Two More Strategies to Speed Up Connected Components Labeling Algorithms

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 proposed methodologies reduce the total execution time of state-of-the-art two scan algorithms

Optimizing GPU-Based Connected Components Labeling Algorithms

Connected Components Labeling (CCL) is a fundamental image processing technique, widely used in various application areas. Computational throughput of Graphical Processing Units (GPUs) makes them eligible for such a kind of algorithms. In the last decade, many approaches to compute CCL on GPUs have been proposed. Unfortunately, most of them have focused on 4-way connectivity neglecting the importance of 8-way connectivity. This paper aims to extend state-of-the-art GPU-based algorithms from 4 to 8-way connectivity and to improve them with additional optimizations. Experimental results revealed the effectiveness of the proposed strategies

Towards Reliable Experiments on the Performance of Connected Components Labeling Algorithms

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 with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for many kinds of tests, which analyze the methods from different perspectives. An extensive set of experiments among state-of-the-art techniques is reported and discussed

Connected Components Labeling on DRAGs: Implementation and Reproducibility Notes

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

Connected Components Labeling on DRAGs

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 these equivalences to reduce the number of nodes (decision points). The advantage of this representation is that a DRAG, differently from decision trees usually exploited by the state-of-the-art algorithms, will contain only the minimum number of nodes required to reach the leaf corresponding to a set of condition values. This combines the benefits of using binary decision trees with a reduction of the machine code size. Experiments show a consistent improvement of the execution time when the model is applied to CCL

Friction and Heat Transfer in Membrane Distillation Channels: An Experimental Study on Conventional and Novel Spacers

The results of an experimental investigation on pressure drop and heat transfer in spacer- filled plane channels, which are representative of Membrane Distillation units, are presented and discussed. Local and mean heat transfer coefficients were obtained by using Thermochromic Liquid Crystals and Digital Image Processing. The performances of a novel spacer geometry, consisting of spheres that are connected by cylindrical rods, and are hereafter named spheres spacers, were compared with those of more conventional woven and overlapped spacers at equal values of the Reynolds number Re (in the range ~150 to ~2500), the pitch-to-channel height ratio, the flow attack angle and the thermal boundary conditions (two-side heat transfer). For any flow rate, the novel spacer geometry provided the least friction coefficient and a mean Nusselt number intermediate between those of the overlapped and the woven spacers. For any pressure drop and for any pumping power, the novel spacer provided the highest mean Nusselt number over the whole Reynolds number range that was investigated. The influence of buoyancy was also assessed for the case of the horizontal channels. Under the experimental conditions (channel height H ≈ 1 cm, ∆T ≈ 10 ◦C), it was found to be large in empty (spacer-less) channels that were up to Re ≈ 1200 (corresponding to a Richardson number Ri of ~0.1), but it was much smaller and limited to the range Re < ~500 (Ri < ~0.5) in the spacer-filled channels

How does Connected Components Labeling with Decision Trees perform on GPUs?

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 solutions, designers of parallel algorithms were often inspired by techniques that had already proved successful in a sequential environment, such as the Union-Find paradigm for solving equivalences between provisional labels. However, the use of decision trees to minimize memory accesses, which is one of the main feature of the best performing sequential algorithms, was never taken into account when designing parallel CCL solutions. In fact, branches in the code tend to cause thread divergence, which usually leads to inefficiency. Anyway, this consideration does not necessarily apply to every possible scenario. Are we sure that the advantages of decision trees do not compensate for the cost of thread divergence? In order to answer this question, we chose three well-known sequential CCL algorithms, which employ decision trees as the cornerstone of their strategy, and we built a data-parallel version of each of them. Experimental tests on real case datasets show that, in most cases, these solutions outperform state-of-the-art algorithms, thus demonstrating the effectiveness of decision trees also in a parallel environment

SACHER Project: A Cloud Platform and Integrated Services for Cultural Heritage and for Restoration

The SACHER project provides a distributed, open source and federated cloud platform able to support the life-cycle management of various kinds of data concerning tangible Cultural Heritage. The paper describes the SACHER platform and, in particular, among the various integrated service prototypes, the most important ones to support restoration processes and cultural asset management: (i) 3D Life Cycle Management for Cultural Heritage (SACHER 3D CH), based on 3D digital models of architecture and dedicated to the management of Cultural Heritage and to the storage of the numerous data generated by the team of professionals involved in the restoration process; (ii) Multidimensional Search Engine for Cultural Heritage (SACHER MuSE CH), an advanced multi-level search system designed to manage Heritage data from heterogeneous sources

The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

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

Transgenic Expression of Nonclassically Secreted FGF Suppresses Kidney Repair

FGF1 is a signal peptide-less nonclassically released growth factor that is involved in angiogenesis, tissue repair, inflammation, and carcinogenesis. The effects of nonclassical FGF export in vivo are not sufficiently studied. We produced transgenic mice expressing FGF1 in endothelial cells (EC), which allowed the detection of FGF1 export to the vasculature, and studied the efficiency of postischemic kidney repair in these animals. Although FGF1 transgenic mice had a normal phenotype with unperturbed kidney structure, they showed a severely inhibited kidney repair after unilateral ischemia/reperfusion. This was manifested by a strong decrease of postischemic kidney size and weight, whereas the undamaged contralateral kidney exhibited an enhanced compensatory size increase. In addition, the postischemic kidneys of transgenic mice were characterized by hyperplasia of interstitial cells, paucity of epithelial tubular structures, increase of the areas occupied by connective tissue, and neutrophil and macrophage infiltration. The continuous treatment of transgenic mice with the cell membrane stabilizer, taurine, inhibited nonclassical FGF1 export and significantly rescued postischemic kidney repair. It was also found that similar to EC, the transgenic expression of FGF1 in monocytes and macrophages suppresses kidney repair. We suggest that nonclassical export may be used as a target for the treatment of pathologies involving signal peptide-less FGFs