Dynamic Textures
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Dynamic Textures Models And Applications
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Author : Bernard S. Ghanem
language : en
Publisher:
Release Date : 2010
Dynamic Textures Models And Applications written by Bernard S. Ghanem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Temporal or dynamic textures are video sequences that are spatially repetitive and temporally stationary. Dynamic textures are temporal analogs of the more common spatial texture. They are a family of visual phenomena where the texture elements, or the spatially repeating elements, undergo stochastic motions that are statistically similar. Dynamic textures appear in a vast spectrum of videos, ranging from sequences of moving water, foliage, smoke, and clouds to sequences of swarms of birds, robots, and even humans in crowds. Also, the applications concerning such video sequences are significant and numerous, including surveillance (e.g. monitoring traffic or crowds), detection of the onset of emergencies (e.g. outbreak of fires), and foreground and background separation (e.g. the transfer of a dynamic texture from one environment to another or simply dynamic texture removal). The study of dynamic textures poses numerous challenges, especially for traditional motion models that fail to capture the stochastic nature of dynamic textures. Despite their importance, the study of dynamic textures has just recently attracted the attention of the computer vision community. Most recent work on dynamic texture modeling represents these frames as the responses of a linear dynamical system (LDS) to noise. Despite its merits, this model has limitations because it attempts to model temporal variations in individual pixel intensities; such modeling does not take advantage of global motion coherence. In this dissertation, we will highlight the three main dimensions along which dynamic textures vary: the nature of the texture elements describing the dynamic texture spatially, their organization and layering, and their dynamics. We believe that no single spatiotemporal model can be proposed to handle dynamic textures sampled from this three-dimensional space. Instead, we propose three models, each of which applies to a certain ``range" of dynamic textures in this space. These three models by no means cover the whole space of dynamic textures; however, they build an essential framework or stepping-stone for future models. These models can be used in various applications, including dynamic texture synthesis, compression, recognition, and foreground and background layer separation. When possible, we compare the performance of these models to others in the literature in terms of recognition performance or computational efficiency. Developing these models and applying them to different problems uncovered several new and interesting problems that are also important to the fields of computer vision and image processing. We show how these new problems are generalized and efficiently addressed.
Dynamic Texture Modelling
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Author : Midori Hyndman
language : en
Publisher:
Release Date : 2006
Dynamic Texture Modelling written by Midori Hyndman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
Dynamic textured sequences are scenes with complex motion patterns due to the interactions between the multiple moving components. Examples of dynamic textures include blowing leaves, flickering flames and water splashing in a fountain. In our approach, the images of the sequence are interpreted as the output of a linear autoregressive process driven by white Gaussian noise. We extend earlier work by increasing the amount temporal information included when learning the motion in the scene allowing the models to capture motion patterns which extend over multiple frames, thereby, increasing the perceptual accuracy of the synthesized results. To overcome problems of dynamic model stability, we apply the Maximum Entropy Method (Burg, 1975) for parameter estimation, which is reliably stable on smaller samples of training data, even with higher-order dynamics.
Dynamic Textures
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Author : Gianfranco Doretto
language : en
Publisher:
Release Date : 2005
Dynamic Textures written by Gianfranco Doretto and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
Phase Based Modeling Of Dynamic Textures And Its Applications
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Author : Bernard Semaan Ghanem
language : en
Publisher:
Release Date : 2008
Phase Based Modeling Of Dynamic Textures And Its Applications written by Bernard Semaan Ghanem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Synthesis Of Dynamic Textures From Damaged Video Sequences
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Author :
language : en
Publisher:
Release Date : 2008
Synthesis Of Dynamic Textures From Damaged Video Sequences written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Segmentation Of Dynamic Textures With The Star Mixture Model
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Author : Lee Cooper
language : en
Publisher:
Release Date : 2006
Segmentation Of Dynamic Textures With The Star Mixture Model written by Lee Cooper and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Video compression categories.
Abstract: Emerging video technologies, including automated analysis and content-based compression, closely depend on motion modeling and often require the simultaneous use of multiple models to handle different regions of a video scene. The effective use of multiple models however presents a familiar problem in modeling mixed data: the circular dependence between model identification and segmentation. In this thesis the problem of mixture modeling and segmentation is considered for a special class of videos known as dynamic texture. A mixture of linear spatio-temporal models is developed to model data with distinct spatio-temporal characteristics, and the data is shown to be modeled equivalently by a mixture of subspaces within the feature space. The problem of clustering data onto an unknown number of subspaces with different dimensions is addressed with generalized principal component analysis to solve for the segmentation and models simultaneously. The results of this method are demonstrated with applications in medical imaging and video compression.
Two Stream Convolutional Networks For Dynamic Texture Synthesis
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Author : Matthew Tesfaldet
language : en
Publisher:
Release Date : 2018
Two Stream Convolutional Networks For Dynamic Texture Synthesis written by Matthew Tesfaldet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This thesis introduces a two-stream model for dynamic texture synthesis. The model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow regression. Given an input dynamic texture, statistics of filter responses from the object recognition and optical flow ConvNets encapsulate the per-frame appearance and dynamics of the input texture, respectively. To synthesize a dynamic texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. In addition, the synthesis approach is applied to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. Overall, the proposed approach generates high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, a quantitative evaluation of the proposed dynamic texture synthesis approach is performed via a large-scale user study.
Beyond Dynamic Textures
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Author : Antoni Bert Chan
language : en
Publisher:
Release Date : 2008
Beyond Dynamic Textures written by Antoni Bert Chan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Beyond dynamic textures: A family of stochastic dynamical models for video with applications to computer vision.
Dynamic Textures Using Non Linear Dimensionality Reduction
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Author : Ishan Awasthi
language : en
Publisher:
Release Date : 2006
Dynamic Textures Using Non Linear Dimensionality Reduction written by Ishan Awasthi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Imaging systems categories.
Modeling And Synthesis Of Dynamic Textures In Video Using An Autoregressive Conditional Heteroskedasticity Model
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Author : Tanya Lozoya Moreno
language : en
Publisher:
Release Date : 2012
Modeling And Synthesis Of Dynamic Textures In Video Using An Autoregressive Conditional Heteroskedasticity Model written by Tanya Lozoya Moreno and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Heteroscedasticity categories.