Smoothing B-spline active contour for fast and robust image and video segmentation

Precioso, Fŕed́eric; Barlaud, Michel; Blu, Thierry; Unser, Michaël

doi:10.1109/icip.2003.1246917

Cited by 19 publications

(5 citation statements)

References 27 publications

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“…Since the vector fields used are assumed to contain only one object, changes of topology were not an issue. Therefore, we chose an explicit representation based on evolving spline curves [31,6,19,25]. For translation, the segmentation algorithm can be described synthetically as follows 1.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of a Vector Field: Dominant Parameter and Shape Optimization

et al. 2006

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Vector field segmentation methods usually belong to either of three classes: methods which segment regions homogeneous in direction and/or norm, methods which detect discontinuities in the vector field, and region growing or classification methods. The first two classes of method do not allow segmentation of complex vector fields and control of the type of fields to be segmented, respectively. The third class does not directly allow a smooth representation of the segmentation boundaries. In the particular case where the vector field actually represents an optical flow, a fourth class of methods acts as a detector of main motion. The proposed method combines a vector field model and a theoretically founded minimization approach. Compared to existing methods following the same philosophy, it relies on an intuitive, geometric way to define the model while preserving a general point of view adapted to the segmentation of potentially complex vector fields with the condition that they can be described by a finite number of parameters. The energy to be minimized is deduced from the choice of a specific class of field lines, e.g. straight lines or circles, described by the general form of their parametric equations. In that sense, the proposed method is a principled approach for segmenting parametric vector fields. The minimization problem was rewritten into a shape optimization and implemented by splinebased active contours. The algorithm was applied to the segmentation of precomputed optical flow fields given by an external, independent algorithm.

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Section: Methodsmentioning

confidence: 99%

Segmentation of a Vector Field: Dominant Parameter and Shape Optimization

et al. 2006

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“…Furthermore, this first step provides an efficient estimation of the 8 parameters of the bilinear camera motion model. As a second step, the previous motion segmentation is used to initialize a region-based motion segmentation algorithm based on smoothing spline active contours [11]. Smoothing splines offer a robust active contour implementation to overcome the problem of noisy data that working with MPEG streams implies.…”

Section: Color and Motion Initial Segmentationmentioning

confidence: 99%

Knowledge-Assisted Video Analysis for Content-Adaptive Coding and Transmission

Mezaris

Boulgouris

Kompatsiaris

2006

2006 First International Workshop on Semantic Media Adaptation and Personalization (SMAP'06)

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“…A smoothing spline is an approximation curve controlled by a parameter balancing the trade-off between interpolation error and smoothness. 35,36 It minimizes the following criterion…”

Section: Smoothing Splinesmentioning

confidence: 99%

From Snakes to Region-Based Active Contours Defined by Region-Dependent Parameters

Jehan‐Besson¹,

Gastaud²,

Precioso³

et al. 2004

Appl. Opt.

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Image and sequence segmentation of a the segmentation task are discussed from the point of view of optimizing the segmentation criterion. Such a segmentation criterion involves so-called (boundary and region) descriptors, which, in general, may depend on their respective boundaries or regions. This dependency must be taken into account when one is computing the criterion derivative with respect to the unknown object domain (defined by its boundary). If this dependency not considered, some correctional terms may be omitted. Computing the derivative of the segmentation criterion with a dynamic scheme is described. The scheme is general enough to provide a framework for a wide variety of applications in segmentation. It also provides a theoretical meaning to the philosophy of active contours.

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Smoothing B-spline active contour for fast and robust image and video segmentation

Cited by 19 publications

References 27 publications

Segmentation of a Vector Field: Dominant Parameter and Shape Optimization

Segmentation of a Vector Field: Dominant Parameter and Shape Optimization

Knowledge-Assisted Video Analysis for Content-Adaptive Coding and Transmission

From Snakes to Region-Based Active Contours Defined by Region-Dependent Parameters

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