This short paper presents a general approach for computing robust Wasserstein barycenters (Agueh et al. 2011), (Turner et al. 2014),(Vidal et al. 2020) of persistence diagrams. The classical method consists in computing assignment arithmetic means after finding the optimal transport plans between the barycenter and the persistence diagrams. However, this procedure only works for the transportation cost related to the $q$q-Wasserstein distance $W_{q}$Wq when $q=2$q=2. We adapt an alternative fixed-point method (Tanguy et al. 2025) to compute a barycenter diagram for generic transportation costs ($q > 1$q>1), in particular those robust to outliers, $q \in (1,2)$q∈(1,2). We show the utility of our work in two applications: (i) the clustering of persistence diagrams on their metric space and (ii) the dictionary encoding of persistence diagrams (Sisouk et al. 2024). In both scenarios, we demonstrate the added robustness to outliers provided by our generalized framework.

This paper investigates the connections between rectified flows, flow matching, and optimal transport. Flow matching is a recent approach to learning generative models by estimating velocity fields that guide transformations from a source to a target distribution. Rectified flow matching aims to straighten the learned transport paths, yielding more direct flows between distributions. Our first contribution is a set of invariance properties of rectified flows and explicit velocity fields. In addition, we also provide explicit constructions and analysis in the Gaussian (not necessarily independent) and Gaussian mixture settings and study the relation to optimal transport. Our second contribution addresses recent claims suggesting that rectified flows, when constrained such that the learned velocity field is a gradient, can yield (asymptotically) solutions to optimal transport problems. We study the existence of solutions for this problem and demonstrate that they only relate to optimal transport under assumptions that are significantly stronger than those previously acknowledged. In particular, we present several counterexamples that invalidate earlier equivalence results in the literature, and we argue that enforcing a gradient constraint on rectified flows is, in general, not a reliable method for computing optimal transport maps.

This article presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters Turner et al. (2014), Vidal et al. (2020) of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization problem, which interleaves the optimization of the barycenter weights with the optimization of the atom diagrams. Our approach leverages the analytic expressions for the gradient of both sub-problems to ensure fast iterations and it additionally exploits shared-memory parallelism. Extensive experiments on public ensembles demonstrate the efficiency of our approach, with Wasserstein dictionary computations in the orders of minutes for the largest examples. We show the utility of our contributions in two applications. First, we apply Wassserstein dictionaries to data reduction and reliably compress persistence diagrams by concisely representing them with their weights in the dictionary. Second, we present a dimensionality reduction framework based on a Wasserstein dictionary defined with a small number of atoms (typically three) and encode the dictionary as a low dimensional simplex embedded in a visual space (typically in 2D). In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used to reproduce our results.

Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network. Among these models are the Variational Autoencoders and the Generative Adversarial Networks. In this work, we call them "push-forward" models and study their expressivity. We formally demonstrate that the Lipschitz constant of these generative networks has to be large in order to fit multimodal distributions. More precisely, we show that the total variation distance and the Kullback-Leibler divergence between the generated and the data distribution are bounded from below by a constant depending on the mode separation and the Lipschitz constant. Since constraining the Lipschitz constants of neural networks is a common way to stabilize generative models, there is a provable trade-off between the ability of push-forward models to approximate multimodal distributions and the stability of their training. We validate our findings on one-dimensional and image datasets and empirically show that the recently introduced diffusion models do not suffer of such limitation.

Since the seminal work of Venkatakrishnan et al. in 2013, Plug & Play (PnP) methods have become ubiquitous in Bayesian imaging. These methods derive Minimum Mean Square Error (MMSE) or Maximum A Posteriori (MAP) estimators for inverse problems in imaging by combining an explicit likelihood function with a prior that is implicitly defined by an image denoising algorithm. The PnP algorithms proposed in the literature mainly differ in the iterative schemes they use for optimisation or for sampling. In the case of optimisation schemes, some recent works guarantee the convergence to a fixed point, albeit not necessarily a MAP estimate. In the case of sampling schemes, to the best of our knowledge, there is no known proof of convergence. There also remain important open questions regarding whether the underlying Bayesian models and estimators are well defined, well-posed, and have the basic regularity properties required to support these numerical schemes. To address these limitations, this paper develops theory, methods, and provably convergent algorithms for performing Bayesian inference with PnP priors. We introduce two algorithms: 1) PnP-ULA (Unadjusted Langevin Algorithm) for Monte Carlo sampling and MMSE inference; and 2) PnP-SGD (Stochastic Gradient Descent) for MAP inference. Using recent results on the quantitative convergence of Markov chains, we establish detailed convergence guarantees for these two algorithms under realistic assumptions on the denoising operators used, with special attention to denoisers based on deep neural networks. We also show that these algorithms approximately target a decision-theoretically optimal Bayesian model that is well-posed. The proposed algorithms are demonstrated on several canonical problems such as image deblurring, inpainting, and denoising, where they are used for point estimation as well as for uncertainty visualisation and quantification.

