Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed generative sensing framework aims at transforming low-end, low-quality sensor data into higher quality sensor data in terms of achieved classification accuracy. The low-end data can be transformed into higher quality data of the same modality or into data of another modality. Different from existing methods for image generation, the proposed framework is based on discriminative models and targets to maximize the recognition accuracy rather than a similarity measure. This is achieved through the introduction of selective feature regeneration in a deep neural network (DNN). The proposed generative sensing will essentially transform low-quality sensor data into high-quality information for robust perception. Results are presented to illustrate the performance of the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-105
Number of pages6
ISBN (Electronic)9781538618578
DOIs
StatePublished - Jun 26 2018
Event1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018 - Miami, United States
Duration: Apr 10 2018Apr 12 2018

Other

Other1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018
CountryUnited States
CityMiami
Period4/10/184/12/18

Fingerprint

Sensors
Artificial intelligence
Deep neural networks
Deep learning

Keywords

  • classification
  • deep learning
  • generative sensing
  • image
  • infrared
  • IR
  • multi-modal
  • NIR
  • quality
  • recognition
  • resolution
  • visible

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Media Technology

Cite this

Karam, L., Borkar, T., Cao, Y., & Chae, J. (2018). Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition. In Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018 (pp. 100-105). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MIPR.2018.00025

Generative Sensing : Transforming Unreliable Sensor Data for Reliable Recognition. / Karam, Lina; Borkar, Tejas; Cao, Yu; Chae, Junseok.

Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 100-105.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Karam, L, Borkar, T, Cao, Y & Chae, J 2018, Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition. in Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018. Institute of Electrical and Electronics Engineers Inc., pp. 100-105, 1st IEEE Conference on Multimedia Information Processing and Retrieval, MIPR 2018, Miami, United States, 4/10/18. https://doi.org/10.1109/MIPR.2018.00025
Karam L, Borkar T, Cao Y, Chae J. Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition. In Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 100-105 https://doi.org/10.1109/MIPR.2018.00025
Karam, Lina ; Borkar, Tejas ; Cao, Yu ; Chae, Junseok. / Generative Sensing : Transforming Unreliable Sensor Data for Reliable Recognition. Proceedings - IEEE 1st Conference on Multimedia Information Processing and Retrieval, MIPR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 100-105
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