Frédéric Precioso | University of Nice (original) (raw)

Papers by Frédéric Precioso

This paper deals with fast video segmentation using active contours. Region-based active contours... more This paper deals with fast video segmentation using active contours. Region-based active contours is a powerful technique for video segmentation. However most of these methods are implemented using level-sets. Although level-set methods provide accurate segmentation, they suffer from large computational cost. The proposed method uses B-Spline parametric method to highly improve the computation cost. Our method removes irregular sampling assumption. It combines multi-resolution regular sampling and length penalty.

Lecture Notes in Computer Science, 2011

This paper describes a novel method for shape detection and image segmentation. The proposed meth... more This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability

Lecture Notes in Computer Science, 2014

Image Processing, IEEE International Conference, 2011

This paper describes a novel method for active contour segmentation based on foreground/backgroun... more This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (PDFs) describing foreground and background regions. The most common techniques use χ2, Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

In the past ten years, new powerful algorithms based on efficient data structures have been propo... more In the past ten years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm which provides approximate nearest neighbors in a Euclidean space with sub-linear complexity is probably the most popular, the Euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the Earth-Mover Distance and χ 2 -distance. In this paper, we present a new LSH scheme adapted to χ 2 -distance for approximate nearest neighbors search in high-dimensional spaces. We define the specific hashing functions, we prove their local-sensitivity and compare, through experiments, our method with the Euclidean Locality Sensitive Hashing algorithm in the context of image retrieval on real image databases. The results prove the relevance of such a new LSH scheme either providing far better accuracy in the context of image retrieval than Euclidean scheme for an equivalent speed, or providing an equivalent accuracy but with a high gain in terms of processing speed.

Medical Image Computing and Computer-Assisted Intervention, 2010

Active contour methods are often methods of choice for demanding segmentation problems, yet segme... more Active contour methods are often methods of choice for demanding segmentation problems, yet segmentation of medical images with complex intensity patterns still remains a challenge for these methods. This paper proposes a method to incorporate interactively specified foreground/background regions into the active model framework while keeping the user interaction to the minimum. To achieve that, the proposed functional to be minimized includes a term to encourage active contour to separate the points close to the specified foreground region from the points close to the specified background region in terms of geodesic distance. The experiments on multi-modal prostate images demonstrate that the proposed method not only can achieve robust and accurate results, but also provides an efficient way to interactively improve the results.

International Conference on Computer Vision, 2011

Text detection and recognition in real images taken in unconstrained environments, such as street... more Text detection and recognition in real images taken in unconstrained environments, such as street view images, remain surprisingly challenging in Computer Vision. In this paper, we present a comprehensive strategy combining bottom-up and top-down mechanisms to detect Text boxes. The bottom-up part is based on character segmentation and grouping . The top-down part is achieved with a statistical learning approach based on box descriptors. Our main contribution consists in introducing a new descriptor, Fuzzy HOG (F-HOG), fully adapted for text box analysis. A thorough experimental validation proves the efficiency of the whole system outperforming state of the art results on the standard ICDAR text detection benchmark. Another contribution concerns the exploitation of our text extraction in a complete search engine scheme. We propose to retrieve a location from a textual query: combining our text box detection technology with OCR on georeferenced street images, we achieved a GIS system with a fully automatic textual indexing. We demonstrate the relevance of our system on the real urban database of [10].

Applied Optics, 2004

This synthetic paper deals with image and sequence segmentation when looking at the segmentation ... more This synthetic paper deals with image and sequence segmentation when looking at the segmentation task from a criterion optimization point of view. Such a segmentation criterion involves so-called (boundary and region) descriptors which, in the general case, may depend on their respective boundary or region. This dependency must be taken into account when computing the criterion derivative with respect to the unknown object domain (defined by its boundary). If not, some correctional terms may be omitted. This article focuses computing the derivative of the segmentation criterion using a dynamic scheme. The presented scheme is general enough to provide a framework for a wide variety of applications in segmentation. It also provides a theoretical meaning to the active contour philosophy.