This paper presents a unified computational framework for the estimation of distances, geodesics and barycenters of merge trees. We extend recent work on the edit distance [104] and introduce a new metric, called the Wasserstein distance between merge trees, which is purposely designed to enable efficient computations of geodesics and barycenters. Specifically, our new distance is strictly equivalent to the $L$2-Wasserstein distance between extremum persistence diagrams, but it is restricted to a smaller solution space, namely, the space of rooted partial isomorphisms between branch decomposition trees. This enables a simple extension of existing optimization frameworks [110] for geodesics and barycenters from persistence diagrams to merge trees. We introduce a task-based algorithm which can be generically applied to distance, geodesic, barycenter or cluster computation. The task-based nature of our approach enables further accelerations with shared-memory parallelism. Extensive experiments on public ensembles and SciVis contest benchmarks demonstrate the efficiency of our approach - with barycenter computations in the orders of minutes for the largest examples - as well as its qualitative ability to generate representative barycenter merge trees, visually summarizing the features of interest found in the ensemble. We show the utility of our contributions with dedicated visualization applications: feature tracking, temporal reduction and ensemble clustering. We provide a lightweight C++ implementation that can be used to reproduce our results.

The definition of an ”object” through the presentation of several of its instances is certainly one of the most efficient ways for humans and machines to learn. An object can be ”learned” from a single image, just because it is repeating. In this paper, we explore a three step algorithm to detect repeated objects in images. Starting from a graph of auto-correspondences inside an image, we first extract subgraphs composed of repetitions of unbreakable pieces of objects, that we call atoms. Then, these graphs of atoms are grouped into initial propositions of object instances. Finally, geometry inconsistencies are filtered out to end up with the final repeated object. The meaningfulness of object repetitions is measured by their Number of False Alarms (NFA), which provides a natural order among repeated objects in images; a very low NFA being a strong proof of existence of the discovered object. Source codes are available at https://rdguez-mariano.github.io/pages/autosim.

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast runtimes, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics.

The classic approach to image matching consists in the detection, description and matching of keypoints. In the description, the local information surrounding the keypoint is encoded. This locality enables affine invariant methods. Indeed, smooth deformations caused by viewpoint changes are well approximated by affine maps. Despite numerous efforts, affine invariant descriptors have remained elusive. This has led to the development of IMAS (Image Matching by Affine Simulation) methods that simulate viewpoint changes to attain the desired invariance. Yet, recent CNN-based methods seem to provide a way to learn affine invariant descriptors. Still, as a first contribution, we show that current CNN-based methods are far from the state-of-the-art performance provided by IMAS. This confirms that there is still room for improvement for learned methods. Second, we show that recent advances in affine patch normalization can be used to create adaptive IMAS methods that select their affine simulations depending on query and target images. The proposed methods are shown to attain a good compromise: on the one hand, they reach the performance of state-of-the-art IMAS methods but are faster; on the other hand, they perform significantly better than non-simulating methods, including recent ones. Source codes are available at https://rdguez-mariano.github.io/pages/adimas.

The classic approach to image matching consists in the detection, description and matching of keypoints. This defines a zero-order approximation of the mapping between two images, determined by corresponding point coordinates. But the patches around keypoints typically contain more information, which may be exploited to obtain a first-order approximation of the mapping, incorporating local affine maps between corresponding keypoints. In this work, we propose a LOCal Affine Transform Estimator (LOCATE) method based on neural networks. We show that LOCATE drastically improves the accuracy of local geometry estimation by tracking inverse maps. A second contribution on guided matching and refinement is also presented. The novelty here consists in the use of LOCATE to propose new SIFT-keypoint correspondences with precise locations, orientations and scales. Our experiments show that the precision gain provided by LOCATE does play an important role in applications such as guided matching. The third contribution of this paper consists in a modification to the RANSAC algorithm, that uses LOCATE to improve the homography estimation between a pair of images. These approaches outperform RANSAC for different choices of image descriptors and image datasets, and permit to increase the probability of success in identifying image pairs in challenging matching databases. The source codes are available at: https://rdguez-mariano.github.io/ pages/locate .

