Object Recognition: Distributed Architecture Based on Heterogeneous Devices to Integrate Sensor Information

2019 ◽  
pp. 181-188
Author(s):  
Jose-Luis Poza-Lujan ◽  
Juan-Luis Posadas-Yagüe ◽  
Eduardo Munera ◽  
Jose E. Simó ◽  
Francisco Blanes
Keyword(s):  
Object Recognition ◽  
Distributed Architecture ◽  
Heterogeneous Devices ◽  
Sensor Information ◽  
Integrate Sensor

scholarly journals Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 112 ◽  
Author(s):  
Jose-Luis Poza-Lujan ◽  
Juan-Luis Posadas-Yagüe ◽  
José-Enrique Simó-Ten ◽  
Francisco Blanes
Keyword(s):  
Object Recognition ◽  
Smart Cities ◽  
Image Sensors ◽  
Sensor Data ◽  
Intelligent Environments ◽  
Distributed Information ◽  
Information Object ◽  
Environment Map ◽  
Recognition Of Objects ◽  
Integrate Sensor

Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.

scholarly journals Smart Resources for Smart Cities: Distributed Architecture to Integrate Sensor Information

2019 ◽  
Author(s):  
Jose-Luis Poza-Lujan ◽  
Juan-Luis Posadas-Yagüe ◽  
Jose-Enrique Simó-Ten ◽  
Juan Francisco Blanes Noguera
Keyword(s):  
Industry 4.0 ◽  
Smart Cities ◽  
Image Sensors ◽  
Sensor Data ◽  
Intelligent Environments ◽  
Distributed Information ◽  
Environment Map ◽  
Recognition Of Objects ◽  
Sensor Information ◽  
Integrate Sensor

Objects recognition is a necessary task in smart city environments. This recognition can be used in processes such as the reconstruction of the environment map or the intelligent navigation of vehicles. This paper proposes an architecture that integrates heterogeneous distributed information to recognize objects in intelligent environments. The architecture is based on the IoT / Industry 4.0 model to interconnect the devices, called Smart Resources. Smart Resources can process local sensor data and send information to other devices. These other devices can be located in the same operating range, the Edge, in the same intranet, the Fog, or on the Internet, the Cloud. Smart Resources must have an intelligent layer in order to be able to process the information. A system with two Smart Resources equipped with different image sensors has been implemented to validate the architecture. Experiments show that the integration of information increases the certainty in the recognition of objects between 2\% and 4\%. Consequently, in the field of intelligent environments, it seems appropriate to provide the devices with intelligence, but also capabilities to collaborate closely with other devices.

An Improved Close Value Method for Multi-Sensor Object Recognition

2014 ◽  
Vol 707 ◽  
pp. 487-490
Author(s):  
Chang Qing Zhang
Keyword(s):  
Decision Making ◽  
Object Recognition ◽  
Information Fusion ◽  
Information Entropy ◽  
Recognition Method ◽  
Multi Attribute Decision Making ◽  
Sensor Information

Multi-sensor information fusion problem contains many characteristic indexes, and thus it can be resolved using a multi-attribute decision making method. Information entropy is used to objectively determine the attributes weights, and thus it can overcome the subjective randomness. The aim of this paper is to develop a new multi-sensor object recognition method based on close value method. The example of part recognition proves that the proposed method is both feasible and effective.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

Sign in / Sign up

Export Citation Format

Share Document