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146
Regionbased representations of image and video: segmentation tools for multimedia services.
 IEEE Trans. Circuits and Systems for Video Technology,
, 1999
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Scalesets image analysis
 International Journal of Computer Vision
, 2006
"... This paper introduces a multiscale theory of piecewise image modelling, called the scalesets theory, and which can be regarded as a regionoriented scalespace theory. The first part of the paper studies the general structure of a geometrically unbiased regionoriented multiscale image descriptio ..."
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Cited by 52 (4 self)
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This paper introduces a multiscale theory of piecewise image modelling, called the scalesets theory, and which can be regarded as a regionoriented scalespace theory. The first part of the paper studies the general structure of a geometrically unbiased regionoriented multiscale image description and introduces the scalesets representation, a representation which allows to handle such a description exactly. The second part of the paper deals with the way scalesets image analyses can be built according to an energy minimization principle. We consider a rather general formulation of the partitioning problem which involves minimizing a twotermbased energy, of the form λC+D, where D is a goodnessoffit term and C is a regularization term. We describe the way such energies arise from basic principles of approximate modelling and we relate them to operational rate/distorsion problems involved in lossy compression problems. We then show that an important subset of these energies constitutes a class of multiscale energies in that the minimal cut of a hierarchy gets coarser and coarser as parameter λ increases. This allows us to devise a fast dynamicprogramming procedure to find the complete scalesets representation of this family of minimal cuts. Considering then the construction of the hierarchy from which the minimal cuts are extracted, we end up with an exact and parameterfree
Extracting Meaningful Curves From Images
 JOURNAL OF MATHEMATICAL IMAGING AND VISION
, 2003
"... Since the beginning, Mathematical Morphology has proposed to extract shapes from images as connected components of level sets. These methods have proved very efficient in shape recognition and shape analysis. In this paper, we present an improved method to select the most meaningful level lines (bou ..."
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Cited by 40 (8 self)
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Since the beginning, Mathematical Morphology has proposed to extract shapes from images as connected components of level sets. These methods have proved very efficient in shape recognition and shape analysis. In this paper, we present an improved method to select the most meaningful level lines (boundaries of level sets) from an image. This extraction can be based on statistical arguments, leading to a parameter free algorithm. It permits to roughly extract all pieces of level lines of an image, that coincide with pieces of edges. By this method, the number of encoded level lines is reduced by a factor 100, without any loss of shape contents. In contrast to edge detections algorithm or snakes methods, such a level lines selection method delivers accurate shape elements, without user parameter: no smoothing involved and selection parameters can be computed by Helmholtz Principle.
Region merging techniques using information theory statistical measures
 IEEE TRANS IMAGE PROCESS
, 2010
"... The purpose of the current work is to propose, under a statistical framework, a family of unsupervised region merging techniques providing a set of the most relevant regionbased explanations of an image at different levels of analysis. These techniques are characterized by general and nonparametr ..."
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Cited by 28 (1 self)
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The purpose of the current work is to propose, under a statistical framework, a family of unsupervised region merging techniques providing a set of the most relevant regionbased explanations of an image at different levels of analysis. These techniques are characterized by general and nonparametric region models, with neither color nor texture homogeneity assumptions, and a set of innovative merging criteria, based on information theory statistical measures. The scale consistency of the partitions is assured through i) a size regularization term into the merging criteria and a classical merging order, or ii) using a novel scalebased merging order to avoid the region size homogeneity imposed by the use of a size regularization term. Moreover, a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy. Most significant automatically extracted partitions show the ability to represent the semantic content of the image from a human point of view. Finally, a complete and exhaustive evaluation of the proposed techniques is performed, using not only different databases for the two main addressed problems (objectoriented segmentation of generic images and texture image segmentation), but also specific evaluation features in each case: under and oversegmentation error, and a large set of regionbased, pixelbased and error consistency indicators, respectively. Results are promising, outperforming in most indicators both objectoriented and texture stateoftheart segmentation techniques.
Color LAR codec: a color image representation and compression scheme based on local resolution adjustment and selfextracting region representation
 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
, 2007
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N.E.: A comparative evaluation of interactive segmentation algorithms
 Pattern Recognition
, 2010
"... In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of userexperiments, in which participants were tasked with extracting one hundred objects from a common dataset: twentyfive with each algorithm, constra ..."
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Cited by 26 (1 self)
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In this paper we present a comparative evaluation of four popular interactive segmentation algorithms. The evaluation was carried out as a series of userexperiments, in which participants were tasked with extracting one hundred objects from a common dataset: twentyfive with each algorithm, constrained within a time limit of two minutes for each object. To facilitate the experiments, a “scribbledriven ” segmentation tool was developed to enable interactive image segmentation by simply marking areas of foreground and background with the mouse. As the participants refined and improved their respective segmentations, the corresponding updated segmentation mask was stored along with the elapsed time. We then collected and evaluated each recorded mask against a manually segmented groundtruth, thus allowing us to gauge segmentation accuracy over time. Two benchmarks were used for the evaluation: the wellknown Jaccard index for measuring object accuracy, and a new fuzzy metric, proposed in this paper, designed for measuring boundary accuracy. Analysis of the experimental results demonstrates the effectiveness of the suggested measures and provides valuable insights into the performance and characteristics of the evaluated algorithms.
