Connected Components Algorithm Research Articles

An efficient incremental strongly connected components algorithm for evolving directed graphs

The strongly connected components (SCC) algorithm can contract a directed graph into a directed acyclic graph and is widely used in directed graph analysis applications, such as reachability queries. A variety of SCC algorithms for static directed graphs have been proposed but such algorithms require non-negligible runtime overheads to repeatedly perform computations on an entire graph in response to the frequent changes in the evolving directed graphs that are ubiquitous in the real world. In general, evolving directed graphs are often evolving with minor changes (less than 5%).It allows us to compute SCC in an evolving directed graph on the basis of incremental computations in order to reduce the response time. This paper proposes Inc-SCC, an efficient incremental SCC algorithm for evolving directed graphs, reducing the data access and computation overhead of the algorithm by eliminating unnecessary computations, and using the disjoint feature of SCC for parallel processing to improve the performance of the SCC algorithm. We propose a heuristic optimization method to further speed up the convergence of Inc-SCC. Experiments show that Inc-SCC can be used to enhance the timeliness for evolving directed graphs. When the number of the changed edges of the entire directed graph is 5%, the speedup of Inc-SCC over the existing algorithm is from 2.8 to 12 times. When the number of thechanged edges of an entire directed graph is 0.5%, the speedup of Inc-SCC over the existing algorithm is from 2.9 to 12 times.

Comparative Study and Proof of Single-Pass Connected Components Algorithms

Union-find algorithms form the basis of managing sets of equivalent labels within most connected components labelling algorithms. The new class of single-pass connected components analysis (CCA) algorithms (where a feature vector of each component is extracted during processing) are analysed and compared within this context. Such algorithms have been developed for stream processing, using customised hardware architectures. Many of these use an improved union-find algorithm requiring only a single lookup for its find operation. This paper analyses this optimisation and formally proves that the resulting single lookup connected components algorithm associates each pixel with its correct component when extracting the components’ feature vectors. Analysis of the algorithm led to a new double lookup algorithm that reduces the total number of memory accesses and is a step towards unifying pixel-based methods and run-based methods. State-of-the-art CCA algorithms are compared in terms of the number of memory accesses, which is a limiting factor for hardware-based acceleration, with key implementation trade-offs identified between hardware resources and worst-case processing speed.

A Simple Randomized 3-edge Connected Component Algorithm

Finding the 3-edge connected components of a graph is a well-researched problem for which many algorithms are known. In this paper, we present a new linear-time randomized algorithm for the problem. To the best of our knowledge, this is the first randomized algorithm for partitioning a graph into 3-edge connected components. The algorithm is a composition of simple building blocks, it is easy to understand and implement, and it has no corner cases.

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm.

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature. Furthermore, in this paper, an improved segmentation approach based on watershed algorithm, neutrosophic sets (NS), and fast fuzzy c-mean clustering algorithm (FFCM) for CT liver tumor segmentation is proposed. To increase the contrast of the liver CT images, the intensity values are adjusted and high frequencies are removed using histogram equalization and median filter approach. It is followed by transforming the CT image to NS domain, which is described using three subsets (percentage of truth T, the percentage of indeterminacy I, and percentage of falsity F). The obtained NS image is enhanced by adaptive threshold and morphological operators to focus on liver parenchyma. The enhanced NS image passed to a watershed algorithm for post-segmentation process and liver parenchyma is extracted using the connected component algorithm. Finally, the liver tumors are segmented from the segmented liver using fast fuzzy c-mean (FFCM). A quantitative analysis is carried out to evaluate segmentation results using six different indices. The results show that the overall accuracy offered by the employed neutrosophic sets is accurate, less time consuming, less sensitive to noise and performs better on non-uniform CT images.

A New Efficient Approach to Detect Skin in Color Image Using Bayesian Classifier and Connected Component Algorithm

Skin detection is an interesting problem in image processing and is an important preprocessing step for further techniques like face detection, objectionable image detection, etc. However, its performance has not really been high because of the high overlapped degree between “skin” and “nonskin” pixels. This paper proposes a new approach to improve the skin detection performance using the Bayesian classifier and connected component algorithm. Specifically, the Bayesian classifier is utilized to identify “true skin” pixels using the first posterior probability threshold, which is approximate to 1, and to identify "skin candidate" pixels using the second posterior probability threshold. Subsequently, the connected component algorithm is used to find all the connected components containing the “skin candidate” pixels. According to the fact that a skin pixel often connects with other skin pixels in an image, all pixels in a connected component are classified as “skin” if there is at least one “true skin” pixel in that connected component. It means that the “nonskin” pixels whose color is similar to skin are classified as “nonskin” when they have the posterior probabilities lower than the first posterior probability threshold and do not connect with any “true skin” pixel. This idea can help us to improve the skin classification performance, especially the false positive rate.

