Romaric Audigier

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This paper analyzes the robustness issue in three segmentation approaches: the iterative relative fuzzy object extraction, the watershed transforms (WT) by image foresting transform and by minimum spanning forest. These methods need input seeds, which can be source of variability in the segmentation result. So, the robustness of these segmentation methods(More)
This paper makes a rereading of two successful image segmentation approaches, the fuzzy connectedness (FC) and the watershed (WS) approaches, by analyzing both by means of the image foresting transform (IFT). This graph-based transform provides a sound framework for analyzing and implementing these methods. This paradigm allows to show the duality existing(More)
To better understand the numerous solutions related to watershed transform (WT), this paper shows the relationships between some discrete definitions of the WT: the watersheds based on image foresting transform (IFT), topographic distance (TD), local condition (LC), and minimum spanning forest (MSF). We demonstrate that the tie-zone (TZ) concept, that(More)
Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object(More)
In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (e.g. organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user's actions for(More)
In a recent paper [1], a new type of watershed (WS) transform was introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique (and thereby unbiased) optimal solution. Indeed, many optimal solutions are sometimes possible when segmenting an image by WS. The TZWS assigns(More)
There are many watershed transform algorithms in literature but most of them do not exactly correspond to their respective definition. The solution given by such algorithms depends on their implementation. Others fit with their definition which allows multiple solutions. The solution chosen by such algorithms depends on their implementation too. It is the(More)
In this work, a new type of watershed transform is introduced: the tie-zone watershed (TZWS). This region-based watershed transform does not depend on arbitrary implementation and provides a unique and optimal solution. Indeed, many solutions are sometimes possible when segmenting an image with a watershed algorithm. In this case, the TZWS assigns each(More)
Multi-person tracking is still a challenging problem due to recurrent occlusion, pose variation and similar appearances between people. Inspired by the success of sparse representations in single object tracking and face recognition, we propose in this paper an online tracking by detection framework based on collaborative sparse representations. We argue(More)