Federico Bolelli

Federico Bolelli
Università degli Studi di Modena e Reggio Emilia | UNIMO · Department of Engineering "Enzo Ferrari"

PHD

About

38
Publications
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412
Citations
Introduction

Publications

Publications (38)
Chapter
Several algorithmic solutions for the optimization of Connected Components Labeling have been proposed in literature. Among them, one of the most effective is a block-based mask to drastically reduce the number of memory accesses during the labeling procedure. This paper proposes a systematic approach for labeling multiple pixels at once, automatic...
Chapter
Full-text available
At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to main...
Article
Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been...
Article
Full-text available
Convolutional Neural Networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). Like in many other medical imaging domains, state-of-the-art methods take advantage of architectures developed for other tasks, frequently assu...
Conference Paper
Full-text available
Connected Components Labeling (CCL) is a fundamental task in binary image processing. Since its introduction in the sixties, several algorithmic strategies have been proposed to optimize its execution time. Most CCL algorithms in literature, including the current state-of-the-art, are designed to work on an input stored with 1-byte per pixel, even...
Chapter
Full-text available
This paper provides a detailed description of how to install, setup, and use the YACCLAB benchmark to test the algorithms published in “A Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes,” underlying how the parameters affect and influence experimental results.
Article
Full-text available
In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, GRAPHGEN, that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms intro...
Article
Background and objectives: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting. Design, setting, participants...
Article
Full-text available
This paper presents a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the skin lesion segmentation task, which is a fundamental first step in the automated melanoma detection process. The proposed framework generates both skin lesion images and their segmentation masks, making the data augmentation process extr...
Article
Full-text available
The problem of labeling the connected components of a binary image is well defined, and several proposals have been presented in the past. Since an exact solution to the problem exists, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together wit...
Article
Full-text available
Connected Components Labeling is an essential step of many Image Processing and Computer Vision tasks. Since the first proposal of a labeling algorithm, which dates back to the sixties, many approaches have optimized the computational load needed to label an image. In particular, the use of decision forests and state prediction have recently appear...
Chapter
Full-text available
In this paper, we introduce a novel GPU-based Connected Components Labeling algorithm: the Block-based Union Find. The proposed strategy significantly improves an existing GPU algorithm, taking advantage of a block-based approach. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposal...
Chapter
Full-text available
In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means...
Article
Full-text available
Connected Components Labeling (CCL) is a crucial step of several image processing and computer vision pipelines. Many efficient sequential strategies exist, among which one of the most effective is the use of a block-based mask to drastically cut the number of memory accesses. In the last decade, aided by the fast development of Graphics Processing...
Chapter
Full-text available
In this paper the problem of Connected Components Labeling (CCL) in binary images using Graphic Processing Units (GPUs) is tackled by a different perspective. In the last decade, many novel algorithms have been released, specifically designed for GPUs. Because CCL literature concerning sequential algorithms is very rich, and includes many efficient...
Chapter
Full-text available
This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmentation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features. An encoder-de...
Chapter
Full-text available
In this paper we describe the algorithmic implementation details of “Connected Components Labeling on DRAGs” (Directed Rooted Acyclic Graphs), studying the influence of parameters on the results. Moreover, a detailed description of how to install, setup and use YACCLAB (Yet Another Connected Components LAbeling Benchmark) to test DRAG is provided.
Preprint
Full-text available
In this paper the problem of Connected Components Labeling (CCL) in binary images using Graphic Processing Units (GPUs) is tackled by a different perspective. In the last decade, many novel algorithms have been released, specifically designed for GPUs. Because CCL literature concerning sequential algorithms is very rich, and includes many efficient...
Preprint
Full-text available
This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmen-tation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features. An encoder-d...
Article
Full-text available
Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic “someone” tag. The lack of movie description datasets with characters’ visual annotations surely plays a relevant role in this shortage. Recently, we proposed to extend the M-VAD dataset by introducing such informati...
Conference Paper
Full-text available
This paper proposes a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the image segmentation field, and a Convolutional-Deconvolutional Neural Network (CDNN) to automatically generate lesion segmentation mask from dermoscopic images. Training the CDNN with our GAN generated data effectively improves the state-o...
Preprint
Full-text available
In this paper we introduce a new Connected Components Labeling (CCL) algorithm which exploits a novel approach to model decision problems as Directed Acyclic Graphs with a root, which will be called Directed Rooted Acyclic Graphs (DRAGs). This structure supports the use of sets of equivalent actions, as required by CCL, and optimally leverages thes...
Conference Paper
Full-text available
This work presents a research project, named XDOCS, aimed at extending to a much wider audience the possibility to access a variety of historical documents published on the web. The paper presents an overview of the indexing process that will be used to achieve the goal, focusing on the adopted dewarping technique. The proposed dewarping approach p...
Conference Paper
Full-text available
In this paper we present an innovative technique to semi-automatically index handwritten word images. The proposed method is based on HOG descriptors and exploits Dynamic Time Warping technique to compare feature vectors elaborated from single handwritten words. Our strategy is applied to a new challenging dataset extracted from Italian civil regis...
Conference Paper
Full-text available
This paper presents two strategies that can be used to improve the speed of Connected Components Labeling algorithms. The first one operates on optimal decision trees considering image patterns occurrences, while the second one articulates how two scan algorithms can be parallelized using multi-threading. Experimental results demonstrate that the p...
Conference Paper
In this paper we propose a new paradigm for connected components labeling, which employs a general approach to minimize the number of memory accesses, by exploiting the information provided by already seen pixels, removing the need to check them again. The scan phase of our proposed algorithm is ruled by a forest of decision trees connected into a...

Projects

Projects (4)
Project
Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth) project is funded by the EC under the topic ICT-11-2018-2019 “HPC and Big Data enabled Large-scale Test-beds and Applications”. DeepHealth is a 3-year project, kicked-off in mid January 2019 and is expected to conclude its work in December 2021. The aim of DeepHealth is to offer a unified framework completely adapted to exploit underlying heterogeneous HPC and Big Data architectures; and assembled with state-of-the-art techniques in Deep Learning and Computer Vision. In particular,the project will combine High-Performance Computing (HPC) infrastructures with Deep Learning (DL) and Artificial Intelligence (AI) techniques to support biomedical applications that require the analysis of large and complex biomedical datasets and thus, new and more efficient ways of diagnosis, monitoring and treatment of diseases.
Archived project
Indexing of Historical Documents