CHARPIAT , Guillaume

The variational approach in image segmentation consists in defining a criterion depending on a contour and in computing its derivative with respect to the contour in order to minimize it with a gradient descent method. We propose a way to compute shape statistics of a sample set of contours and to incorporate them as a shape prior in the...

Shape evolutions, as well as shape matchings or image segmentation with shape prior, involve the preliminary choice of a suitable metric in the space of shapes. Instead of choosing a particular one, we propose a framework to learn shape metrics from a set of examples of shapes, designed to be able to handle sparse sets of highly varying shapes,...

Comment coloriser une image noir et blanc automatiquement, sans que l'utilisateur n'ait à intervenir ? Ce problème est plus complexe qu'il n'y paraît...

Graph cuts are widely used in many fields of computer vision in order to minimize in small polynomial time complexity certain classes of energies. These specific classes depend on the way chosen to build the graphs representing the problems to solve. We study here all possible ways of building graphs and the associated energies minimized,...

International audience

We propose a new method based on graph cuts for the segmentation of burned areas in time series of satellite images. The method consists in rewriting a segmentation problem as a (s, t)-min-cut on the spatio-temporal image graph and computing this minimal cut. As burned areas grow in time, we introduce growth constraint in graph cuts by using...

This article proposes a framework for dealing with several problems related to the analysis of shapes. Two related such problems are the definition of the relevant set of shapes and that of defining a metric on it. Following a recent research monograph by Delfour and Zolesio , we consider the characteristic functions of the subsets of ^2 and...

In this paper, we view multiple object tracking as a graphpartitioning problem. Given any object detector, we build the graph ofall detections and aim to partition it into trajectories. To quantifythe similarity of any two detections, we consider local cues such as pointtracks and speed, global cues such as appearance, as well as...

We propose a new representation of videos, as spatio- temporal fibers. These fibers are clusters of trajectories that are meshed spatially in the image domain. They form a hier- archical partition of the video into regions that are coherent in time and space. They can be seen as dense, spatially- organized, long-term optical flow. Their...

The level set representation of shapes is useful for shape evolution and is widely used for the minimization of energies with respect to shapes. Many algorithms consider energies depending explicitly on the signed distance function (SDF) associated with a shape, and differentiate these energies with respect to the SDF directly in order to make...

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We aim to color automatically greyscale images, without any manual intervention. The color proposition could then be interactively corrected by user-provided color landmarks if necessary. Automatic colorization is nontrivial since there is usually no one-to-one correspondence between color and local texture. The contribution of our framework is...

We propose a new binary partition tree (BPT)-based framework for multi-class segmentation of remote sensing images. In the literature, BPTs are typically computed in a bottom-up manner based on spectral similarities, then analyzed to extract image objects. When image objects exhibit a considerable internal spectral variability, it often happens...

We propose a new framework for multi-class image segmentation with shape priors using a binary partition tree. In the literature, such trees are used to represent hierarchical partitions of images, and are usually computed in a bottom-up manner based on color similarities, then analyzed to detect objects with a known shape prior. However, not...

A partition tree is a hierarchical representation of an image. Once constructed, it can be repeatedly processed to extract information. Multi-object multi-class image segmentation with shape priors is one of the tasks that can be efficiently done upon an available tree. The traditional construction approach is a greedy clustering based on color...

In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy, especially in remote sensing where manually curated public datasets are rare. We study the multi-modal cadaster map alignment problem for which available annotations...

A large part of the world is already covered by maps of buildings , through projects such as OpenStreetMap. However when a new image of an already covered area is captured, it does not align perfectly with the buildings of the already existing map, due to a change of capture angle , atmospheric perturbations, human error when annotating...

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of...

International audience

We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional...

We address the pixelwise classification of high-resolution aerial imagery. While convolutional neural networks (CNNs) are gaining increasing attention in image analysis, it is still challenging to adapt them to produce fine-grained classification maps. This is due to a well-known trade-off between recognition and localization: the impressive...