Image Processing, IEEE International Conference, 2009

The development of street-level geoviewers become recently a very active and challenging research... more The development of street-level geoviewers become recently a very active and challenging research topic. In this context, the detection, representation and classification of windows can be beneficial for the identification of the respective facade. In this paper, a novel method for windows and facade retrieval is presented. This method, based on a similarity of graph of contours, introduces a new kernel on graph for inexact graph matching. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside a structure set, as well as spatial proximity. Then we are able to extract a window as a sub-graph of the graph of all contours of the facade image and to retrieve similar windows from a database of images of facades.

Scandinavian Conference on Image Analysis, 2009

In the past few years, street-level geoviewers has become a very popular web-application. In this... more In the past few years, street-level geoviewers has become a very popular web-application. In this paper, we focus on a first urban concept which has been identified as useful for indexing then retrieving a building or a location in a city: the windows. The work can be divided into three successive processes: first, object detection, then object characterization, finally similarity function design (kernel design). Contours seem intuitively relevant to hold architecture information from building facades. We first provide a robust window detector for our unconstrained data, present some results and compare our method with the reference one. Then, we represent objects by fragments of contours and a relational graph on these contour fragments. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside the structure set as well as spatial proximity. One difficulty to evaluate the relevance of our approach is that there is no reference database available. We made, thus, our own dataset. The results are quite encouraging regarding what was expected and what provide methods the literature.

The first step for video-content analysis, content-based video browsing and retrieval is the part... more The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it cap- tures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content anal- ysis,

International Conference on Image Processing, 2002

Image Processing, IEEE International Conference, 2001

Video segmentation is among the most important challenges of video processing and compression (MP... more Video segmentation is among the most important challenges of video processing and compression (MPEG-4 and MPEG-7). A drawback of classical methods is the computational cost due to the model complexity. In this paper we propose to use a B-Spline parametric contour to implement a region-based active contour segmentation. Hence we get a fast variational method based on active contours with an intrinsic regularizing constraint. More precisely the evolution force follows from the minimization of a region-based criterion. The theory of B-Splines allows analytical computation of the contour curvature at any point. The model complexity is fixed and depends on the desired level of detail. This complexity is highly compared to non parametric methods. We compare our new approach to classical parametric polygon-based methods. We show experiments on real video sequences.

The first step for video-content analysis, content-based video browsing and retrieval is the part... more The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it captures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content analysis, indexing and classification. Although many video shot boundary detection algorithms have been proposed in the literature, in most approaches, several parameters and thresholds have to be set in order to achieve good results. In this paper, we present a robust learning detector of sharp cuts without any threshold to set nor any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classifier method.

Image Processing, IEEE International Conference, 2009

This paper presents an actor video retrieval system based on face video-tubes extraction and repr... more This paper presents an actor video retrieval system based on face video-tubes extraction and representation with sets of temporally coherent features. Visual features, SIFT points, are tracked along a video shot, resulting in sets of feature point chains (spatio-temporal tubes). These tubes are then classified and retrieved using a kernel-based SVM learning framework for actor retrieval in a movie. In this paper, we present optimized feature tubes, we extend our feature representation with spatial location of SIFT points and we describe the new Spatio-Temporal Tube Kernel (STTK) of our contentbased retrieval system. Our approach has been tested on a real movie and proved to be faster and more robust for actor retrieval task.

2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, 2007

Video shot boundary detection plays an important role in video processing. It is the first step t... more Video shot boundary detection plays an important role in video processing. It is the first step toward videocontent analysis and content-based video retrieval. We develop a hierarchical approach for shot boundary detection based on the assumption that hierarchy helps to take decisions by reducing the amount of indeterminate transitions. Our method consists in first detecting abrupt transitions using a learning-based approach, then non-abrupt transitions are split into gradual transitions and normal frames. We describe in this paper, a machine learning system for shot boundary detection. The core of this system is a kernel-based SVM classifier. We present some results obtained for shot extraction TRECVID 2006 Task.