An Efficient Strongly Connected Components Algorithm in the Fault Tolerant Model

In this paper we study the problem of maintaining the strongly connected components of a graph in the presence of failures. In particular, we show that given a directed graph $$G=(V,E)$$ with $$n=|V|$$ and $$m=|E|$$ , and an integer value $$k\ge 1$$ , there is an algorithm that computes in $$O(2^{k}n\log ^2 n)$$ time for any set F of size at most k the strongly connected components of the graph $$G{\setminus }F$$ . The running time of our algorithm is almost optimal since the time for outputting the SCCs of $$G{\setminus } F$$ is at least $$\varOmega (n)$$ . The algorithm uses a data structure that is computed in a preprocessing phase in polynomial time and is of size $$O(2^{k} n^2)$$ . Our result is obtained using a new observation on the relation between strongly connected components (SCCs) and reachability. More specifically, one of the main building blocks in our result is a restricted variant of the problem in which we only compute strongly connected components that intersect a certain path. Restricting our attention to a path allows us to implicitly compute reachability between the path vertices and the rest of the graph in time that depends logarithmically rather than linearly in the size of the path. This new observation alone, however, is not enough, since we need to find an efficient way to represent the strongly connected components using paths. For this purpose we use a mixture of old and classical techniques such as the heavy path decomposition of Sleator and Tarjan (J Comput Syst Sci 26:362–391, 1983) and the classical Depth-First-Search algorithm. Although, these are by now standard techniques, we are not aware of any usage of them in the context of dynamic maintenance of SCCs. Therefore, we expect that our new insights and mixture of new and old techniques will be of independent interest.

Communication-avoiding parallel minimum cuts and connected components

We present novel scalable parallel algorithms for finding global minimum cuts and connected components, which are important and fundamental problems in graph processing. To take advantage of future massively parallel architectures, our algorithms are communication-avoiding : they reduce the costs of communication across the network and the cache hierarchy. The fundamental technique underlying our work is the randomized sparsification of a graph: removing a fraction of graph edges, deriving a solution for such a sparsified graph, and using the result to obtain a solution for the original input. We design and implement sparsification with O (1) synchronization steps. Our global minimum cut algorithm decreases communication costs and computation compared to the state-of-the-art, while our connected components algorithm incurs few cache misses and synchronization steps. We validate our approach by evaluating MPI implementations of the algorithms on a petascale supercomputer. We also provide an approximate variant of the minimum cut algorithm and show that it approximates the exact solutions well while using a fraction of cores in a fraction of time.

Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images

The severity of fat in ultrasonic liver images is quantified based on characteristics of three regions in the image namely diaphragm, periportal veins and texture of liver parenchyma. The characteristics of these regions vary with the severity of fat in the liver, and is subjected to low signal to noise ratio, low contrast, poorly defined organ boundaries, etc., hence locating these regions in ultrasound images is challenging task for the sonographers. Automated detection of these regions will help the sonographers to do accurate diagnosis in shorter time, and also acts as a fundamental step to develop automated diagnostic algorithms. In this paper, we propose a novel multi-modal framework for detecting diaphragm, periportal veins and texture of liver parenchyma in ultrasonic liver ultrasound images. Since the characteristics of these regions differ from each other, we propose a specific algorithm for detecting each region. Diaphragm and periportal veins are detected with the combination of Viola Jones and GIST descriptor based classifier, while homogeneous texture regions are detected with the combination of histogram features based classifier and connected components algorithm. The proposed algorithm when tested on 180 ultrasound liver images, detected the diaphragm, periportal veins and texture regions with an accuracy of 97%, 91% and 100% respectively.

Surface defect identification and measurement for metal castings by vision system

An inspection system based on vision technology was developed to identify defects on the surface of a metal part produced by a casting process. In the proposed methodology, binary images of the bright and dark regions of the surface are first obtained. Connected components of these images are processed to find the shadows originated from defects. The algorithm to process the binary images was implemented on a Jetson TK1 board, and programmed in CUDA. The setup performs the computation in 900 ms for images of 5 megapixels, and the connected components algorithm is three times faster compared to commercial software running on a CPU. The parameters to find the shadows are independent of the field of view and resolution, i.e., the quantities that relate the two binary images can be expressed in pixels.

