"Video Frame’s Background Modeling: Reviewing the Techniques"
written by Hamid Hassanpour, Mehdi Sedighi, Ali Reza Manashty,
published by Journal of Signal and Information Processing, Vol.2 No.2, 2011
has been cited by the following article(s):
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[9] On the Mahalanobis Distance Classification Criterion for a Ventricular Septal Defect Diagnosis System
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[12] Surveillance Video Synopsis Techniques: A Review.
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[18] Motion background modeling based on context-encoder
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[19] Parallel Computer Vision Algorithms for Graphics Processing Units
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[20] Orientation Invariant Autonomous Recognition of Individual Snow Leopards
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[23] Geographic reasoning on multi-modal fire spread data
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[24] A General-Purpose Sensing Floor Architecture for Human-Environment Interaction
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[26] Estudio e implementación de estrategias de detección de objetos móviles mediante métodos basados en el uso de Local Binary Patterns
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[29] Moving Object Detection Using Background Subtraction
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[30] Background and Literature Review
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[31] 彩色视频运动目标自适应在线聚类提取算法
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[33] Robust subspace estimation using low-rank optimization: theory and applications
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[35] pROST: a smoothed -norm robust online subspace tracking method for background subtraction in video
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[36] A novel video dataset for change detection benchmarking.
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[37] A Fuzzy System for Background Modeling in Video Sequences.
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[38] Object Detection Based on Spatio-temporal Light Field Sensing
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[39] ROBUST SUBSPACE ESTIMATION USING LOW-RANK OPTIMIZATION.
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[40] A Fuzzy System for Background Modeling in Video Sequences
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[41] Improved background modeling for real-time spatio-temporal non-parametric moving object detection strategies
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[42] GPU-based implementation of an optimized nonparametric background modeling for real-time moving object detection
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[43] pROST: A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video
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[44] On the Mahalanobis distance classification criterion for multidimensional normal distributions
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[46] Hardware Acceleration of Background Modeling in the Compressed Domain
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[47] 基于DWT 的自适应在线聚类运动目标提取方法
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