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23
Multiple Motion Segmentation With Level Sets
, 2000
"... Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. To date, a wealth of approaches to motion segmentation have been proposed. Many of them suffer from the local nature of models used. G ..."
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Cited by 55 (7 self)
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Segmentation of motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. To date, a wealth of approaches to motion segmentation have been proposed. Many of them suffer from the local nature of models used. Global models, such as those based on Markov random fields, perform, in general, better. In this paper, we propose a new approach to motion segmentation that is based on a global model. The novelty of the approach is twofold. First, inspired by recent work of other researchers we formulate the problem as that of region competition, but we solve it using the level set methodology. The key features of a level set representation, as compared to active contours, often used in this context, are its ability to handle variations in the topology of the segmentation and its numerical stability. The second novelty of the paper is the formulation in which, unlike in many other motion segmentation algori...
Object Classification in 3-D Images Using Alpha-Trimmed Radial Basis Function Network Mean
, 1999
"... We propose a pattern classification based approach for simultaneous 3-D object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. ..."
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Cited by 10 (2 self)
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We propose a pattern classification based approach for simultaneous 3-D object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids.
Prediction and Tracking of Moving Objects in Image Sequences
, 2000
"... We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initializa ..."
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Cited by 8 (2 self)
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We employ a prediction model for moving object velocity and location estimation derived from Bayesian theory. The optical flow of a certain moving object depends on the history of its previous values. A joint optical flow estimation and moving object segmentation algorithm is used for the initialization of the tracking algorithm. The segmentation of the moving objects is determined by appropriately classifying the unlabeled and the occluding regions. Segmentation and optical flow tracking is used for predicting future frames.
Navier-Stokes formulation for modelling turbulent optical flow
- in BMVC07
"... This paper proposes a physics-based methodology for the analysis of optical flows displaying complex patterns. Turbulent motion, such as that exhibited by fluid substances, can be modelled using fluid dynamics principles. Together with supplemental equations, such as the conservation of mass, and we ..."
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Cited by 4 (0 self)
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This paper proposes a physics-based methodology for the analysis of optical flows displaying complex patterns. Turbulent motion, such as that exhibited by fluid substances, can be modelled using fluid dynamics principles. Together with supplemental equations, such as the conservation of mass, and well formulated boundary conditions, the Navier-Stokes equations can be used to model complex fluid motion estimated from image sequences. In this paper, we propose to use a robust kernel which adapts to the local data geometry in the diffusion stage of the Navier-Stokes formulation. The proposed kernel is Gaussian and embeds the Hessian of the local data as its covariance matrix. The local Hessian models the variation of the flow in a certain neighbourhood. Moreover, we use a robust statistics mechanism in order to eliminate the outliers from the estimation process. The proposed methodology is applied on artificial vector fields and in image sequences showing atmospheric and solar phenomena. 1
Robust processing of optical flow of fluids
- IEEE Trans. Image Processing
"... Abstract—This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The ..."
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Cited by 4 (2 self)
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Abstract—This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The proposed methodology, which relies on Navier–Stokes equations, is used for processing fluid optical flow by using a succession of stages such as advection, diffusion and mass conservation. A robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework. The diffusion kernel is Gaussian with the covariance matrix defined by the local second derivatives. Such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly. A new approach is developed for detecting fluid flow structures such as vortices. The proposed methodology is applied on artificially generated vector fields as well as on various image sequences. Index Terms—Computational fluid dynamics, diffusion, optical flow of fluids, vortex detection. I.
Kernel Bandwidth Estimation for Nonparametric Modeling
, 2009
"... Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimati ..."
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Cited by 4 (1 self)
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Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the Schrödinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.
An EM-like Algorithm for Motion Segmentation via Eigendecomposition
- Proc. British Machine Vision Conf
, 2001
"... This paper presents an iterative maximum likelihood framework for motion segmentation via the pairwise checking of pixel blocks. We commence from a characterisation of the motion blocks in terms of a matrix of pairwise similarity weghts for their motion vectors. The eigenvectors of this similarity w ..."
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Cited by 4 (0 self)
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This paper presents an iterative maximum likelihood framework for motion segmentation via the pairwise checking of pixel blocks. We commence from a characterisation of the motion blocks in terms of a matrix of pairwise similarity weghts for their motion vectors. The eigenvectors of this similarity weight matrix represent the initial pairwise clusters, i.e the independant motions present in the scene. We develop a maximum likelihood framework which allows to update both the link weight matrix and the associated set of pairwise clusters. We experiment with the resulting clustering method on a number of real world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%. 1
Robust And Adaptive Techniques In Self-Organizing Neural Networks
, 1998
"... this paper, we shall describe robust and adaptive training algorithms that have been developed the past three years and aim at enhancing the capabilities of the self-organizing and the RBF neural networks [3]-[12] ..."
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Cited by 2 (0 self)
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this paper, we shall describe robust and adaptive training algorithms that have been developed the past three years and aim at enhancing the capabilities of the self-organizing and the RBF neural networks [3]-[12]
Optical flow diffusion with robustified kernels
- In Proc. International Conference on Computer Analysis of Images and Patterns, LNCS 3691
, 2005
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Detected motion classification with a double-background and a neighborhood-based difference
- Pattern Recognition Letters
"... This paper describes a new method to detect moving objects in a dynamic scene based on background subtraction. The main goal of the method is to obtain and keep a stable background image to cope with variations on environmental changing conditions. In this way, we use a double background (long-term ..."
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Cited by 1 (0 self)
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This paper describes a new method to detect moving objects in a dynamic scene based on background subtraction. The main goal of the method is to obtain and keep a stable background image to cope with variations on environmental changing conditions. In this way, we use a double background (long-term background and short-term background) to deal with temporal stability and fast changes. In addition, this method computes the temporal changes in the video sequence by a local convolution mask taking into account the information of the pixel neighborhood, being less sensitive to noise. Besides, the method classifies the regions of change in moving and static blobs. The first ones represent real moving objects, and the second are related to illumination changes and noise. Finally, experimental results and a performance measure establishing the confidence of the method are presented.
