A texture-based method for modeling the background and detecting moving objects

By Marko Heikkilä, Matti Pietikäinen. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

1,357 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7673ffefa2d47b2aed3fe6cb8b95bb60d7127976

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In Computer Vision and Pattern Recognition

Citations in the corpus

Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation (2018, methodology): “Experimental results [222] on the CDnet 2012 dataset [62] demonstrate that SDAE gives better performance than traditional methods (MOG [180], KDE [49], LBP [77]), and recent state-of-art model (PBAS [81]).”
Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes (2010, neutral): “[1] [2] [4] [8] [10].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, background): “(2013) [424] LBP Histogram [141] LBP Histogram [141] LBP Histogram [141] RGB Mei et al.”
Competition on counter measures to 2-D facial spoofing attacks (2011, methodology): “Specifically, we use the GMM background modeling method [12] to detect the background and Viola-Jones face detector [26] to detect the face area.”
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling (2014, methodology): “, [34], [35]); we use what we consider to be easy”
Traditional and recent approaches in background modeling for foreground detection: An overview (2014, methodology): “Dynamic backgrounds Statistical Models: MOG [244][52][223][225], KDE [84][257][323][193], SVR [280], SVDD [255][253] Advanced Statistical Models: DMM [114], KGMM [77], KGHMM [168], ViBe [21], PBAS [124] Fuzzy Models: T2-FMOG [18] Domain Transform Models: Waviz [290], Wave-Back [205] Features: Textur”
Segmenting Salient Objects from Images and Videos (2010, methodology): “The results are compared with methods in [24, 21, 20, 10] from which the last mentioned is a general background subtraction method.”
Description of interest regions with local binary patterns (2009, methodology): “The local binary pattern (LBP) texture operator [19–21], has been highly successful for various computer vision problems such as face recognition [22], background subtraction [23], and recognition of 3D textured surfaces [24], but it has not been used for describing interest regions so far.”
Multi-Layer Background Subtraction Based on Color and Texture (2007, methodology): “[2] proposed to represent LBP texture feature using a2 -bin LBP histogram over a neighborhood region.”
Local binary features for texture classification: Taxonomy and experimental study (2017, neutral): “The six classes of our taxonomy”
Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval (2012, methodology): “To address this directional limitation, Gabor transform (GT) [11], rotated wavelet filters [12], dual-tree complex wavelet filters (DT-CWFs), DT rotated CWFs [13], and rotational invariant complex wavelet filters [14] have been proposed for texture image retrieval”
Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, methodology): “LBP and LBSP are dynamic texture approaches, FuzzyGGM is a fuzzy approach, AdaSOM is a neurofuzzy approach, and EigHMM uses a combination of HMM and eigenbackground (subspace based approach).”
Local Binary Patterns and Its Application to Facial Image Analysis: A Survey (2011, neutral): “…instance, face image analysis [3]-[4], image and video retrieval [5]-[6], environment modeling [7]-[8], visual inspection [9]-[10], motion analysis [11]-[12], biomedical and aerial image analysis [13]-[14], remote sensing [15], so forth (see a comprehensive bibliography of LBP methodology online…”
SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity (2015, methodology): “2 covers more pixels than the basic version of LBP or LTP (typically the 8-connected neighbors) without having to interpolate intensity values.”
Camera-trap images segmentation using multi-layer robust principal component analysis (2017, methodology): “tial coherence-based approach (Fuzzy-Adaptive-SOM) [21], Gaussian Mixture Model (GMM) [34], Adaptive Gaussian Mixture Model (AGMM) [43], Texture-based method (DPTexture) [13], and multi-layer background subtraction (multi-layer) [39].”
Ensemble Video Object Cut in Highly Dynamic Scenes (2013, methodology): “First row: original image from dataset AirportHall, Lobby, Curtain and WaterSurface (from left to right); Second row: ACMMM03 [12]; Third row: LBP-P [8]; Last row: proposed MBGC fine their segmentation results in a collaborative and iterative manner, achieving surprising good performance.”