Local Binary Pattern Based Fast Digital Image Stabilization

Kir, Burcu; Kurt, Meltem; Urhan, Oğuzhan

doi:10.1109/lsp.2014.2359981

Cited by 13 publications

(16 citation statements)

References 16 publications

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“…The actual GMV, ground truth intentional motions, and retrieved intentional motions are shown in Figures 15-18. Tables 1 and 2 show the RMSE and SNR values obtained using six different DIS algorithms, including the MF [11], KF [15], wavelet decomposition (WD) method [19], EMD-based method [20], enhanced EMD-based (E-EMD) method [21], and the proposed method. First, from Tables 1 and 2, we can conclude that the MF generates the poorest results in Tests 1 and 2, KF in Test 4, and WD in Test 3.…”

Section: Performance Of the Vmd-re Methods In Dismentioning

confidence: 99%

“…From the preceding analysis, the modes behind the third mode correspond to jitter motions (including the third mode), whereas the first and second modes comprise intentional motions. considered to approximately obey Gaussian distribution [10,15]. Therefore, RE value will remain at low levels when the two modes are both intentional motion components; otherwise, RE value will remain at high levels.…”

Section: Intentional Motion Vector Reconstructionmentioning

confidence: 99%

“…However, MF performance highly depends on window size. Another traditional method is KF, which estimates intentional motions using a dynamic motion model [15][16][17]. KF uses the current observation and previously estimated state to generate intentional motion.…”

Section: Introductionmentioning

confidence: 99%

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Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Hao

2017

Entropy

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Cameras mounted on vehicles frequently suffer from image shake due to the vehicles' motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE). In this paper, the global motion vector (GMV) is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF), Kalman filter (KF), wavelet decomposition (MD) method, empirical mode decomposition (EMD)-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.

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Section: Performance Of the Vmd-re Methods In Dismentioning

confidence: 99%

Section: Intentional Motion Vector Reconstructionmentioning

confidence: 99%

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Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Hao

2017

Entropy

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“…It is gray-scale invariant local descriptor which was proposed by Ojala [2] and is a powerful descriptor for texture recognition. The descriptor is used in various fields of image processing problems such as shape localization [3], interest region description [4], gender classification [5], palm-print recognition [6], digital image stabilization [7] and especially face recognition problems [8]. There are a lot of improved versions of original LBP.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition

Kazak

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many texture recognition problems, Local Binary Patterns (LBP) method is used for feature extraction. This method is based on comparison of each centre pixel and its neighbours' intensity value in image. Due to its simplicity of calculation, LBP has become one of the most popular feature extraction techniques. In literature, different neighbourhood topologies of LBP structure are given such as circle, square, ellipse, parabola, hyperbola, and Archimedean spiral. This paper focuses on the use of uniform and basic LBP that have spiral topology in texture classification. We first derive basic and uniform LBP features based on spiral topology. Then the performances of several classification methods such as linear discriminant analysis (LDA), linear regression classifier (LRC), support vector machines (SVM), Chi-square test, and G-test are compared using these features in UIUC texture database.

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“…Firstly, eight areas of concern (SSW's -searching sub-windows) are selected in each frame. Then, they are binarized using enhanced Local Binary Patterns [17] method. Next, the correlation template (MSW -matching sub-window) is selected as a central part of corresponding SSW and is correlated with previous SSW using Number of non-matching points (NNMP) [17].…”

Section: Introductionmentioning

confidence: 99%