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A variational method with a noise detector for impulse noise removal

引用

1st international conference on scale space and variational methods in computer vision

作者： Chen, Shoushui Yang, Xin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

ISBN: (纸本)9783540728221

In this paper we propose a combined method for removing impulse noise. In the first phase, we use an efficient detector, called the Statistics of Ordered Difference Detector (SODD) to identify pixels which are likely to be corrupted by impulse noise. The proposed SODD can yield very high noise detection accuracy at high noise density. This noise detection phase is crucial for the following noise removal. In the second phase, only these noise candidates are restored using the variational method. Edges and noise free pixels of images filtered by our combined method are preserved. Simulation results indicate that the proposed method is significantly better than those using other impulse noise reduction filters.

关键词： Impulse noise

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Beauty with variational methods: An optic flow approach to hairstyle simulation

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1st international conference on scale space and variational methods in computer vision

作者： Demetz, Oliver Weickert, Joachim Bruhn, Andres Welk, Martin Univ Saarland Fac Math & Comp Sci Math Image Anal Grp Bldg E1-1 D-66041 Saarbrucken Germany

ISBN: (纸本)9783540728221

Although variational models offer many advantages in image analysis, their successful application to real-world problems is documented only for some specific areas such as medical imaging. In this paper we show how well-adapted variational ideas can solve the problem of hairstyle simulation in a fully automatic way: A customer in a hairdresser's shop selects a new hairstyle from a database, and this hairstyle is automatically registered to a digital image of the customer's face. Interestingly already a carefully modified optic flow method of Horn and Schunck turns out to be ideal for this application. These modifications include an extension to colour sequences, an incorporation of warping ideas in order to allow large deformation rates, and the inclusion of shape information that is characteristic for human faces. Employing classical numerical ideas such as finite differences and SOR iterations offers sufficient performance for real-life applications. In a number of experiments we demonstrate that our variational approach is capable of solving the hairstyle simulation problem with high quality in a fully practical setting.

关键词： variational techniques

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Generic maximum likely scale selection

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1st international conference on scale space and variational methods in computer vision

作者： Pedersen, Kim Steenstrup Loog, Marco Markussen, Bo Univ Copenhagen Dept Comp Sci Copenhagen Denmark Nordic Biosci AS Herlev Denmark Royal Vet & Agr Univ Dept Nat Sci DK-1870 Frederiksberg Denmark

ISBN: (纸本)9783540728221

The fundamental problem of local scale selection is addressed by means of a novel principle, which is based on maximum likelihood estimation. The principle is generally applicable to a broad variety of image models and descriptors, and provides a generic scale estimation methodology. The focus in this work is on applying this selection principle under a Brownian image model. This image model provides a simple scale invariant prior for natural images and we provide illustrative examples of the behavior of our scale estimation on such images. In these illustrative examples, estimation is based on second order moments of multiple measurements outputs at a fixed location. These measurements, which reflect local image structure, consist in the cases considered here of Gaussian derivatives taken at several scales and/or having different derivative orders.

关键词： Maximum likelihood estimation

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A variational approach for 3D motion estimation of incompressible PIV flows

引用

1st international conference on scale space and variational methods in computer vision

作者： Alvarez, Luis Castano, Carlos Garcia, Miguel Krissian, Karl Mazorra, Luis Salgado, Agustin Sanchez, Javier Univ Las Palmas Gran Canaria Dept Informat & Sistemas Las Palmas Gran Canaria Spain

ISBN: (纸本)9783540728221

Estimation of motion has many applications in fluid analysis. Lots of work has been carried out using Particle Image Velocimetry to design experiments which capture and measure the flow motion using 2D images. Recent technological advances allow capturing 3D PIV image sequences of moving particles. In this context, we propose a new three-dimensional variational (energy-based) technique. Our technique is based on solenoidal projection to take into account the incompressibility of the real flow. It uses the result of standard flow motion estimation techniques like iterative cross-correlation or pyramidal optical flow as an initialization, and improves significantly their accuracies. The performance of the proposed technique is measured and illustrated using numerical simulations.

关键词： Motion estimation

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Bounds on the minimizers of (nonconvex) regularized least-squares

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1st international conference on scale space and variational methods in computer vision

作者： Nikolova, Mila PRES UniverSud CNRS ENS Cachan CMLA F-94230 Cachan France

ISBN: (纸本)9783540728221

This is a theoretical study on the minimizers of cost-functions composed of an l(2) data-fidelity term and a possibly nonsmooth or non-convex regularization term acting on the differences or the discrete gradients of the image or the signal to restore. More precisely, we derive general nonasymptotic analytical bounds characterizing the local and the global minimizers of these cost-functions. We provide several bounds relevant to the observation model. For edge-preserving regularization, we exhibit a tight bound on the l(infinity) norm of the residual (the error) that is independent of the data, even if its l(2) norm is being minimized. Then we focus on the smoothing incurred by the (local) minimizers in terms of the differences or the discrete gradient of the restored image (or signal).