In this paper we introduce a Wasserstein-type distance on the set of Gaussian mixture models. This distance is defined by restricting the set of possible coupling measures in the optimal transport problem to Gaussian mixture models. We derive a very simple discrete formulation for this distance, which makes it suitable for high dimensional problems. We also study the corresponding multi-marginal and barycenter formulations. We show some properties of this Wasserstein-type distance, and we illustrate its practical use with some examples in image processing.

The classic approach to image matching consists in the detection, description and matching of keypoints. The descriptor encodes the local information around the keypoint. An advantage of local approaches is that viewpoint deformations are well approximated by affine maps. This motivated the quest for affine invariant local descriptors. Despite numerous efforts, such descriptors remained elusive, ultimately resulting in the compromise of using viewpoint simulations to attain affine invariance. In this work we propose a CNN-based patch descriptor which captures affine invariance without the need for viewpoint simulations. This is achieved by training a neural network to associate similar vectorial representations to patches related by affine transformations. During matching, these vectors are compared very efficiently. The invariance to translation, rotation and scale is still obtained by the first stages of SIFT, which produce the keypoints. The proposed descriptor outperforms the state-of-the-art in retaining affine invariant properties.

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperforms other patch-based competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as a small memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. The experiments show that our algorithm compares favorably to other state-of-art methods. Video examples, code and models are publicly available at https://github.com/m-tassano/dvdnet.

This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.

We propose a mathematical method to analyze the numerous algorithms performing image matching by affine simulation (IMAS). To become affine invariant they apply a discrete set of affine transforms to the images, prior to the comparison of all images by a scale invariant image matching (SIIM), like SIFT (scale invariant feature transform). Obviously this multiplication of images to be compared increases the image matching complexity. Three questions arise: (a) what is the best set of affine transforms to apply to each image to gain full practical affine invariance? (b) what is the lowest attainable complexity for the resulting method? (c) how is the underlying SIIM method chosen? We provide an explicit answer and a mathematical proof of quasi-optimality of the solution to the first question. As an answer to (b) we find that the near-optimal complexity ratio between full affine matching and scale invariant matching is more than halved, compared to the current IMAS methods. This means that the number of key points necessary for affine matching can be halved, and that the matching complexity is divided by four for exactly the same performance. This also means that an affine invariant set of descriptors can be associated with any image. The price to pay for full affine invariance is that the cardinality of this set is around 6.4 times larger than for a SIIM.

Abstract The realistic synthesis and rendering of film grain is a crucial goal for many amateur and professional photographers and film‐makers whose artistic works require the authentic feel of analogue photography. The objective of this work is to propose an algorithm that reproduces the visual aspect of film grain texture on any digital image. Previous approaches to this problem either propose unrealistic models or simply blend scanned images of film grain with the digital image, in which case the result is inevitably limited by the quality and resolution of the initial scan. In this work, we introduce a stochastic model to approximate the physical reality of film grain, and propose a resolution‐free rendering algorithm to simulate realistic film grain for any digital input image. By varying the parameters of this model, we can achieve a wide range of grain types. We demonstrate this by comparing our results with film grain examples from dedicated software, and show that our rendering results closely resemble these real film emulsions. In addition to realistic grain rendering, our resolution‐free algorithm allows for any desired zoom factor, even down to the scale of the microscopic grains themselves.

This paper presents a novel unsupervised method to transfer the style of an example image to a source image. The complex notion of image style is here considered as a local texture transfer, eventually coupled with a global color transfer. For the local texture transfer, we propose a new method based on an adaptive patch partition that captures the style of the example image and preserves the structure of the source image. More precisely, this example-based partition predicts how well a source patch matches an example patch. Results on various images show that our method outperforms the most recent techniques.

