Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

2020 Joint 9th International Conference on Informatics, Electronics &Amp; Vision (ICIEV) and 2020 4th International Conference

Grana

2020

Self Cite

Contours extraction, also known as chain-code extraction, is one of the most common algorithms of binary image processing. Despite being the raster way the most cache friendly and, consequently, fast way to scan an image, most commonly used chain-code algorithms perform contours tracing, and therefore tend to be fairly inefficient. In this paper, we took a rarely used algorithm that extracts contours in raster scan, and optimized its execution time through template functions, look-up tables and decision trees, in order to reduce code branches and the average number of load/store operations required. The result is a very fast solution that outspeeds the state-of-the-art contours extraction algorithm implemented in OpenCV, on a collection of real case datasets. Contribution-This paper significantly improves the performance of existing chain-code algorithms, by smartly introducing decision trees to reduce code branches and the average number of load/store operations required.

Section: Resultsmentioning

confidence: 99%

A Warp Speed Chain-Code Algorithm Based on Binary Decision Trees

Allegretti

2020 Joint 9th International Conference on Informatics, Electronics &Amp; Vision (ICIEV) and 2020 4th International Conference

Grana

2020

Self Cite

“…Obviously, all the classic operations for image manipulation such as rotation, resizing, mirroring and colour space change are available. Extremely optimized processing functions, like noising, blurring, contour finding [26], image skeletonization [27] and connected components labelling [28] are implemented as well. ECVL image-processing operations can be applied on-the-fly during deep neural networks training to implement data augmentation.…”

Section: The European Computer Vision Librarymentioning

confidence: 99%

The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures

Aldinucci

Atienza

Technologies and Applications for Big Data Value

et al. 2022

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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 maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.

“…Of course, all the classic operations for image manipulation such as rotation, resizing, mirroring, and color space change are available. Processing functions, like noising, blurring, contour finding [8], image skeletonization [9], and connected components labeling [10], [11], [12] are implemented as well. A cross-platform GUI based on ECVL and wxWidgets [13] is also provided to allow simple exploration and test of ECVL functionalities.…”

Section: A Ecvl Image and Functionsmentioning

confidence: 99%

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Cancilla

Canalini

2020 25th International Conference on Pattern Recognition (ICPR)

et al. 2021

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Given the overwhelming impact of machine learning on the last decade, several libraries and frameworks have been developed in recent years to simplify the design and training of neural networks, providing array-based programming, automatic differentiation and user-friendly access to hardware accelerators. None of those tools, however, was designed with native and transparent support for Cloud Computing or heterogeneous High-Performance Computing (HPC). The DeepHealth Toolkit is an open source Deep Learning toolkit aimed at boosting productivity of data scientists operating in the medical field by providing a unified framework for the distributed training of neural networks, which is able to leverage hybrid HPC and cloud environments in a transparent way for the user. The toolkit is composed of a Computer Vision library, a Deep Learning library, and a front-end for non-expert users; all of the components are focused on the medical domain, but they are general purpose and can be applied to any other field. In this paper, the principles driving the design of the DeepHealth libraries are described, along with details about the implementation and the interaction between the different elements composing the toolkit. Finally, experiments on common benchmarks prove the efficiency of each separate component and of the DeepHealth Toolkit overall.