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. The computed convex hull is finally rasterized to provide the final result as, for example, in the famous scikit-image library. To compute the convex hull of an arbitrary set of points, the points of the list that lie on the outline of the convex hull must be selected (to simplify, we call these points “extrema”). To find them, for an arbitrary set of points, it is necessary to browse all the points but not in the particular case of a binary shape. In this specific situation, the extrema necessarily belong to the inner boundary of the shape. It is a waste of time to browse all the pixels as it is possible to discard most of them when we search for these extrema. Based on this analysis, we propose a new method to compute the convex hull dedicated to binary shapes. This method browses as few pixels as possible to select a small subset of boundary pixels. Then it deduces the convex hull only from this subset. As the size of the subset is very small, the convex hull is computed in real time. We compare it with the commonly used methods and common functions from libraries to prove that our approach is faster. This comparison shows that, for a very small shape, the difference is acceptable, but when the area of the shape grows, this difference becomes significant. This leads us to conclude that substituting current functions to compute convex hull of binary shapes with our algorithm in frequently used libraries would lead to a great improvement.

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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. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each’s complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user’s efforts.

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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. In this paper, we propose an end-to-end topology-aware 3D segmentation method for plant tissues. The strength of the method is that it takes care of the 3D topology of segmented structures. The keystone is a training phase and a new topology-aware loss - the CavityLoss - that are able to help the network to focus on the topological errors to fix them during the learning phase. The evaluation of our method on both fixed and live plant organ datasets shows that our method outperforms state-of-the-art methods (and contrary to state-of-the-art methods, does not require any post-processing stage). The code of CavityLoss is freely available at https://github.com/onvungocminh/CavityLoss

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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. It is able to locate what we call the leakage pixels and to encode the boundary information coming from the given ground truth. Thanks to this adapted loss, we are able to significantly refine the quality of the predicted boundaries during the learning procedure. Furthermore, our loss function is differentiable and can be applied to any kind of neural network used in image processing. We apply this loss function on the standard U-Net and DC U-Net on Electron Microscopy datasets. They are well-known to be challenging due to their high noise level and to the close or even connected objects covering the image space. Our segmentation performance, in terms of Variation of Information (VOI) and Adapted Rank Index (ARI), are very promising and lead to $\approx{}15%$ better scores of VOI and $\approx{}5%$ better scores of ARI than the state-of-the-art. The code of boundary-awareness loss is freely available at https://github.com/onvungocminh/MBD_BAL

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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. Since this distance was initially developped for grayscale images, we propose here an extension of this transform to multivariate images; we call it vectorial Dahu pseudo-distance. An efficient way to compute it is provided in this paper. Besides, we provide benchmarks demonstrating how much the vectorial Dahu pseudo-distance is more robust and competitive compared to other MB-based distances, which shows how much this distance is promising for salient object detection, shortest path finding, and object segmentation.

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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. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each n th slice of the volume to segment, we consider three images, corresponding to the (n-1)th, nth, and (n-1)th slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the n th slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of a 3D volume takes only a few seconds. The prediction is based on Random Forests, and has the advantage of not being dependant of the acquisition modality, making it robust to inter-base data.

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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. We then apply it on five state-of-the-art protocols, and exhibit that there indeed exist inconsistencies among their evaluation criteria. Our aim here is not to rank the investigated evaluation protocols, but rather raising awareness in the community that we should carefully reconsider them in order to converge to their optimal usage.

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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. We show that the saliency maps we obtain are competitive with state-of-the-art visual saliency methods and, that such approaches are very promising for use in identity document detection and segmentation, even without taking into account any prior knowledge about document contents. In particular they can work in real-time on smartphones.

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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. Experimental results on nine segmentation algorithms using different evaluation frameworks are also provided to emphasize the interest of our method.

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