Jonathan Fabrizio

How to compute the convex hull of a binary shape? A real-time algorithm to compute the convex hull of a binary shape

By Jonathan Fabrizio

2023-09-13

In Journal of Real-Time Image Processing volume

Abstract In this article, we present an algorithm to compute the convex hull of a binary shape. Efficient algorithms to compute the convex hull of a set of points had been proposed long time ago. For a binary shape, the common practice is to rely on one of them: to compute the convex hull of binary shape, all pixels of the shape are first listed, and then the convex hull is computed on this list of points.

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The Dahu graph-cut for interactive segmentation on 2D/3D images

By Minh Ôn Vũ Ngọc, Edwin Carlinet, Jonathan Fabrizio, Thierry Géraud

2022-12-03

In Pattern Recognition

Abstract Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images.

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Topology-aware method to segment 3D plan tissue images

By Minh Ôn Vũ Ngọc, Nicolas Boutry, Jonathan Fabrizio

2022-10-25

In 36th conference on neural information processing systems, AI for science workshop

Abstract The study of genetic and molecular mechanisms underlying tissue morphogenesis has received a lot of attention in biology. Especially, accurate segmentation of tissues into individual cells plays an important role for quantitative analyzing the development of the growing organs. However, instance cell segmentation is still a challenging task due to the quality of the image and the fine-scale structure. Any small leakage in the boundary prediction can merge different cells together, thereby damaging the global structure of the image.

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Introducing the boundary-aware loss for deep image segmentation

By Minh Ôn Vũ Ngọc, Yizi Chen, Nicolas Boutry, Joseph Chazalon, Edwin Carlinet, Jonathan Fabrizio, Clément Mallet, Thierry Géraud

2021-11-28

In Proceedings of the 32nd british machine vision conference (BMVC)

Abstract Most contemporary supervised image segmentation methods do not preserve the initial topology of the given input (like the closeness of the contours). One can generally remark that edge points have been inserted or removed when the binary prediction and the ground truth are compared. This can be critical when accurate localization of multiple interconnected objects is required. In this paper, we present a new loss function, called, Boundary-Aware loss (BALoss), based on the Minimum Barrier Distance (MBD) cut algorithm.

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A new minimum barrier distance for multivariate images with applications to salient object detection, shortest path finding, and segmentation

By Minh Ôn Vũ Ngọc, Nicolas Boutry, Jonathan Fabrizio, Thierry Géraud

2020-06-02

In Computer Vision and Image Understanding

Abstract Distance transforms and the saliency maps they induce are widely used in image processing, computer vision, and pattern recognition. One of the most commonly used distance transform is the geodesic one. Unfortunately, this distance does not always achieve satisfying results on noisy or blurred images. Recently, a new (pseudo-)distance, called the minimum barrier distance (MBD), more robust to pixel variations, has been introduced. Some years after, Géraud et al. have proposed a good and fast-to compute approximation of this distance: the Dahu pseudo-distance.

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Segmentation of gliomas and prediction of patient overall survival: A simple and fast procedure

By Élodie Puybareau, Guillaume Tochon, Joseph Chazalon, Jonathan Fabrizio

2018-11-05

In Proceedings of the workshop on brain lesions (BrainLes), in conjunction with MICCAI

Abstract In this paper, we propose a fast automatic method that seg- ments glioma without any manual assistance, using a fully convolutional network (FCN) and transfer learning. From this segmentation, we predict the patient overall survival using only the results of the segmentation and a home made atlas. The FCN is the base network of VGG-16, pretrained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 BraTS Challenge.

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Document detection in videos captured by smartphones using a saliency-based method

By Minh Ôn Vũ Ngọc, Jonathan Fabrizio, Thierry Géraud

2018-09-20

In International conference on document analysis and recognition workshops (ICDARW)

Abstract Smartphones are now widely used to digitizepaper documents. Document detection is the first importantstep of the digitization process. Whereas many methods extractlines from contours as candidates for the document boundary, we present in this paper a region-based approach. A key feature of our method is that it relies on visual saliency, using a recent distance existing in mathematical morphology. We show that the performance of our method is competitive with state-of-the-art methods on the ICDAR Smartdoc 2015 Competition dataset.

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A first step toward a fair comparison of evaluation protocols for text detection algorithms

By Aliona Dangla, Élodie Puybareau, Guillaume Tochon, Jonathan Fabrizio

2018-02-02

In Proceedings of the IAPR international workshop on document analysis systems (DAS)

Abstract Text detection is an important topic in pattern recognition, but evaluating the reliability of such detection algorithms is challenging. While many evaluation protocols have been developed for that purpose, they often show dissimilar behaviors when applied in the same context. As a consequence, their usage may lead to misinterpretations, potentially yielding erroneous comparisons between detection algorithms or their incorrect parameters tuning. This paper is a first attempt to derive a methodology to perform the comparison of evaluation protocols.

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Saliency-based detection of identity documents captured by smartphones

By Minh Ôn Vũ Ngọc, Jonathan Fabrizio, Thierry Géraud

2018-02-02

In Proceedings of the IAPR international workshop on document analysis systems (DAS)

Abstract Smartphones have became an easy and convenient mean to acquire documents. In this paper, we focus on the automatic segmentation of identity documents in smartphone photos or videos using visual saliency (VS). VS-based approaches, which pertain to computer vision, have not be considered yet for this particular task. Here we compare different VS methods, and we propose a new VS scheme, based on a recent distance belonging to the scope of mathematical morphology.

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From text detection to text segmentation: A unified evaluation scheme

By Stefania Calarasanu, Jonathan Fabrizio, Séverine Dubuisson

2016-10-01

In Proceedings of the 2nd international workshop on robust reading conference (IWRR-ECCV)

Abstract Current text segmentation evaluation protocols are often incapable of properly handling different scenarios (broken/merged/partial characters). This leads to scores that incorrectly reflect the segmentation accuracy. In this article we propose a new evaluation scheme that overcomes most of the existent drawbacks by extending the EvaLTex protocol (initially designed to evaluate text detection at region level). This new unified platform has numerous advantages: it is able to evaluate a text understanding system at every detection stage and granularity level (paragraph/line/word and now character) by using the same metrics and matching rules; it is robust to all segmentation scenarios; it provides a qualitative and quantitative evaluation and a visual score representation that captures the whole behavior of a segmentation algorithm.

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