This paper addresses the problem of tonal fluctuation in videos. Due to the automatic settings of consumer cameras, the colors of objects in image sequences might change over time. We propose here a fast and computationally light method to stabilize this tonal appearance, while remaining robust to motion and occlusions. To do so, a minimally viable color correction model is used, in conjunction with an effective estimation of dominant motion. The final solution is a temporally weighted correction, explicitly driven by the motion magnitude, both visually efficient and very fast, with potential to real time processing. Experimental results obtained on a variety of sequences outperform the current state of the art in terms of tonal stability, at a much reduced computational complexity.

In this work, we present a fast and parametric method to achieve tonal stabilization in videos containing color fluctuations. Our main contribution is to compensate tonal instabilities with a color transformation guided by dominant motion estimated between temporally distant frames. Furthermore, we propose a temporal weighting scheme, where the intensity of tonal stabilization is directly guided by the motion speed. Experiments show that the proposed method compares favorably with the state-of-the-art in terms of accuracy and computational complexity.

The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles.

This paper introduces a new approach for the automatic estimation of illuminants in a digital color image. The method relies on two assumptions. First, the image is supposed to contain at least a small set of achromatic pixels. The second assumption is physical and concerns the set of possible illuminants, assumed to be well approximated by black body radiators. The proposed scheme is based on a projection of selected pixels on the Planckian locus in a well chosen chromaticity space, followed by a voting procedure yielding the estimation of the illuminant. This approach is very simple and learning-free. The voting procedure can be extended for the detection of multiple illuminants when necessary. Experiments on various databases show that the performances of this approach are similar to those of the best learning-based state-of-the-art algorithms.

Building high dynamic range (HDR) images by combining photographs captured with different exposure times present several drawbacks, such as the need for global alignment and motion estimation in order to avoid ghosting artifacts. The concept of spatially varying pixel exposures (SVE) proposed by Nayar et al. enables to capture in only one shot a very large range of exposures while avoiding these limitations. In this paper, we propose a novel approach to generate HDR images from a single shot acquired with spatially varying pixel exposures. The proposed method makes use of the assumption stating that the distribution of patches in an image is well represented by a Gaussian Mixture Model. Drawing on a precise modeling of the camera acquisition noise, we extend the piecewise linear estimation strategy developed by Yu et al. for image restoration. The proposed method permits to reconstruct an irradiance image by simultaneously estimating saturated and under-exposed pixels and denoising existing ones, showing significant improvements over existing approaches.

In disaster situations, remote sensing images are very useful to quickly assess damages. However, the choice of available images for the studied area is frequently limited. It is often needed to compare images acquired by different sensors and with different acquisition conditions. We propose a new feature-based approach to detect changes between a pair of either optical or radar images. This approach is based on the SIFT algorithm and an a contrario approach. It can deal with multi-resolutions, multi-sensors and multi-incidence angles situations, and it offers promising results.

Since the seminal work of Mann and Picard in 1995, the standard way to build high dynamic range (HDR) images from regular cameras has been to combine a reduced number of photographs captured with different exposure times. The algorithms proposed in the literature differ in the strategy used to combine these frames. Several experimental studies comparing their performances have been reported, showing in particular that a maximum likelihood estimation yields the best results in terms of mean squared error. However, no theoretical study aiming at establishing the performance limits of the HDR estimation problem has been conducted. Another common aspect of all HDR estimation approaches is that they discard saturated values. In this paper, we address these two issues. More precisely, we derive theoretical bounds for the performance of unbiased estimators for the HDR estimation problem. The unbiasedness hypothesis is motivated by the fact that most of the existing estimators, among them the best performing and most well known, are nearly unbiased. Moreover, we show that, even with a small number of photographs, the maximum likelihood estimator performs extremely close to these bounds. As a second contribution, we propose a general strategy for integrating the information provided by saturated pixels in the estimation process, hence improving the estimation results. Finally, we analyze the sensitivity of the HDR estimation process to camera parameters, and we show that small errors in the camera calibration process may severely degrade the estimation results.

