Optimized Block-based Connected Components Labeling with Decision Trees

In this paper we define a new paradigm for 8-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. Firstly we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Secondly we propose a new scanning technique that moves on a 2x2 pixel grid over the image, which is optimized by the automatically generated decision tree.An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms

Distributed Graph Automata and Verification of Distributed Algorithms

Combining ideas from distributed algorithms and alternating automata, we introduce a new class of finite graph automata that recognize precisely the languages of finite graphs definable in monadic second-order logic. By restricting transitions to be nondeterministic or deterministic, we also obtain two strictly weaker variants of our automata for which the emptiness problem is decidable. As an application, we suggest how suitable graph automata might be useful in formal verification of distributed algorithms, using Floyd-Hoare logic.Comment: 26 pages, 6 figures, includes a condensed version of the author's Master's thesis arXiv:1404.6503. (This version of the article (v2) is identical to the previous one (v1), except for minor changes in phrasing.

Integrated Development and Parallelization of Automated Dicentric Chromosome Identification Software to Expedite Biodosimetry Analysis

Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the effectiveness of parallelization in accelerating ADCI. Our software will be an important tool to handle the magnitude of mass casualty ionizing radiation events by expediting accurate detection of dicentric chromosomes

A State-of-the-Art Review with Code about Connected Components Labeling on GPUs

This article is about Connected Components Labeling (CCL) algorithms developed for GPU accelerators. The task itself is employed in many modern image-processing pipelines and represents a fundamental step in different scenarios, whenever object recognition is required. For this reason, a strong effort in the development of many different proposals devoted to improving algorithm performance using different kinds of hardware accelerators has been made. This paper focuses on GPU-based algorithmic solutions published in the last two decades, highlighting their distinctive traits and the improvements they leverage. The state-of-the-art review proposed is equipped with the source code, which allows to straightforwardly reproduce all the algorithms in different experimental settings. A comprehensive evaluation on multiple environments is also provided, including different operating systems, compilers, and GPUs. Our assessments are performed by means of several tests, including real-case images and synthetically generated ones, highlighting the strengths and weaknesses of each proposal. Overall, the experimental results revealed that block-based oriented algorithms outperform all the other algorithmic solutions on both 2D images and 3D volumes, regardless of the selected environment