On the complexity of monitoring Orchids signatures, and recurrence equations

Modern monitoring tools such as our intrusion detection tool Orchids work by firing new monitor instances dynamically. Given an Orchids signature (a.k.a. a rule, a specification), what is the complexity of checking that specification, that signature? In other words, let f(n) be the maximum number of monitor instances that can be fired on a sequence of n events: we design an algorithm that decides whether f(n) is asymptotically exponential or polynomial, and in the latter case returns an exponent d such that $$f(n) = \varTheta (n^d)$$ . Ultimately, the problem reduces to the following mathematical question, which may have other uses in other domains: given a system of recurrence equations described using the operators $$+$$ and $$\max $$ , and defining integer sequences $$u_n$$ , what is the asymptotic behavior of $$u_n$$ as n tends to infinity? We show that, under simple assumptions, $$u_n$$ is either exponential or polynomial, and that this can be decided, and the exponent computed, using a simple modification of Tarjan’s strongly connected components algorithm, in linear time.

Modified image fusion technique to remove defocus noise in optical scanning holography

In sectional image reconstruction based on optical scanning holography (OSH) system, tomographic images are always contaminated by defocus noise. Though lots of methods have been proposed to solve this problem, some unsatisfactory results remain. In this article, a modified image fusion method is presented based on wavelet transform and connected component algorithms to deal with the defects in a random-phase OSH system where different sectional images usually have a relative displacement, rotation and loss. The method can fully utilize redundant information and affine transformation to repair the defects, and the defocus noise can be removed well. The final sectional images are more visible.

Real-time enhancement of sparse 3D maps using a parallel segmentation scheme based on superpixels

In this work, we focus on the problem of feature-based 3D mapping of environments with large textureless regions, which generates sparse 3D maps that may not represent well the mapped scene. To deal with this problem, based on our previous work (Cruz Martinez et al. in 2016 IEEE international symposium on mixed and augmented reality (ISMAR) adjunct proceedings, IEEE, 2016), we propose to enhance sparse 3D maps by using a superpixel-based segmentation with the aim of generating denser 3D maps of the scene in real time. Superpixels are middle-level features, which represent similar regions in an image, which can be connected in order to segment textureless areas. We propose a graphics processor unit architecture for (1) superpixel extraction considering chromatic and depth information, (2) superpixel-based segmentation, generation of connectivity matrix to compute the connected components algorithm and (3) mapping of segmented regions to 3D points. We use the ORB-SLAM system (Mur-Artal et al. in IEEE Trans Robot 31(5):1147–1163, 2015) to generate a sparse 3D map and to project the textureless segments onto it at 27 frames per second. We assessed our approach in terms of segmentation and map quality. Regarding the latter, covered area by the generated map, depth accuracy, and computational performance are reported.

Advanced Approach of Material Region Detections on Fibre-Reinforced Concrete CT-Scans

Detections of material regions on CT-scans of solids are commonly treated manually by an expert. Although such manual detections have many advantages, some amount of human error is also incorporated. Moreover, expert opinions may vary significantly. We present an application of the k-means++ clustering as an alternative option to manual way of material area detections. k-means++ clustering is derived from k-means (the method of vector quantization, originally from signal processing), popular for cluster analysis in data mining and image processing communities. The algorithm s main advantages are its simple implementation and fast convergence to a local optimum of an objective function. We benchmark the suggested approach on transverse CT-scans of a fibre-reinforced concrete solid. Moreover, we introduce a technique for processing air distribution, such that the appropriate pixels detected as the pixels of air are converted into pixels representing concrete. The technique is based on the connected component algorithm. Benchmark and results of proposed method conclude the paper.

$hp$-Adaptive Discontinuous Galerkin Methods for Neutron Transport Criticality Problems

In this article we consider the application of high-order/$hp$-version adaptive discontinuous Galerkin finite element methods (DGFEMs) for the discretization of the $k_{eff}$-eigenvalue problem associated with the neutron transport equation. To this end, we exploit the dual weighted residual approach to derive a reliable and efficient a posteriori error estimate for the computed critical value of $k_{eff}$. Moreover, by exploiting the underlying block structure of the $hp$-version DGFEM, we propose and implement an efficient numerical solver based on exploiting Tarjan's strongly connected components algorithm to compute the inverse of the underlying transport operator; this is then utilized as an efficient preconditioner for the $k_{eff}$-eigenvalue problem. Finally, on the basis of the derived a posteriori error estimator we propose an $hp$-adaptive refinement algorithm which automatically refines both the angular and spatial domains. The performance of this adaptive strategy is demonstrated on a series ...

