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We present a no-reference (NR) image quality assessment (IQA) algorithm that is inspired by the representation of visual scenes in the primary visual cortex of the human visual system. Specifically, we use the sparse coding model of the area V1 to construct an overcomplete dictionary for sparsely representing pristine (undistorted) natural images. First, we empirically demonstrate that the distribution...
Large scale network has the characteristics of large number of nodes and complex structure, which makes it difficult to display in limited space. The paper proposes an expandable community division method for network visualization. The method use community detection algorithm based on network modularity to detect the network node and greedy algorithm to find the maximum modularity community. Different...
Node occlusion and edge congestion problems, which are caused by the increasing scale and complexity of network, had become a hot spot in network visualization research. To solve the visual clutter problem in network, edges close to each other in network were bundled by curving them. A segmental forced-directed algorithm (FDA) simplification model and a community based compatible edge bundling network...
The diabetic foot syndrome (DFS) is linked to loss of neuron functions, implying that the patients do not feel their feet and may unknowingly injure themselves or apply excessive plantar pressure. Such patients are at 17-40 times higher risk of foot amputation than non-diabetics. Sensor-equipped insoles are being developed to warn diabetics against inadverted excessive pressure. For the successful...
The huge amount of redundant multimedia data, like video, has become a problem in terms of both space and copyright. Usually, the methods for identifying near-duplicate videos are neither adequate nor scalable to find pairs of similar videos. Similarity self-join operation could be an alternative to solve this problem in which all similar pairs of elements from a video dataset are retrieved. Nonetheless,...
In medical information retrieval research, automatically classifying X-ray images based on body-parts is a challenging problem. In ImageCLEF's 2015 campaign there was a contest where the participants were challenged to cluster X-ray images into different groups based on presence of particular body-part in that X-ray image. In brief the challenge was to classify given X-ray images primarily into five...
In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted L1 minimization. Then a heuristic approach is provided...
We present a system for the acquisition, analysis and visualisation of Twitter data. Twitter messages are harvested and stored in a distributed cluster, and the data is processed using algorithms implemented in a MapReduce framework. We present a clustering algorithm capable of identifying the main topics of interest in a tweet data set. Also, we designed a visualization method which allows to follow...
To help the work of TTCN-3 test system architects we created a tool that is able to visualize large test systems, fitting to daily work practices. We defined and implemented 2 layouts, clustering and interaction methods. We also show 2 structuring methods, that are not yet supported by the TTCN-3 standard. With this tool we have analyzed all standardized test suites available at www.ttcn3.org, finding...
To effectively and efficiently retrieve desired images from a large image database, an intuitional and common type of approaches is text-based image retrieval which accesses the images by comparing conceptual terms of a query and image data. Unfortunately, this type of image retrieval is not easy to earn users' satisfactions due to the problem of the image database maintenance. Another useful type...
Augmented Reality (AR) browsers show geo-referenced data in the current view of a user. When the amount of data grows too large, the display quickly becomes cluttered. Clustering items by spatial and semantic attributes can temporarily alleviate the issue, but is not effective against an increasing amount of data. We present an adaptive information density display for AR that balances the amount of...
The enormous growth of data in the last decades led to big data challenge in the network security field. Traditional visual analysis method for large-scale network exploration is inadequate. Efficient methods for visual clutter reduction, network structure exploration and network behavior detection are needed. In this paper, we propose two methods: Enhanced Histogram Brush (EHB) and Flow-based Fast...
The main information of image focus in the target area, and the rest part contains a large amount of redundancy. The image segmentation is an important technology in image processing. This paper presents an improved rough set image segmentation algorithm, which is based on the theory of fuzzy C-means clustering, the human visual attention model and relative position. Combination of fuzzy clustering...
This paper focuses on the study of combination of some of the existing clustering methods and utilizing them meaningfully for feature dimensionality reduction. The efficacy of different cluster validity indices is investigated in selecting good features. A fuzzy ART network is used for experimentation. Colon dataset, wine dataset and iris dataset are used in this study to see the effectiveness of...
Segmentation is a process to obtain the desirable features in image processing. However, the existing techniques that use the multilevel thresholding method in image segmentation are computationally demanding due to the lack of an automatic parameter selection process. This paper proposesan automatic parameter selection technique called an automatic multilevel thresholding algorithm using stratified...
In this paper, we shall critically appraise sparse representation based denoising applications. An essential task for this framework is dictionary learning. Our novel proposition involves learning such a dictionary not only by analyzing the distribution of training data in the metric space but also exploiting local nature of the visual scene. Subsequently, the learning scheme is further developed...
With the development of single-person analysis in computer vision, social group analysis has received growing attention as the next area of research. In particular, group detection has been actively studied as the first step of social analysis. Here, group means an F-formation, that is, a spatial organization of people gathered for conversation. Popular group detection methods are based on coincidences...
This paper presents a framework of automatic clustering to determine correctly matched keypoints locations in aerial images for visual-based attitude estimation. In this work, correct and false matches are automatically identified using a clustering technique which utilizes the outlier information to determine the initial number of clusters and cross-correlation. The proposed framework has been tested...
We present an image classification method which consists of salient region (SR) detection, local feature extraction, and pairwise local observations based Naive Bayes classifier (NBPLO). Different from previous image classification algorithms, we propose a scale, translation, and rotation invariant image classification algorithm. Based on the discriminative pairwise local observations, we develop...
Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, the TBIR applications still suffer from the deficient and inaccurate tags provided by users. Inspired by the subspace clustering methods, we formulate the tag completion problem in a subspace clustering model which assumes that images are sampled...
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