关键词： restoration regularization variational methods bounds l(infinity) norm edge preservation inverse problems non-convex analysis nonsmooth analysis

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Modeling foveal vision

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1st international conference on scale space and variational methods in computer vision

作者： Florack, Luc Eindhoven Univ Technol Dept Biomed Engn NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9783540728221

A geometric model is proposed for an artificial foveal vision system, and its plausibility in the context of biological vision is explored. The model is based on an isotropic, scale invariant two-form that describes the spatial layout of receptive fields in the the visual sensorium (in the biological context roughly corresponding to retina, LGN, and V1). It overcomes the limitation of the familiar log-polar model by handling its singularity in a graceful way. The log-polar singularity arises as a result of ignoring the physical resolution limitation inherent in any real (artificial or biological) visual system. The incorporation of such a limitation requires the introduction of a physical constant, measuring the radius of the geometric foveola (a central region characterized by maximal resolving power). The proposed model admits a description in singularity-free canonical coordinates that generalize the well-established log-polar coordinates, and that reduce to these in the asymptotic case of negligibly sized geometric foveola (or, equivalently, at peripheral locations in the visual field). Biological plausibility of the model is demonstrated by comparison with known facts on human vision.

关键词： generalized log-polar map foveal vision cortical magnification scale invariance receptive field scaling

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3D object recognition by eigen-scale-space of contours

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1st international conference on scale space and variational methods in computer vision

作者： Lee, Tim K. Drew, Mark S. British Columbia Canc Res Ctr Canc Control Res Program 675 W 10th Ave Vancouver BC V5Z 1L3 Canada Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9783540728221

People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algorithm for recognizing 3D objects using a new feature space, built from curvature scale space images, as a shape representation that is scale, translation, rotation and reflection invariant. As well, the new shape representation removes the inherent ambiguity of the zero position of arc length for a scale space image. The 2-stage matching algorithm, conducted in the eigenspaces of the feature space, is analogous to the way people recognize an object: first identifying the type of object, and then determining the actual object. We test the new algorithm on a 3D database comprising 209 colour objects in 2926 different view poses, and achieve a 97% recognition rate for the object type and 95% for the object pose.

关键词： object recognition shape 3D object scale-space filtering eigen-analysis

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A variational framework for the simultaneous segmentation and object Behavior classification of image sequences

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1st international conference on scale space and variational methods in computer vision

作者： Gui, Laura Thiran, Jean-Philippe Paragios, Nikos Ecole Polytech Fed Lausanne Signal Proc Inst Lausanne Switzerland Laboratoire MAS Ecole Centrale Paris France

ISBN: (纸本)9783540728221

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thereby enhancing the final result. In particular, classification offers dynamic probabilistic priors to guide segmentation, while segmentation supplies its results to classification, ensuring that they are consistent with prior knowledge. The prior models are learned from training data and updated dynamically, based on segmentations of earlier images in the sequence. We demonstrate the potential of our approach in a hand gesture recognition application, where the combined use of segmentation and classification improves robustness in the presence of occlusion and background complexity.

关键词： Object recognition

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Direct shape-from-shading with adaptive higher order regularisation

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1st international conference on scale space and variational methods in computer vision

作者： Vogel, Oliver Bruhn, Andres Weickert, Joachim Didas, Stephan Univ Saarland Fac Math & Comp Sci Math Image Anal Grp Bldg E1-1 D-66041 Saarbrucken Germany

ISBN: (纸本)9783540728221

Although variational methods are popular techniques in the context of shape-from-shading, they are in general restricted to indirect approaches that only estimate the gradient of the surface depth. Such methods suffer from two drawbacks: (i) They need additional constraints to enforce the integrability of the solution. (ii) They require the application of depth-from-gradient algorithms to obtain the actual surface. In this paper we present three novel approaches that avoid the aforementioned drawbacks by construction: (i) First, we present a method that is based on homogeneous higher order regularisation. Thus it becomes possible to estimate the surface depth directly by solving a single partial differential equation. (ii) Secondly, we develop a refined technique that adapts this higher order regularisation to semantically important structures in the original image. This addresses another drawback of existing variational methods: the blurring of the results due to the regularisation. (iii) Thirdly, we present an even further improved approach, in which the smoothness process is steered directly by the evolving depth map. This in turn allows to tackle the well-known problem of spontaneous concave-convex switches in the solution. In our experimental section both qualitative and quantitative experiments on standard shape-from-shading data sets are performed. A comparison to the popular variational method of Frankot and Chellappa shows the superiority of all three approaches.

关键词： computer vision shape-from-shading variational methods partial differential equations

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A variational framework for adaptive satellite images segmentation

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1st international conference on scale space and variational methods in computer vision

作者： Besbes, Olfa Belhadj, Ziad Boujemaa, Nozha INRIA Rocquencourt IMEDIA Project F-78153 Le Chesnay France URISA SUPCOM Ariana Tunisia

ISBN: (纸本)9783540728221

In this paper, we present an adaptive variational segmentation algorithm of spectral / texture regions in satellite images using level set. Satellite images contain both textured and non-textured regions, so for each region spectral and texture cues are integrated according to their discrimination power. Motivated by Fisher-Rao linear discriminant analysis;two region weights are defined to code respectively the relevance of spectral and texture cues. Therefore;regions with or without texture are processed in an unified framework. The obtained segmentation criterion is minimized via curves evolution within an explicit correspondence between the interiors of evolving curves and regions in the segmentation. The shape derivation principle is used to derive the system of coupled evolution equations in such a way that we consider the region weights and the statistical parameters variability. Experimental results on both natural and satellite images are shown.

关键词： Image segmentation

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