High dynamic range (HDR) images are usually generated by combining multiple photographs acquired with different exposure times. This approach, while effective, suffers from various drawbacks. The irradiance estimation is performed by combining, for each pixel, different exposure values at the same spatial position. This estimation scheme does not take advantage of the redundancy present in most images. Moreover, images must be perfectly aligned and objects must be in the exact same position in all frames in order to combine the different exposures. In this work, we propose a new HDR image generation approach that simultaneously copes with these problems and exploits image redundancy to produce a denoised result. A reference image is chosen and a patch-based approach is used to find similar pixels that are then combined for the irradiance estimation. This patch-based approach permits to obtain a denoised result and is robust to image misalignments and object motions. Results show significant improvements in terms of noise reduction over previous HDR image generation techniques, while being robust to motion and changes between the exposures.

In this paper, we address the problem of the restoration of images which have been affected by impulse noise or by a mixture of Gaussian and impulse noise. We rely on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are run in the case of pure impulse noise and in the case of a mixture. The method proves to be particularly powerful, especially for the restoration of textured regions, and compares favorably to recent restoration methods.

In this paper, we show that a patch-based approach can successfully be applied for impulse noise removal. This requires careful choices for both the distance between patches and for the statistical estimator of the original patch. This method proves to be particularly powerful, especially for the restoration of textured areas, and compares favorably to recent restoration methods.

The scale invariant feature transform (SIFT) algorithm, commonly used in computer vision, does not perform well on synthetic aperture radar (SAR) images, in particular because of the strong intensity and the multiplicative nature of the noise. We present an improvement of this algorithm for SAR images. First, a robust yet simple way to compute gradient on radar images is introduced. This step is first used to develop a new keypoints extraction algorithm, based on the Harris criterion. Second, we rely on this gradient definition to adapt the computation of both the main orientation and the geometric descriptor to SAR image specificities. We validate this new algorithm with different experiments and present an application of our new SAR-SIFT algorithm.

In this paper, we introduce a class of local indicators that enable us to compute efficiently optimal transport plans associated with arbitrary weighted distributions of $N$ demands and $M$ supplies in $\mathbb{R}$ in the case where the cost function is concave. Indeed, whereas this problem can be solved linearly when the cost is a convex function of the distance on the line (or more generally when the cost matrix between points is a Monge matrix), to the best of our knowledge no simple solution has been proposed for concave costs, which are more realistic in many applications, especially in economic situations. The problem we consider may be unbalanced, in the sense that the weight of all the supplies might be larger than the weight of all the demands. We show how to use the local indicators hierarchically to solve the transportation problem for concave costs on the line.

This work is concerned with the modification of the gray level or color distribution of digital images. A common drawback of classical methods aiming at such modifications is the revealing of artefacts or the attenuation of details and textures. In this work, we propose a generic filtering method enabling, given the original image and the radiometrically corrected one, to suppress artefacts while preserving details. The approach relies on the key observation that artefacts correspond to spatial irregularity of the so-called transportation map, defined as the difference between the original and the corrected image. The proposed method draws on the nonlocal Yaroslavsky filter to regularize the transportation map. The efficiency of the method is shown on various radiometric modifications: contrast equalization, midway histogram, color enhancement, and color transfer. A comparison with related approaches is also provided.

Blotches are very common, localized, and non persistent impairments in digitized film archive. Many methods have been proposed so far for detecting them and restoring the underlying regions. Most detection techniques rely on the hypothesis that blotches contradict a model of motion regularity and, up to a prior motion compensation, correspond to significant temporal variations of intensity with respect to a global threshold. In this paper, we propose a statistical approach to detect blotches in image sequences, which yields thresholds adapted to the local statistics of the frames, and which takes into account gray level differences in neighborhoods instead of isolated points. This approach is combined with a block-based motion estimation. The whole procedure is confronted with classical approaches on several sequences.

In this paper, we take interest in the process of assigning a given color distribution to an image. Two examples of such image modifications are histogram equalization (or specification) and color transfer, in which the color palette of a style image is assigned to a source image. Classical methods for gray level specification, as well as more recent methods for color transfer, can be defined as optimal transportation problems. The corresponding image modifications are known to produce visually unpleasing effects such as the removal of details and texture, as well as the enhancement of noise or compression patterns. In this paper, a new method is proposed for the suppression of these artifacts. The method relies on a non local regularization of the transportation map, defined as the difference between the original image and the modified one. The interest of using this method is demonstrated on the aforementioned applications: contrast adjustment and color transfer.

