Interactive multilevel focus+context visualization framework

In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.     

Outlier-preserving focus+context visualization in parallel coordinates

Focus+context visualization integrates a visually accentuated representation of selected data items in focus (more details, more opacity, etc.) with a visually deemphasized representation of the rest of the data, i.e., the context. The role of context visualization is to provide an overview of the data for improved user orientation and improved navigation. A good overview comprises the representation of both outliers and trends. Up to now, however, context visualization not really treated outliers sufficiently. In this paper we present a new approach to focus+context visualization in parallel coordinates which is truthful to outliers in the sense that small-scale features are detected before visualization and then treated specially during context visualization. Generally, we present a solution which enables context visualization at several levels of abstraction, both for the representation of outliers and trends. We introduce outlier detection and context generation to parallel coordinates on the basis of a binned data representation. This leads to an output-oriented visualization approach which means that only those parts of the visualization process are executed which actually affect the final rendering. Accordingly, the performance of this solution is much more dependent on the visualization size than on the data size which makes it especially interesting for large datasets. Previous approaches are outperformed, the new solution was successfully applied to datasets with up to 3 million data records and up to 50 dimensions.     

Feature-preserving volume data reduction and focus+context visualization

The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.     

SPDW+: a seamless approach for capturing quality metrics in software development environments

Among the key factors for the success of a metrics program are the regularity of metrics collection, a seamless and efficient data collection methodology, and the presence of non-intrusive automated data collection tools. This paper presents the software process data warehousing architecture SPDW+ as a solution to the frequent, seamless, and automated capturing of software quality metrics, and their integration in a central repository for a full range of analyses. The striking features of the SPDW+ ETL (data extraction, transformation, and loading) approach are that it addresses heterogeneity issues related to the software development context, it is automatable and non-intrusive, and it allows different capturing frequency and latency strategies, hence allowing both analysis and monitoring of software metrics. The paper also provides a reference framework that details three orthogonal dimensions for considering ETL issues in the software development process context, used to develop SPDW+ ETL. The advantages of SPDW+ are: (1) flexibility to meet the requirements of the frequent changes in SDP environments; (2) support for monitoring, which implies the execution of frequent and incremental loads; (3) automation of the complex and time-consuming task of capturing metrics, making it seamless; (4) freedom of choice regarding management models and support tools used in projects; and (5) cohesion and consistency of the information stored in the metrics repository which will be used to compare data of different projects. The paper presents the reference framework, illustrates the key role played by the metrics capturing process in a metrics program using a case study, and presents the striking features of SPDW+ and its ETL approach, as well as an evaluation based on a prototype implementation.     

LNMF: lightweight network for multi-focus image fusion

Multimedia Tools and Applications - Due to limitations of the optical lenses, using the digital single-lens camera to obtain an all-in-focus image is difficult. In order to overcome this...     

Team cognition in a cyber defense context: focus on social support behaviors

In the face of increasingly prominent cyber security issues, the organization of cyber team analysts has become crucial to thwart cyber threats. Few studies have examined the functioning of the team and the interaction between individuals in a cyber defense context and how the context influences team adaptation. The present study investigates team cognition in a cyber defense context and in particular the nature of task- or team-centered communication among analysts during a cyber defense simulation exercise. Results indicate that markers of situation assessment and shared mental models are both strategically present and linked. Nevertheless, the frequency of these markers varies depending on the quantity and quality of problems encountered; in particular, variations in social support behaviors are observed. Decreasing social support behaviors during high level activities suggests the adaptation of social behaviors depending on the threats and attacks on the system. Theoretical and practical implications are discussed in terms of theories and potential consequences for strategic adaptation and team resilience.

     

SESF-Fuse: an unsupervised deep model for multi-focus image fusion

Neural Computing and Applications - Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects...     

Inferring context with reliable collaborators: a novel similarity estimation method for recommender systems

Applied Intelligence - Additional context information is vital for context-aware recommender systems. The whole paradigm of context-aware recommender systems is built upon the availability of...     