Segmentation of Liver Abnormality based on Label Connected Component Algorithm

Medical imaging uses a non invasive method to view the internal structure of human body. This helps the radiologists to study and visualize the region of interest for treatment planning and surgery. Computer tomography imaging gives detail view of soft tissues of abdominal organ. The objective of the proposed work to segment the liver from abdominal CT image using segmentation technique. The method uses bilateral filter for preprocessing and for finding the liver region, label connected component algorithm is proposed. The performance of the algorithm is compared with ground truth from experts. The performance measures includes Dice coefficient, misclassification rate and the volume of liver.

Individual Tree Crown Modeling and Change Detection From Airborne Lidar Data

Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne lidar point clouds. Three datasets covering a part of Rotterdam, The Netherlands, have been classified into several classes including trees. A connected components algorithm is applied first to cluster the tree points. However, closely located and intersected trees are clustered together as multi-tree components. A tree-shaped model-based continuously adaptive mean shift (CamShift) algorithm is implemented to further segment these components into individual trees. Then, the tree parameters are derived in two independent methods: a point-based method using the convex hull and a model-based method which fits a tree-shaped model to the lidar points. At last, changes are detected by comparing the parameters of corresponding tree models which are matched by a tree-to-tree matching algorithm using overlapping bounding boxes and point-to-point distances. The results are visualized and statistically analyzed. The CamShift using a tree model kernel yields high segmentation accuracies. The model-based change detection is consistent with the point-based method according to the small differences between the parameters of single trees. The highlight is that it is more robust to data noise and to the segmentation of multi-tree components compared to the point-based method. The detected changes show the potential of the method to monitor the growth of urban trees.

A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution.

The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.

An Automatic Approach of Road Regions Extraction from Satellite Images based on Connected Component Algorithm

An Automatic Approach of Road Regions Extraction from Satellite Images based on Connected Component Algorithm

Improving Scalability of Java Archive Search Engine through Recursion Conversion And Multithreading

Based on the fact that bytecode always exists on Java archive, a bytecode based Java archive search engine had been developed [1, 2]. Although this system is quite effective, it still lack of scalability since many modules apply recursive calls and this system only utilizes one core (single thread). In this research, Java archive search engine architecture is redesigned in order to improve its scalability. All recursion are converted to iterative forms although most of these modules are logically recursive and quite difficult to convert (e.g. Tarjan’s strongly connected component algorithm). Recursion conversion can be conducted by following its respective recursive pattern. Each recursion is broke down to four parts (before and after actions of current and its children) and converted to iteration with the help of caller reference. This conversion mechanism improves scalability by avoiding stack overflow error caused by method calls. System scalability is also improved by applying multithreading mechanism which successfully cut off its processing time. Shorter processing time may enable system to handle larger data. Multithreading is applied on major parts which are indexer, vector space model (VSM) retriever, low-rank vector space model (LRVSM) retriever, and semantic relatedness calculator (semantic relatedness calculator also involves multiprocess). The correctness of both recursion conversion and multithread design are proved by the fact that all implementation yield similar result.

Parallel Light Speed Labeling: an efficient connected component algorithm for labeling and analysis on multi-core processors

In the last decade, many papers have been published to present sequential connected component labeling (CCL) algorithms. As modern processors are multi-core and tend to many cores, designing a CCL algorithm should address parallelism and multithreading. After a review of sequential CCL algorithms and a study of their variations, this paper presents the parallel version of the Light Speed Labeling for connected component analysis (CCA) and compares it to our parallelized implementations of State-of-the-Art sequential algorithms. We provide some benchmarks that help to figure out the intrinsic differences between these parallel algorithms. We show that thanks to its run-based processing, the LSL is intrinsically more efficient and faster than all pixel-based algorithms. We show also, that all the pixel-based are memory-bound on multi-socket machines and so are inefficient and do not scale, whereas LSL, thanks to its RLE compression can scale on such high-end machines. On a 4 ? 15-core machine, and for 8192 ? 8192 images, LSL outperforms its best competitor by a factor ?10.8 and achieves a throughput of 42.4 gigapixel labeled per second.