Maskbased second generation connectivity and attribute filters
 IEEE TRANS. PATTERN ANAL. MACH. INTELL
, 2007
"... Connected filters are edgepreserving morphological operators, which rely on a notion of connectivity. This is usually the standard 4 and 8connectivity, which is often too rigid since it cannot model generalized groupings such as object clusters or partitions. In the settheoretical framework of co ..."
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Cited by 25 (6 self)
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Connected filters are edgepreserving morphological operators, which rely on a notion of connectivity. This is usually the standard 4 and 8connectivity, which is often too rigid since it cannot model generalized groupings such as object clusters or partitions. In the settheoretical framework of connectivity, these groupings are modeled by the more general secondgeneration connectivity. In this paper, we present both an extension of this theory, and provide an efficient algorithm based on the MaxTree to compute attribute filters based on these connectivities. We first look into the drawbacks of the existing framework that separates clustering and partitioning and is directly dependent on the properties of a preselected operator. We then propose a new type of secondgeneration connectivity termed maskbased connectivity which eliminates all previous dependencies and extends the ways the image domain can be connected. A previously developed DualInput MaxTree algorithm for area openings is adapted for the wider class of attribute filters on images characterized by secondgeneration connectivity. CPUtimes for the new algorithm are comparable to the original algorithm, typically deviating less than 10 percent either way.
Adaptive multivariate approximation using binary space partitions and geometric wavelets
 SIAM Journal on Numerical Analysis, To Appear
, 2005
"... Abstract. The Binary Space Partition (BSP) technique is a simple and efficient method to adaptively partition an initial given domain to match the geometry of a given input function. As such the BSP technique has been widely used by practitioners, but up until now no rigorous mathematical justificat ..."
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Cited by 23 (1 self)
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Abstract. The Binary Space Partition (BSP) technique is a simple and efficient method to adaptively partition an initial given domain to match the geometry of a given input function. As such the BSP technique has been widely used by practitioners, but up until now no rigorous mathematical justification to it has been offered. Here we attempt to put the technique on sound mathematical foundations, and we offer an enhancement of the BSP algorithm in the spirit of what we are going to call geometric wavelets. This new approach to sparse geometric representation is based on recent development in the theory of multivariate nonlinear piecewise polynomial approximation. We provide numerical examples of nterm geometric wavelet approximations of known test images and compare them with dyadic wavelet approximation. We also discuss applications to image denoising and compression.
Connected operators
"... Connected operators are filtering tools that act by merging elementary regions called flat zones. Connecting operators cannot create new contours nor modify their position. Therefore, they have very good contourpreservation properties and are capable of both lowlevel filtering and higherlevel obj ..."
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Cited by 20 (2 self)
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Connected operators are filtering tools that act by merging elementary regions called flat zones. Connecting operators cannot create new contours nor modify their position. Therefore, they have very good contourpreservation properties and are capable of both lowlevel filtering and higherlevel object recognition. This article gives an overview on connected operators and their application to image and video filtering. There are two popular techniques used to create connected operators. The first one relies on a reconstruction process. The operator involves first a simplification step based on a “classical ” filter and then a reconstruction process. In fact, the reconstruction can be seen as a way to create a connected version of an arbitrary operator. The simplification effect is defined and limited by the first step. The examples we show include simplification in terms of size or contrast. The second strategy to define connected operators relies on a hierarchical regionbased representation of the input image, i.e., a tree, computed in an initial step. Then, the simplification is obtained by pruning the tree, and, third, the output image is constructed from the pruned tree. This article presents the most important trees that have been used to create connected operators and also discusses important families of simplification or pruning criteria. We also give a brief overview on efficient implementations of the reconstruction process and of tree construction. Finally, the [ Philippe Salembier and Michael H.F. Wilkinson] [A review of regionbased morphological image processing techniques]
P.: Binary partition trees for object detection
 IEEE Trans. on Image Processing
"... Abstract—This paper discusses the use of Binary Partition Trees (BPTs) for object detection. BPTs are hierarchical regionbased representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object d ..."
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Cited by 17 (5 self)
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Abstract—This paper discusses the use of Binary Partition Trees (BPTs) for object detection. BPTs are hierarchical regionbased representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the search space for the object detection task. Then we analyze and objectively compare various similarity measures for the tree construction. We conclude that different similarity criteria should be used for the part providing accuracy in the BPT and for the part defining the search space and specific criteria are proposed for each case. Then we discuss the object detection strategy based on BPT. The notion of node extension is proposed and discussed. Finally, several object detection examples illustrating the generality of the approach and its efficiency are reported. Index Terms—Binary partition tree, hierarchical representation, image region analysis, image representations, image segmentation, object detection. I.