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66
A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape
- International Journal of Computer Vision
, 2007
"... Abstract. Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of reg ..."
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Cited by 169 (4 self)
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Abstract. Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals and point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.
Optical flow estimation and segmentation of multiple moving dynamic textures
- In CVPR
, 2005
"... We consider the problem of modeling a scene containing multiple dynamic textures undergoing multiple rigid-body motions, e.g., a video sequence of water taken by a rigidly moving camera. We propose to model each moving dynamic texture with a time varying linear dynamical system (LDS) plus a 2-D tran ..."
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Cited by 36 (4 self)
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We consider the problem of modeling a scene containing multiple dynamic textures undergoing multiple rigid-body motions, e.g., a video sequence of water taken by a rigidly moving camera. We propose to model each moving dynamic texture with a time varying linear dynamical system (LDS) plus a 2-D translational motion model. We first consider a scene with a single moving dynamic texture and show how to simultaneously learn the parameters of the time varying LDS as well as the optical flow of the scene using the socalled dynamic texture constancy constraint (DTCC). We then consider a scene with multiple non-moving dynamic textures and show that learning the parameters of each time invariant LDS as well as their region of support is equivalent to clustering data living in multiple subspaces. We solve this problem with a combination of PCA and GPCA. Finally, we consider a scene with multiple moving dynamic textures, and show how to simultaneously learn the parameters of multiple time varying LDS and multiple 2-D translational models, by clustering data living in multiple dynamically evolving subspaces. We test our approach on sequences of flowers, water, grass, and a beating heart. 1.
Shum.Synthesizing dynamic texture with closed-loop linear dynamic systems.In
- European Conference on Computer Vision
, 2004
"... Abstract. Dynamic texture can be defined as a temporally continuous and infinitely varying stream of images that exhibit certain temporal statistics. Linear dynamic system (LDS) represented by the state-space equation has been proposed to model dynamic texture[12]. LDS can be used to synthesize dyna ..."
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Cited by 30 (2 self)
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Abstract. Dynamic texture can be defined as a temporally continuous and infinitely varying stream of images that exhibit certain temporal statistics. Linear dynamic system (LDS) represented by the state-space equation has been proposed to model dynamic texture[12]. LDS can be used to synthesize dynamic texture by sampling the system noise. However, the visual quality of the synthesized dynamic texture using noise-driven LDS is often unsatisfactory. In this paper, we regard the noise-driven LDS as an open-loop control system and analyze its stability through its pole placement. We show that the noise-driven LDS can produce good quality dynamic texture if the LDS is oscillatory. To deal with an LDS not oscillatory, we present a novel approach, called closedloop LDS (CLDS) where feedback control is introduced into the system. Using the succeeding hidden states as an input reference signal, we design a feedback controller based on the difference between the current state and the reference state. An iterative algorithm is proposed to generate dynamic textures. Experimental results demonstrate that CLDS can produce dynamic texture sequences with promising visual quality. 1
Layered dynamic textures
- Advances in Neural Information Processing Systems 18
, 2006
"... A dynamic texture is a video model that treats a video as a sample from a spatio-temporal stochastic process, specifically a linear dynamical system. One problem associated with the dynamic texture is that it cannot model video where there are multiple regions of distinct motion. In this work, we in ..."
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Cited by 24 (5 self)
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A dynamic texture is a video model that treats a video as a sample from a spatio-temporal stochastic process, specifically a linear dynamical system. One problem associated with the dynamic texture is that it cannot model video where there are multiple regions of distinct motion. In this work, we introduce the layered dynamic texture model, which addresses this problem. We also introduce a variant of the model, and present the EM algorithm for learning each of the models. Finally, we demonstrate the efficacy of the proposed model for the tasks of segmentation and synthesis of video. 1
R.: Unsupervised texture segmentation with nonparametric neighborhood statistics. Computer Vision–ECCV
, 2006
"... Abstract. This paper presents a novel approach to unsupervised tex-ture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using spec ..."
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Cited by 24 (4 self)
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Abstract. This paper presents a novel approach to unsupervised tex-ture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental de-scription of textures as images derived from stationary random fields and models the associated higher-order statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropy-based metric on the prob-ability density functions of image neighborhoods to give an optimal seg-mentation. The entropy minimization drives a very fast level-set scheme that uses threshold dynamics, which allows for a very rapid evolution to-wards the optimal segmentation during the initial iterations. The method does not rely on a training stage and, hence, is unsupervised. It automat-ically tunes its important internal parameters based on the information content of the data. The method generalizes in a straightforward manner from the two-region case to an arbitrary number of regions and incor-porates an efficient multi-phase level-set framework. This paper presents numerous results, for both the two-texture and multiple-texture cases, using synthetic and real images that include electron-microscopy images. 1
Segmenting dynamic textures with Ising descriptors, ARX models and level sets
- in Dynamical Vision Workshop in the European Conf. on Computer Vision
, 2006
"... Abstract. We present a new algorithm for segmenting a scene consisting of multiple moving dynamic textures. We model the spatial statistics of a dynamic texture with a set of second order Ising descriptors whose temporal evolution of is governed by an AutoRegressive eXogenous (ARX) model. Given this ..."
