Current trends in image segmentation are to compute a hierarchy of image segmentations from fine to coarse. A classical approach to obtain a single meaningful image partition from a given hierarchy is to cut it in an optimal way, following the seminal approach of the scale-set theory. While interesting in many cases, the resulting segmentation, being a non-horizontal cut, is limited by the structure of the hierarchy. In this paper, we propose a novel approach that acts by transforming an input hierarchy into a new saliency map. It relies on the notion of shape space: a graph representation of a set of regions extracted from the image. Each region is characterized with an attribute describing it. We weigh the boundaries of a subset of meaningful regions (local minima) in the shape space by extinction values based on the attribute. This extinction-based saliency map represents a new hierarchy of segmentations highlighting regions having some specific characteristics. Each threshold of this map represents a segmentation which is generally different from any cut of the original hierarchy. This new approach thus enlarges the set of possible partition results that can be extracted from a given hierarchy. Qualitative and quantitative illustrations demonstrate the usefulness of the proposed method.

This paper presents a hybrid partitioning method used to improve the performance of solving a Satisfiability (SAT) problems. The principle of our approach consist firstly to apply a static partitioning to decompose the search tree in finite set of disjoint sub-trees, than assign each sub-tree to one computing core. However it is not easy to choose the relevant branching variables to partition the search tree. We propose in this context to partition the search tree according to the variables that occur more frequently then others. The advantage of this method is that it gives a good disjoint sub- trees. However, the drawback is the imbalance load between all computing cores of the system. To overcome this drawback, we propose as novelty to extend the static partitioning by combining with a new dynamic partitioning that assure a good load balancing between cores. Each time a new waiting core is detected, the dynamic partitioning selects automatically using an estimation function the computing core which has the most work to do in order to partition dynamically its sub-tree in two parts. It keeps one part and gives the second part to the waiting core. Preliminary result show that a good speedup is achieved using our hybrid method.

Spectral Umixing (SU) in hyperspectral remote sensing aims at recovering the signatures of the pure materials in the scene (endmembers) and their abundances in each pixel of the image. The usual SU chain does not take spectral variability (SV) into account, and relies on the estimation of the Intrinsic Dimensionality (ID) of the data, related to the number of endmembers (NOE) to use. However, the ID can be significantly overestimated in difficult scenarios, and sometimes does not correspond to the desired scale and application dependent NOE. Spurious endmembers are then frequently extracted and included in the model. We propose an algorithm for SU incorporating SV, using collaborative sparsity to discard the least explicative endmembers in the whole image. We compute an algorithmic regularization path for this problem to select the optimal set of endmembers using a statistical criterion. Results on simulated and real data show the interest of the approach.

Rational expressions are powerful tools to define automata, but often restricted to single-tape automata. Our goal is to unleash their expressive power for transducers, and more generally, any multitape automaton; for instance $(a^+\mathbin{\vert} x + b^+\mathbin{\vert} y)^*$. We generalize the construction of the derived-term automaton by using <i>expansions</i>. This approach generates small automata, and even allows us to support a composition operator.

In this article we demonstrate 4 algorithms for calculating the maximal disjoint decomposition of a given set of types. We discuss some advantages and disadvantages of each, and compare their performance. We extended currently known work to describe an efficient algorithm for manipulating binary decision diagrams representing types in a programming language which supports subtyping viewed as subsets.

Digitization of the real world using real sensors has many drawbacks; in particular, we loose “well-composedness” in the sense that two digitized objects can be connected or not depending on the connectivity we choose in the digital image, leading then to ambiguities. Furthermore, digitized images are arrays of numerical values, and then do not own any topology by nature, contrary to our usual modeling of the real world in mathematics and in physics. Loosing all these properties makes difficult the development of algorithms which are “topologically correct” in image processing: e.g., the computation of the tree of shapes needs the representation of a given image to be continuous and well-composed; in the contrary case, we can obtain abnormalities in the final result. Some well-composed continuous representations already exist, but they are not in the same time $n$-dimensional and self-dual. In fact, $n$-dimensionality is crucial since usual signals are more and more 3-dimensional (like 2D videos) or 4-dimensional (like 4D Computerized Tomography-scans), and self-duality is necessary when a same image can contain different objects with different contrasts. We developed then a new way to make images well-composed by interpolation in a self-dual way and in $n$-D; followed with a span-based immersion, this interpolation becomes a self-dual continuous well-composed representation of the initial $n$-D signal. This representation benefits from many strong topological properties: it verifies the intermediate value theorem, the boundaries of any threshold set of the representation are disjoint union of discrete surfaces, and so on.

Many text segmentation methods are elaborate and thus are not suitable to real-time implementation on mobile devices. Having an efficient and effective method, robust to noise, blur, or uneven illumination, is interesting due to the increasing number of mobile applications needing text extraction. We propose a hierarchical image representation, based on the morphological Laplace operator, which is used to give a robust text segmentation. This representation relies on several very sound theoretical tools; its computation eventually translates to a simple labeling algorithm, and for text segmentation and grouping, to an easy tree-based processing. We also show that this method can also be applied to document binarization, with the interesting feature of getting also reverse-video text.

This document presents the system submission for the group composed of MIT Lincoln Laboratory, Johns Hopkins University (JHU), Laboratoire de Recherche et de Développement de l’EPITA (LRDE) and Universidad Autónoma de Madrid (ATVS). The primary submission is a combination of four systems focused on i-vector systems. Two secondary submissions are also included

Given a directed acyclic graph (DAG) and two arbitrary nodes, find maximal common joins of the two nodes. In this technical report I suggest an algorithm for efficiently calculating the minimal set of nodes which derive from a pair of nodes.