In this paper, we address the problem of the restoration of image sequences which have been affected by local intensity modifications (local contrast changes). Such artifacts can be encountered particularly in biological or archive film sequences, and are usually due to inconsistent exposures or sparse time sampling. In order to reduce such local artifacts, we introduce a local stabilization operator, called LStab, which acts as a time filter on image patches and relies on a similarity measure which is robust to contrast changes. Thereby, this operator is able to take motion into account without relying on a sophisticated motion estimation procedure. The efficiency of the stabilization is shown on various sequences. The experimental results compare favorably with state-of-the-art approaches.

This paper focuses on the matching of local features between images. Given a set of query descriptors and a database of candidate descriptors, the goal is to decide which ones should be matched. This is a crucial issue, since the matching procedure is often a preliminary step for object detection or image matching. In practice, this matching step is often reduced to a specific threshold on the Euclidean distance to the nearest neighbor. Our first contribution is a robust distance between descriptors, relying on the adaptation of the Earth Mover's Distance to circular histograms. It is shown that this distance outperforms classical distances for comparing SIFT-like descriptors, while its time complexity remains reasonable. Our second and main contribution is a statistical framework for the matching procedure, which yields validation thresholds automatically adapted to the complexity of each query descriptor and to the diversity and size of the database. The method makes it possible to detect multiple occurrences, as well as to deal with situations where the target is not present. Its performances are tested through various experiments on a large image database.

In this paper, the matching of SIFT-like features [5] between images is studied. The goal is to decide which matches between descriptors of two datasets should be selected. This matching procedure is often a preliminary step towards some computer vision applications, such as object detection and image registration for instance. The distances between the query descriptors and the database candidates being computed, the classical approach is to select for each query its nearest neighbor, depending on a global threshold on dissimilarity measure. In this contribution, an a contrario framework for the matching procedure is introduced, based on a threshold on a probability of false detections. This approach yields dissimilarity thresholds automatically adapted to each query descriptor and to the diversity and size of the database. We show on various experiments on a large image database, the ability of such a method to decide whether a query and its candidates should be matched.

Many computer vision algorithms make use of local features, and rely on a systematic comparison of these features. The chosen dissimilarity measure is of crucial importance for the overall performances of these algorithms and has to be both robust and computationally efficient. Some of the most popular local features (like SIFT [4] descriptors) are based on one-dimensional circular histograms. In this contribution, we present an adaptation of the Earth moverpsilas distance to one-dimensional circular histograms. This distance, that we call CEMD, is used to compare SIFT-like descriptors. Experiments over a large database of 3 million descriptors show that CEMD outperforms classical bin-to-bin distances, while having reasonable time complexity.

In this paper, we present a set of texture features that are locally invariant to similarity or affinity. The proposed indexing scheme relies on the topographic map, a shape-based representation of images. Thanks to the hierarchical organization of the topographic map, the approach gives a grip on the multi-scale structure of textures. Using simple one dimensional histograms, the method is shown to achieve state-of-the-art performances among locally invariant methods, both on the whole Brodatz and UIUC databases.

In this work, we propose a method to segment a 1-D histogram without a priori assumptions about the underlying density function. Our approach considers a rigorous definition of an admissible segmentation, avoiding over and under segmentation problems. A fast algorithm leading to such a segmentation is proposed. The approach is tested both with synthetic and real data. An application to the segmentation of written documents is also presented. We shall see that this application requires the detection of very small histogram modes, which can be accurately detected with the proposed method.

Image flicker is a general film effect, which can be observed in videos as well as in old films, and consists of fast variations of the frame contrast and brightness. Reducing flicker of a sequence improves its visual quality and can be an essential first treatment before ulterior manipulations. This paper presents an axiomatic analysis of the problem, which leads to a global and fast method of "de-flicker," based on the scale-space theory. The stability of this process, called scale-time equalization, is ensured by the scale-time framework. Results on different sequences are given and show great visual improvement.

Color palettes are an important tool for color image analysis, since they are the initial point of different techniques such as quantization or indexing. This paper presents a new method for the automatic construction of a color palette, which adjusts dynamically its number of colors according to the visual content of the image. The method is based on appropriately segmenting the HSI color space, which is achieved by individually partitioning the histograms associated to each color component. As a result we obtain a hierarchical color palette, which represents the color image with a reduced number of colors.