Fast image and video segmentation based on alpha-tree multiscale representation

International audienceIn this paper, we propose to rely on a recent image representation model, namely the α-tree, to achieve efficient segmentation of images and videos. The α-tree is a multiscale representation of an image, based on its quasi-flat zones. An in-depth study of this tree reveals some interesting features of image pixels and regions. These features are then used in the design of both automatic and interactive segmentation algorithms. Interactivity is achieved thanks to a new and efficient implementation scheme. Experiments on the Berkeley Segmentation Dataset lead to very promising results

Hyperspectral image representation through alpha-trees

International audienceα-trees provide a hierarchical representation of an image into partitions of regions with increasing heterogeneity. This model, inspired from the single-linkage paradigm, has recently been revisited for grayscale images and has been successfully used in the field of remote sensing. This article shows how this representation can be adapted to more complex data here hyperspectral images, according to different strategies. We know that the measure of distance between two neighbouring pixels is a key element for the quality of the underlying tree, but usual metrics are not satisfying. We show here that a relevant solution to understand hyperspectral data relies on the prior learning of the metric to be used and the exploitation of domain knowledge

Interoperability of multiscale visual representations for satellite image big data

International audienceIn this paper, we propose an interoperable solution for dealing with hierarchical representations of satellite images. Computationally intensive construction of the tree representation is performed on the server side, while the need for computational resources on the client side are greatly reduced (tree postprocessing or visualization). The proposed scheme is interoperable in the sense that it does not impose any constraints on the client environment, and API for C++, Java and Python languages are currently available. The communication is performed nodewise in a binary format using array structure for limiting the memory footprint

Hyperspectral image classification from multiscale description with constrained connectivity and metric learning

International audienceMapping of remote sensing data is usually done through image classification. For hyperspectral images, the classification process often relies only on the spectral signature of each single pixel. Nevertheless, combining spatial and spectral features has been a promising way for accuracy improvement. We address here this problem by computing spectral features from spatially identified regions, sampled from a hierarchical image representation, namely α-tree, built with prior knowledge. The sampling of the tree nodes (i.e., regions) is based on the paradigm of constrained connectivity and the global range criterion. In this paper, we extend this criterion to hy-perspectral data and apply it to our knowledge-based α-tree. Our results show an improvement of pixelwise classification accuracy over spectral features only

Efficient Schemes for Computing α-tree Representations

International audienceHierarchical image representations have been addressed by various models by the past, the max-tree being probably its best representative within the scope of Mathematical Morphology. However, the max-tree model requires to impose an ordering relation between pixels, from the lowest values (root) to the highest (leaves). Recently, the α-tree model has been introduced to avoid such an ordering. Indeed, it relies on image quasi-flat zones, and as such focuses on local dissimilarities. It has led to successful attempts in remote sensing and video segmentation. In this paper, we deal with the problem of α-tree computation, and propose several efficient schemes which help to ensure real-time (or near-real time) morphological image processing

La stéganographie : une solution pour enrichir le contenu des vidéos numériques

National audienceAprès une présentation des concepts en sécurité de l'information, cet article montre par des illustrations que la stéganographie peut servir à la transmission d'informations non confidentielles. Nous présenterons ensuite quelques techniques stéganographiques adaptées au média vidéo. Dans ce contexte, la stéganographie présente l'avantage de conserver une compatibilité ascendante avec tous les lecteurs multimédia existants. Cependant nous montrerons que les solutions actuelles n'offrent pas une indépendance aux traitements pouvant être effectués sur les vidéos (telles les compressions)

GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference

International audienceThis paper presents GraphBPT, a tool for hierarchical representation of images based on binary partition trees. It relies on a new BPT construction algorithm that have interesting tuning properties. Besides, access to image pixels from the tree is achieved efficiently with data compression techniques, and a textual representation of BPT is also provided for interoperability. Finally, we illustrate how the proposed tool takes benefit from probabilistic inference techniques by empowering the BPT with its equivalent factor graph. The relevance of GraphBPT is illustrated in the context of image segmentation

Interoperability of multiscale visual representations for satellite image big data

International audienceIn this paper, we propose an interoperable solution for dealing with hierarchical representations of satellite images. Computationally intensive construction of the tree representation is performed on the server side, while the need for computational resources on the client side are greatly reduced (tree postprocessing or visualization). The proposed scheme is interoperable in the sense that it does not impose any constraints on the client environment, and API for C++, Java and Python languages are currently available. The communication is performed nodewise in a binary format using array structure for limiting the memory footprint

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

International audienceMonitoring flood is an important task for disaster management. It requires to distinguish between changes related to water from the other changes. We address such an issue by relying on both spatial and intensity information. To do so, we exploit min-tree that emphasize intensity extrema in a multiscale, efficient framework. We thus suggest a two-step approach operating on satellite image time series. We first perform a temporal analysis to identify images containing possible floods. Then a spatial analysis is achieved to detect flood areas on the selected images. Both steps relies on the analysis of component attributes extracted from the min-tree representation. We conduct some experiments on a flooded scene observed through Sentinel-1 SAR imagery. The results show that flood areas can be efficiently and accurately characterized with spatial component attributes extracted from hierarchical representations from SAR time series

Buffering hierarchical representation of color video streams for interactive object selection

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