2011 18th IEEE International Conference on Image Processing, 2011

The paper reports on a novel method for reconstruction of cellular features including cell nuclei... more The paper reports on a novel method for reconstruction of cellular features including cell nuclei and cellular boundaries from actin tagged fluorescence confocal microscopy images. Such reconstruction can provide spatial context for subsequent quantitative analysis of changes to actin organisation and cell morphology in both controlled and stressed cell cultures. The proposed method is fully automatic and is formulated within

2011 18th IEEE International Conference on Image Processing, 2011

This paper describes a novel method for active contour segmentation based on foreground/backgroun... more This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (pdf) describing foreground and background regions. The most common techniques use χ 2 , Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.

2014 IEEE International Conference on Image Processing (ICIP), 2014

This article deals with statistical region-based active contour segmentation using the alpha-dive... more This article deals with statistical region-based active contour segmentation using the alpha-divergence family as similarity measure between the density probability functions of the background and the object regions of interest. Following previous publications on that topic, main originality of this contribution is in the proposed joint optimization of the energy steering the evolution of the active curve and the parameter alpha related to the metric of the divergence and closely related to the statistical luminance distribution of the data. Experiments are shown on both synthetic noisy and textured data as well as on real images (natural and medical ones). We show that the joint optimization process leads to satisfying results for every targeted tasks: above all, it is shown that the proposed approach overcome classic statistical-based region active contour approach using Kullback-Leibler divergence as similarity measure, that can stuck in local extrema during the usual optimization process.

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012

In this article, a complete original framework for non supervised statistical region based active... more In this article, a complete original framework for non supervised statistical region based active contour segmentation is proposed. More precisely, the method is based on the maximization of alphadivergences between non paramterically estimated probability density functions (PDF) of the inner and outer regions defined by the evolving curve. In this paper, we define the variational context associated to distance maximization in the particular case of alphadivergence and we also provide the complete derivation of the partial differential equation leading the segmentation. Results on synthetic data (corrupted with a high level of Gaussian and Poisonian noises) but also on clinical images (X-ray images) show that the proposed non supervised approach improves classical approach of that kind.

This paper deals with fast video segmentation using active contours. Region-based active contours... more This paper deals with fast video segmentation using active contours. Region-based active contours is a powerful technique for video segmentation. However most of these methods are implemented using level-sets. Although level-set methods provide accurate segmentation, they suffer from large computational cost. The proposed method uses B-Spline parametric method to highly improve the computation cost. Our method removes irregular sampling assumption. It combines multi-resolution regular sampling and length penalty.

Lecture Notes in Computer Science, 2011

This paper describes a novel method for shape detection and image segmentation. The proposed meth... more This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability

Lecture Notes in Computer Science, 2014

Image Processing, IEEE International Conference, 2011

This paper describes a novel method for active contour segmentation based on foreground/backgroun... more This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (PDFs) describing foreground and background regions. The most common techniques use χ2, Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012

In the past ten years, new powerful algorithms based on efficient data structures have been propo... more In the past ten years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm which provides approximate nearest neighbors in a Euclidean space with sub-linear complexity is probably the most popular, the Euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the Earth-Mover Distance and χ 2 -distance. In this paper, we present a new LSH scheme adapted to χ 2 -distance for approximate nearest neighbors search in high-dimensional spaces. We define the specific hashing functions, we prove their local-sensitivity and compare, through experiments, our method with the Euclidean Locality Sensitive Hashing algorithm in the context of image retrieval on real image databases. The results prove the relevance of such a new LSH scheme either providing far better accuracy in the context of image retrieval than Euclidean scheme for an equivalent speed, or providing an equivalent accuracy but with a high gain in terms of processing speed.

Medical Image Computing and Computer-Assisted Intervention, 2010

Active contour methods are often methods of choice for demanding segmentation problems, yet segme... more Active contour methods are often methods of choice for demanding segmentation problems, yet segmentation of medical images with complex intensity patterns still remains a challenge for these methods. This paper proposes a method to incorporate interactively specified foreground/background regions into the active model framework while keeping the user interaction to the minimum. To achieve that, the proposed functional to be minimized includes a term to encourage active contour to separate the points close to the specified foreground region from the points close to the specified background region in terms of geodesic distance. The experiments on multi-modal prostate images demonstrate that the proposed method not only can achieve robust and accurate results, but also provides an efficient way to interactively improve the results.

