visual big data analytics aims at supporting bigdataanalytics via visual metaphors, with a plethora of applications in modern settings and scenarios. In all these domains, visual big data analytics paradigms offer s...
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ISBN:
(纸本)9781728191348
visual big data analytics aims at supporting bigdataanalytics via visual metaphors, with a plethora of applications in modern settings and scenarios. In all these domains, visual big data analytics paradigms offer several advantages, among which some noticeable ones are: (i) fast knowledge understanding from bigdata sets;(ii) pattern and trend discovery from bigdata sets;(iii) entity and model discovery from bigdata sets;(iv) sharing insights among organizations. Among several proposals, OLAP-based visual big data analytics methodologies and tools represents a successful case of visual big data analytics frameworks, which is entirely based on OLAP analysis. In this context, an OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. On the basis of these considerations, in this paper we propose an innovative OLAP-shaped visual big data analytics framework that incorporates a state-of-the-art statistical technique for supporting exploration and visualization of OLAP data cubes. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.
The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The tra...
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ISBN:
(纸本)9781509061679
The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The traditional computer vision techniques are unable to analyze such a huge amount of visualdata generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visualdata such as images or videos to find semantic patterns that are useful for interpretation. In this paper, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research.
The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The tra...
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ISBN:
(纸本)9781509061686
The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The traditional computer vision techniques are unable to analyze such a huge amount of visualdata generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visualdata such as images or videos to find semantic patterns that are useful for interpretation. In this paper, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research.
This paper reports on a novel application of computer vision and image processing technologies to an interdisciplinary project in architectural history that seeks to help identify and visualize differences between hom...
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ISBN:
(纸本)9781467399616
This paper reports on a novel application of computer vision and image processing technologies to an interdisciplinary project in architectural history that seeks to help identify and visualize differences between homologous buildings constructed to a common template design. By identifying the mutations in homologous buildings, we assist humanists in giving voice to the contributions of the myriad additional "authors" for these buildings beyond their primary designers. We develop a framework for comparing 3D point cloud representations of homologous buildings captured using lidar: focusing on identifying similarities and differences, both among 3D scans of different buildings and between the 3D scans and the design specifications of architectural drawings. The framework addresses global and local alignment for highlighting gross differences as well as differences in individual structural elements and provides methods for readily highlighting the differences via suitable visualizations. The framework is demonstrated on pairs of homologous buildings selected from the Canadian and Ottoman rail networks. Results demonstrate the utility of the framework confirming differences already apparent to the humanist researchers and also revealing new differences that were not previously observed.
This paper reports on a novel application of computer vision and image processing technologies to an interdisciplinary project in architectural history that seeks to help identify and visualize differences between hom...
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ISBN:
(纸本)9781467399623
This paper reports on a novel application of computer vision and image processing technologies to an interdisciplinary project in architectural history that seeks to help identify and visualize differences between homologous buildings constructed to a common template design. By identifying the mutations in homologous buildings, we assist humanists in giving voice to the contributions of the myriad additional "authors" for these buildings beyond their primary designers. We develop a framework for comparing 3D point cloud representations of homologous buildings captured using lidar: focusing on identifying similarities and differences, both among 3D scans of different buildings and between the 3D scans and the design specifications of architectural drawings. The framework addresses global and local alignment for highlighting gross differences as well as differences in individual structural elements and provides methods for readily highlighting the differences via suitable visualizations. The framework is demonstrated on pairs of homologous buildings selected from the Canadian and Ottoman rail networks. Results demonstrate the utility of the framework confirming differences already apparent to the humanist researchers and also revealing new differences that were not previously observed.
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