptor that is suitable for this task. We benefit from the advantages of local binary patterns variants to introduce a novel spatiotemporal center-symmetric local derivative patterns (STCS-LDP). Several improvements and restrictions are set in the neighboring pixels comparison level, to make the descriptor less sensitive to noise while maintaining robustness to illumination changes. We also present a simple background subtraction algorithm which is based on our STCS-LDP descriptor. Experiments on multiple video sequences proved that our method is efficient and produces comparable results to the state of the art.

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“…The RGB domain is the most popular [8,9], but some authors exploit other color spaces such as HSV [10] or YCbCr [11] in order to increase invariance with respect to brightness changes, and thus illumination changes and shadows. Gradient features [12] and texture features such as LBP histogram [13] or its variants [14,15] offer a robust solution to illumination changes but might be unsuitable in image areas with poor texture. Motion features, such as optical flow [16], should be particularly interesting for scenes where the foreground is moving continuously (absence of temporarily stopped objects) and depth features acquired with range cameras such as the Kinect camera [17] are (to some degree) insensitive to lighting conditions but cannot be computed when objects are far from the camera.…”

Section: Introductionmentioning

confidence: 99%

A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models

Braham

Droogenbroeck

2015

Advanced Concepts for Intelligent Vision Systems

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Abstract. Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been devoted to the development of robust background models, the automatic spatial selection of useful features for representing the background has been neglected. In this paper, we propose a generic and tractable feature selection method. Interesting contributions of this work are the proposal of a selection process coherent with the segmentation process and the exploitation of global foreground models in the selection strategy. Experiments conducted on the ViBe algorithm show that our feature selection technique improves the segmentation results.

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An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos

Cited by 105 publications

References 15 publications

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance

A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models

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