International Conference on Computer Vision, 2011

Text detection and recognition in real images taken in unconstrained environments, such as street... more Text detection and recognition in real images taken in unconstrained environments, such as street view images, remain surprisingly challenging in Computer Vision. In this paper, we present a comprehensive strategy combining bottom-up and top-down mechanisms to detect Text boxes. The bottom-up part is based on character segmentation and grouping . The top-down part is achieved with a statistical learning approach based on box descriptors. Our main contribution consists in introducing a new descriptor, Fuzzy HOG (F-HOG), fully adapted for text box analysis. A thorough experimental validation proves the efficiency of the whole system outperforming state of the art results on the standard ICDAR text detection benchmark. Another contribution concerns the exploitation of our text extraction in a complete search engine scheme. We propose to retrieve a location from a textual query: combining our text box detection technology with OCR on georeferenced street images, we achieved a GIS system with a fully automatic textual indexing. We demonstrate the relevance of our system on the real urban database of [10].

Applied Optics, 2004

This synthetic paper deals with image and sequence segmentation when looking at the segmentation ... more This synthetic paper deals with image and sequence segmentation when looking at the segmentation task from a criterion optimization point of view. Such a segmentation criterion involves so-called (boundary and region) descriptors which, in the general case, may depend on their respective boundary or region. This dependency must be taken into account when computing the criterion derivative with respect to the unknown object domain (defined by its boundary). If not, some correctional terms may be omitted. This article focuses computing the derivative of the segmentation criterion using a dynamic scheme. The presented scheme is general enough to provide a framework for a wide variety of applications in segmentation. It also provides a theoretical meaning to the active contour philosophy.

Image Processing, IEEE International Conference, 2009

The development of street-level geoviewers become recently a very active and challenging research... more The development of street-level geoviewers become recently a very active and challenging research topic. In this context, the detection, representation and classification of windows can be beneficial for the identification of the respective facade. In this paper, a novel method for windows and facade retrieval is presented. This method, based on a similarity of graph of contours, introduces a new kernel on graph for inexact graph matching. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside a structure set, as well as spatial proximity. Then we are able to extract a window as a sub-graph of the graph of all contours of the facade image and to retrieve similar windows from a database of images of facades.

Scandinavian Conference on Image Analysis, 2009

In the past few years, street-level geoviewers has become a very popular web-application. In this... more In the past few years, street-level geoviewers has become a very popular web-application. In this paper, we focus on a first urban concept which has been identified as useful for indexing then retrieving a building or a location in a city: the windows. The work can be divided into three successive processes: first, object detection, then object characterization, finally similarity function design (kernel design). Contours seem intuitively relevant to hold architecture information from building facades. We first provide a robust window detector for our unconstrained data, present some results and compare our method with the reference one. Then, we represent objects by fragments of contours and a relational graph on these contour fragments. We design a kernel similarity function for structured sets of contours which will take into account the variations of contour orientation inside the structure set as well as spatial proximity. One difficulty to evaluate the relevance of our approach is that there is no reference database available. We made, thus, our own dataset. The results are quite encouraging regarding what was expected and what provide methods the literature.

The first step for video-content analysis, content-based video browsing and retrieval is the part... more The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it cap- tures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content anal- ysis,

International Conference on Image Processing, 2002

Image Processing, IEEE International Conference, 2001

Video segmentation is among the most important challenges of video processing and compression (MP... more Video segmentation is among the most important challenges of video processing and compression (MPEG-4 and MPEG-7). A drawback of classical methods is the computational cost due to the model complexity. In this paper we propose to use a B-Spline parametric contour to implement a region-based active contour segmentation. Hence we get a fast variational method based on active contours with an intrinsic regularizing constraint. More precisely the evolution force follows from the minimization of a region-based criterion. The theory of B-Splines allows analytical computation of the contour curvature at any point. The model complexity is fixed and depends on the desired level of detail. This complexity is highly compared to non parametric methods. We compare our new approach to classical parametric polygon-based methods. We show experiments on real video sequences.