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Cited by 22 (2 self)
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Abstract. We present a new algorithm for segmenting a scene consisting of multiple moving dynamic textures. We model the spatial statistics of a dynamic texture with a set of second order Ising descriptors whose temporal evolution of is governed by an AutoRegressive eXogenous (ARX) model. Given this model, we cast the dynamic texture segmentation problem in a variational framework in which we minimize the spatial-temporal variance of the stochastic part of the model. This energy functional is shown to depend explicitly on both the appearance and dynamics of the scene. Our framework naturally handles intensity and texture based image segmentation as well as dynamics based video segmentation as particular cases. Several experiments show the applicability of our method to segmenting scenes using only dynamics, only appearance, and both dynamics and appearance. 1
Clustering dynamic textures with the hierarchical EM algorithm
- In Proc. IEEE CVPR
, 2010
"... The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and vi ..."
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Cited by 20 (12 self)
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The dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and video registration. In this paper, we derive a new algorithm for clustering DT models that is based on the hierarchical EM algorithm. The proposed clustering algorithm is capable of both clustering DTs and learning novel DT cluster centers that are representative of the cluster members, in a manner that is consistent with the underlying generative probabilistic model of the DT. We then demonstrate the efficacy of the clustering algorithm on several applications in motion analysis, including hierarchical motion clustering, semantic motion annotation, and bag-ofsystems codebook generation. 1.
Modeling Music as a Dynamic Texture
- IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
"... We consider representing a short temporal fragment of musical audio as a dynamic texture, a model of both the timbral and rhythmical qualities of sound, two of the important aspects required for automatic music analysis. The dynamic texture model treats a sequence of audio feature vectors as a sampl ..."
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Cited by 19 (3 self)
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We consider representing a short temporal fragment of musical audio as a dynamic texture, a model of both the timbral and rhythmical qualities of sound, two of the important aspects required for automatic music analysis. The dynamic texture model treats a sequence of audio feature vectors as a sample from a linear dynamical system. We apply this new representation to the task of automatic song segmentation. In particular, we cluster audio fragments, extracted from a song, as samples from a dynamic texture mixture (DTM) model. We show that the DTM model can both accurately cluster coherent segments in music and detect transition boundaries. Moreover, the generative character of the proposed model of music makes it amenable for a wide range of applications besides segmentation. As an example, we use DTM models of songs to suggest possible improvements in some other music information retrieval applications such as music annotation and similarity.
Higher order SVD analysis for dynamic texture synthesis
- IEEE Transactions on Image Processing
, 2008
"... Abstract—Videos representing flames, water, smoke, etc., are often defined as dynamic textures: “textures ” because they are characterized by the redundant repetition of a pattern and “dynamic” because this repetition is also in time and not only in space. Dynamic textures have been modeled as linea ..."
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Cited by 13 (0 self)
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Abstract—Videos representing flames, water, smoke, etc., are often defined as dynamic textures: “textures ” because they are characterized by the redundant repetition of a pattern and “dynamic” because this repetition is also in time and not only in space. Dynamic textures have been modeled as linear dynamic systems by unfolding the video frames into column vectors and describing their trajectory as time evolves. After the projection of the vectors onto a lower dimensional space by a singular value decomposition (SVD), the trajectory is modeled using system identification techniques. Synthesis is obtained by driving the system with random noise. In this paper, we show that the standard SVD can be replaced by a higher order SVD (HOSVD), originally known as Tucker decomposition. HOSVD decomposes the dynamic texture as a multidimensional signal (tensor) without unfolding the video frames on column vectors. This is a more natural and flexible decomposition, since it permits us to perform dimension reduction in the spatial, temporal, and chromatic domain, while standard SVD allows for temporal reduction only. We show that for a comparable synthesis quality, the HOSVD approach requires, on average, five times less parameters than the standard SVD approach. The analysis part is more expensive, but the synthesis has the same cost as existing algorithms. Our technique is, thus, well suited to dynamic texture synthesis on devices limited by memory and computational power, such as PDAs or mobile phones. Index Terms—Dynamic textures, tensors, texture synthesis, singular value decomposition (SVD). I.
