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The k-truss of a graph is a subgraph such that each edge is tightly connected to the remaining elements in the k-truss. The k-truss of a graph can also represent an important community in the graph. Finding the k-truss of a graph can be done in a polynomial amount of time, in contrast finding other subgraphs such as cliques. While there are numerous formulations and algorithms for finding the maximal...
Total variation (TV) and non-local patch similarity have been used successfully to enhance the performance of compressive sensing (CS) approaches. However, such techniques can often remove important details in the image or introduce reconstruction artifacts. This paper presents a novel CS method, which uses an adaptive reweighted TV strategy to better preserve image edges. Our method also leverages...
An efficient stereo matching algorithm, which applies adaptive smoothness constraints using texture and edge information, is proposed in this work. First, we determine non-textured regions, on which an input image yields flat pixel values. In the non-textured regions, we penalize depth discontinuity and complement the primary CNN-based matching cost with a color-based cost. Second, by combining two...
In this paper, a new moving block sequence (MBS) representation for resource-constrained project scheduling problems (RCPSPs) is proposed, which is different from the classical activity list that has been widely used for RCPSPs. An activity in a project of RCPSPs has fixed duration and resource demands, thus, it can be modeled as a rectangle block whose height represents the resource demands and width...
Finger vein recognition is a newly developed and promising biometrics technology. In current researches, finger vein recognition algorithms are mostly evaluated on data collected in laboratory environment. Along with its development, this technology gradually transforms from laboratory to actual use. Finger vein images captured in operational systems are different from those obtained in the lab, due...
In this paper we propose two algorithms for overlapping community detection based on neighborhood vector propagation algorithm(NVPA), a community detection algorithm which can detect disjoint communities with high accuracy. The first algorithm is named Link Partition of Overlapping Communities (LPOC). In this algorithm, we first convert a node graph to a link graph, then we use NVPA to find the communities...
Stereo matching, as many problems in computer vision, has been addressed by a multitude of algorithms, each with its own strengths and weaknesses. Instead of following the conventional approach and trying to tune or enhance one of the algorithms so that it dominates the competition, we resign to the idea that a truly optimal algorithm may not be discovered soon and take a different approach. We present...
Community detection is one of the most popular issues in analyzing and understanding the networks. Existing works show that community detection can be enhanced by proper assignments of weights onto the edges of a network. Large numbers of edge weighting schemes have been developed to cope with this problem. However, hardly has a satisfied balance between the local and global weightings been found...
Community Structure is one of the most relevant features of real world networks. Detecting such structures in large scale networks is a challenging task in scientific world. These are similar to clusters in which intra cluster density is more than the inter cluster density. This paper reviews the prominent community detection algorithms that detect both disjoint and overlapped communities. These algorithms...
In this paper we investigate two real crime-related networks, which are both bipartite. The bipartite networks are: a spatial network where crimes of various types are committed in different local government areas; and a dark terrorist network where individuals attend events or have common affiliations. In each case we analyse the communities found by a random-walk based algorithm in the primary weighted...
Stereo matching plays a significant role in various vision based systems including 3D reconstruction, robot localization, mapping and navigation and it has been an intense area of research for many years. In the last decade, with the increase in processing power and reduction in the camera cost, the stereo systems have found many new applications. In this paper, we present a stereo matching algorithm...
The study of community detection has received more and more attention in recent years, the problem is very difficult and of great importance in many fields such as sociology, biology and computer science. But most of the algorithms proposed so far could not utilize the weight information within weighted networks, and many of them are so time-consuming that they are not fit for the large-scale networks...
This paper presents a local stereo matching algorithm based on the window construction method using local edge detection. In order to improve performance of window-based cost aggregation computation, a new rule called Dissimilar Intensity Support technique is proposed to distinguish support pixels with dissimilar intensities from those with similar intensity for each centered pixel. According to the...
Community detection is now playing a significant role in the discovery of underlying structures of social networks. This problem has been proved to be very hard and not been satisfactorily solved yet. Most of the algorithms proposed so far tend to maximize the number of intra-cluster edges, but ignore the importance of the core nodes within clusters. In contrast, this paper proposes a core-based algorithm...
Community detection on networks is a well-known problem encountered in many fields, for which the existing algorithms are inefficient 1) at capturing overlaps in-between communities, 2) at detecting communities having disparities in size and density 3) at taking into account the networks' dynamics. In this paper, we propose a new algorithm (iLCD) for community detection using a radically new approach...
The present paper addresses the problem of image segmentation evaluation by comparing four different approaches. We are introducing a new method of salient object recognition with very good results relative to other already known object detection methods. We developed a simple evaluation framework in order to compare the results of our method with other segmentation methods. The experimental results...
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