IBM Cloud Computing Powering a Smarter Planet

Zhu, Jinzy; Fang, Xing; Guo, Zhe; Niu, Meng; Cheng, Fan; Yue, Shuang; Liu, Qin Yu

doi:10.1007/978-3-642-10665-1_62

Cited by 23 publications

(10 citation statements)

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“…During this stage, a structured CNN is used according to the Inception [11] model, in which a 42-layer deep neural architecture is presented, whose improvements over previous architectures such as GoogLeNet [12] or VGGNet [13] are mainly given by the factorization of the cores of convolution with the consequent spatial reduction of these filters, the use of auxiliary classifiers at the end of the training and the improvement of the techniques for the reduction of the amount of parameters to be learned.…”

Section: Feature Extractionmentioning

confidence: 99%

Method for the Recovery of Indexed Images in Databases from Visual Content

Viloria

Varela

Vargas

et al. 2020

Computational Methods and Data Engineering

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The techniques of content-based image recovery (CBIR) provide a solution to a problem of information retrieval that may arise as follows: from an image of interest to recover or obtain similar images from among those present in a large collection, using only features or features extracted from said images Banuchitra and Kungumaraj (Int J Eng Comput Sci (IJECS) 5 (2016) [1]). Similar images are understood as those in which the same object or scene is observed with variations in perspective, lighting conditions or scale. The stored images are preprocessed and then their corresponding descriptors are indexed. The query image is also preprocessed to extract its descriptor, which is then compared to those stored by applying appropriate similarity measures, which allow the recovery of those images that are similar to the query image. In the present work, a method was developed for the recovery of indexed images in databases from their visual content, without the need to make textual annotations. Feature vectors were obtained from visual contents using artificial neural network techniques with deep learning.

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Section: Feature Extractionmentioning

confidence: 99%

Method for the Recovery of Indexed Images in Databases from Visual Content

Viloria

Varela

Vargas

et al. 2020

Computational Methods and Data Engineering

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“…The initiative seeks to highlight how forward-thinking leaders in business, government and civil society around the world are capturing the potential of smarter systems to achieve economic growth, near-term efficiency, sustainable development and societal progress [69,98].…”

Section: A14 Ibm Smarter Planetmentioning

confidence: 99%

From GPS and virtual globes to spatial computing - 2020

2015

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Spatial computing is a set of ideas, solutions, tools, technologies, and systems that transform our lives with a new prospect of understanding, navigating, visualizing and using locations. In this community whitepaper, we present a perspective on the changing world of spatial computing, research challenges and opportunities and geoprivacy issues for spatial computing. First, this paper provides an overview of the changing world of spatial computing. Next, promising technologies that resulted from the integration of spatial computing in the everyday lives of people is discussed. This integration results with promising technologies, research challenges and opportunities and geoprivacy issues that must be addressed to achieve the potential of spatial computing.

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“…The idea is that these nanoscale sensors will quickly gather data and transmit it to powerful computing engines, which will analyze the information in real time for different applications and web services (Hartwell and Williams 2010). IMB's Smart Planet initiative is boosting IoT driven projects for water management systems, solutions to traffic congestion problems, greener buildings, and many others (Zhu et al 2009). …”

Section: The Internet Of Things and The Internet Of Placesmentioning

confidence: 99%

From paper maps to the Digital Earth and the Internet of Places

Brovelli

Zamboni

Muñoz

2015

Rend. Fis. Acc. Lincei

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Maps have always been tools that have fascinated men, for their ability to make us see the world that surrounds us. They were and are the outcome of models and methods applied to the observation of the world, starting from geodesy, surveying photogrammetry and remote sensing. All these disciplines, which we now group under the new name of geomatics, have had a tremendous boost in recent years. However, the synergy with information computer technology is probably the aspect that is revolutionizing more cartography. Earlier computers and after the Internet have brought us to new concepts and tools that will have profound effects not only in the world of niche of cartographers, but also more generally in the life of all human beings. The Digital Earth, proposed in 1998 by Al Gore, has been enriched in just twenty years of a set of new demands, which make even more interesting and challenging being cartographers today. The paper, without claiming to be comprehensive, aims at providing a concise overview of the state of art and of the advancement in this area. Moreover, it urges the community of geomatics to be protagonist and promoter of a new cartography, largely to be reinvented, and that would put us at the center of processes of knowledge and management of the Earth. The map makers in the past helped discovering new worlds, now the challenge is to rediscover our common world with new eyes of environmental, social, economic equity, sustainability and participation.

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IBM Cloud Computing Powering a Smarter Planet

Cited by 23 publications

References 0 publications

Method for the Recovery of Indexed Images in Databases from Visual Content

Method for the Recovery of Indexed Images in Databases from Visual Content

From GPS and virtual globes to spatial computing - 2020

From paper maps to the Digital Earth and the Internet of Places

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