MSIMCNN: Multi-scale inception module convolutional neural network for multi-focus image fusion

The aim of image fusion is to obtain a clear image by combining useful information coming from multiple images. However, the fused image usually has the problem of artifacts and unclear boundary. To address these problems, a deep convolutional neural network based framework for multi-focus image fusion is proposed in this paper, called multi-scale inception module convolutional neural network (MSIMCNN). MSIMCNN converts the entire image into a binary mask to estimate the focus characteristics, and obtains the clear boundary between focus and defocus. First of all, a pair of focus images and the corresponding feature images detected by the Laplace operator are inputted into the network. The Laplace operator can detect the edge and gradient of focus in the image, which can help us accurately reconstruct the focused area in the focus map and distinguish the focus and defocus boundaries. Then, in the feature extraction stage, different scales of convolution kernels are designed to extract the rich and complementary features at different scales of the source images. At the same time, the inception module is added to increase the width of the network and reduce the parameters, which can extract more focus features required for image reconstruction and reduce the complexity. Finally, the focus map of the source image pair can be obtained in the feature reconstruction stage. In this stage, an efficient method is proposed to make the focus mask, which is used for the calculation of the loss function and the generation of the training set. The experimental results on different data sets confirm the superiority and effectiveness of MSIMCNN compared with other methods.

     

The smart classroom: merging technologies for seamless tele-education

The smart classroom integrates voice-recognition, computer-vision, and other technologies to provide a tele-education experience similar to a real classroom experience.     

Aroundplot: Focus+context interface for off-screen objects in 3D environments

In exploring 3D environments from a first-person viewpoint, the narrow field-of-view makes it difficult to search for an off-screen object, a task that becomes even harder if the user is looking through the small screen of a mobile phone. This paper presents Aroundplot, a novel focus+context interface for providing multiple location cues for off-screen objects in an immersive 3D environment. One part of this technique is a mapping method from 3D spherical coordinates to 2D orthogonal fisheye, which tackles the problems of existing 3D location cue displays such as occlusion among the cues and discordance with the human frame of reference. The other part is a dynamic magnification method that magnifies the context in the direction the view is moving to alleviate the distortion of the orthogonal fisheye and thus to support precise movement. In an evaluation, the participants could find the target object for a given location cue faster and more accurately with Aroundplot than with a top-down 2D radar. They were more accurate with Aroundplot than with a 3D arrow cluster when the number of objects was large; however, accuracy with a small number of objects and the search speed with any number of objects were not significantly different.     

On the use of a joint spatial-frequency representation for the fusion of multi-focus images

This paper shows practical examples of the application of a new image fusion paradigm for achieving a 2-D all in-focus image starting from a set of multi-focus images of a 3-D real object. The goal consists in providing an enhanced 2-D image showing the object entirely in focus. The fusion procedure shown here is based on the use of a focusing pixel-level measure. Such measure is defined in the space–frequency domain through a 1-D pseudo-Wigner distribution. The method is illustrated with different sets of images. Evaluation measures applied to artificially blurred cut and pasted regions have shown that the present scheme can provide equally or even better performance than other alternative image fusion algorithms.     

Exploring context and content links in social media: a latent space method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.     

高精度光学自动检测仪快速对焦方法研究

为提高光学自动检测设备中自动对焦的准确性、快速性和稳定性,提出一种新型的高精度快速自动对焦技术方案.该系统利用光学三角法测距原理将汇聚到工作台上的激光光斑成像在CCD上,再利用光斑灰阶的变化实现精密对焦.实验结果表明:显微镜重复对焦精度可以达到0.1 μm,对焦时间小于200 ms,满足工业光学检测系统中实际应用的需要...     

iCROSS: toward a scalable infrastructure for cross-domain context management

With the development of pervasive computing techniques, the world will be filled with interconnected context-aware domains (e.g., homes, offices, hospitals, etc.). While the previous studies focused solely on the management of contexts produced in a single domain, in this paper we discuss the challenges to be addressed for cross-domain context management. By analyzing the requirements from several scenarios, we identify two context producer–consumer patterns in multi-domain environments. Furthermore, to deal with the mobile entity problem raised in cross-domain context sharing, a transparent query mechanism that enables applications to obtain context information about mobile entities from remote domains is proposed. Two prototype applications—smart home and community services in a smart campus—have been developed to demonstrate the key features and usefulness of cross-domain context management. Initial experiments have also been conducted to evaluate the performance of our system.     

Quality assurance: a shift in focus

Computers in Healthcare Associate Editor William A. Schmitt writes that quality assurance (QA) systems and philosophy has shifted from a punitive approach to focus instead on its more positive aspects. Schmitt also discovers that through the pressures of outside forces, QA may be one of the most important issues of the decade because the survival of many healthcare institutions may depend on it.