The first step for video-content analysis, content-based video browsing and retrieval is the part... more The first step for video-content analysis, content-based video browsing and retrieval is the partitioning of a video sequence into shots. A shot is the fundamental unit of a video, it captures a continuous action from a single camera and represents a spatio-temporally coherent sequence of frames. Thus, shots are considered as the primitives for higher level content analysis, indexing and classification. Although many video shot boundary detection algorithms have been proposed in the literature, in most approaches, several parameters and thresholds have to be set in order to achieve good results. In this paper, we present a robust learning detector of sharp cuts without any threshold to set nor any pre-processing step to compensate motion or post-processing filtering to eliminate false detected transitions. The experiments, following strictly the TRECVID 2002 competition protocol, provide very good results dealing with a large amount of features thanks to our kernel-based SVM classifier method.

Image Processing, IEEE International Conference, 2009

This paper presents an actor video retrieval system based on face video-tubes extraction and repr... more This paper presents an actor video retrieval system based on face video-tubes extraction and representation with sets of temporally coherent features. Visual features, SIFT points, are tracked along a video shot, resulting in sets of feature point chains (spatio-temporal tubes). These tubes are then classified and retrieved using a kernel-based SVM learning framework for actor retrieval in a movie. In this paper, we present optimized feature tubes, we extend our feature representation with spatial location of SIFT points and we describe the new Spatio-Temporal Tube Kernel (STTK) of our contentbased retrieval system. Our approach has been tested on a real movie and proved to be faster and more robust for actor retrieval task.

2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services, 2007

Video shot boundary detection plays an important role in video processing. It is the first step t... more Video shot boundary detection plays an important role in video processing. It is the first step toward videocontent analysis and content-based video retrieval. We develop a hierarchical approach for shot boundary detection based on the assumption that hierarchy helps to take decisions by reducing the amount of indeterminate transitions. Our method consists in first detecting abrupt transitions using a learning-based approach, then non-abrupt transitions are split into gradual transitions and normal frames. We describe in this paper, a machine learning system for shot boundary detection. The core of this system is a kernel-based SVM classifier. We present some results obtained for shot extraction TRECVID 2006 Task.

2011 18th IEEE International Conference on Image Processing, 2011

The paper reports on a novel method for reconstruction of cellular features including cell nuclei... more The paper reports on a novel method for reconstruction of cellular features including cell nuclei and cellular boundaries from actin tagged fluorescence confocal microscopy images. Such reconstruction can provide spatial context for subsequent quantitative analysis of changes to actin organisation and cell morphology in both controlled and stressed cell cultures. The proposed method is fully automatic and is formulated within

2011 18th IEEE International Conference on Image Processing, 2011

This paper describes a novel method for active contour segmentation based on foreground/backgroun... more This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (pdf) describing foreground and background regions. The most common techniques use χ 2 , Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.

2014 IEEE International Conference on Image Processing (ICIP), 2014

This article deals with statistical region-based active contour segmentation using the alpha-dive... more This article deals with statistical region-based active contour segmentation using the alpha-divergence family as similarity measure between the density probability functions of the background and the object regions of interest. Following previous publications on that topic, main originality of this contribution is in the proposed joint optimization of the energy steering the evolution of the active curve and the parameter alpha related to the metric of the divergence and closely related to the statistical luminance distribution of the data. Experiments are shown on both synthetic noisy and textured data as well as on real images (natural and medical ones). We show that the joint optimization process leads to satisfying results for every targeted tasks: above all, it is shown that the proposed approach overcome classic statistical-based region active contour approach using Kullback-Leibler divergence as similarity measure, that can stuck in local extrema during the usual optimization process.

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012

In this article, a complete original framework for non supervised statistical region based active... more In this article, a complete original framework for non supervised statistical region based active contour segmentation is proposed. More precisely, the method is based on the maximization of alphadivergences between non paramterically estimated probability density functions (PDF) of the inner and outer regions defined by the evolving curve. In this paper, we define the variational context associated to distance maximization in the particular case of alphadivergence and we also provide the complete derivation of the partial differential equation leading the segmentation. Results on synthetic data (corrupted with a high level of Gaussian and Poisonian noises) but also on clinical images (X-ray images) show that the proposed non supervised approach improves classical approach of that kind.