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Considerations Regarding an Algebraic Model for Inference and Decision on Heterogeneous Sensory Input

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Soft Computing Applications (SOFA 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 356))

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Abstract

In this paper, we outline the foundations for a model for reasoning on images based on abstract concept and action representation via concept algebra. On performing object detection and recognition on image streams, the instances are mapped to ontology. Concept algebra rules and definitions of abstract notions, permit expressing image semantic and the making of further assumptions. This enables abstract reasoning on knowledge extracted or resulted from a cascade of deductions obtained from sets of images processed with different detection and recognition techniques. It also becomes possible to corroborate knowledge extracted from the image stream with information from heterogeneous sources, such as sensory input. Concept algebra reasoning aims to emulate human reasoning, including learning, but remains quantifiable, making way for verifiability in deductions.

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References

  1. Huang K, Murphy RF (2004) From quantitative microscopy to automated image understanding. J Biomed Opt 9(5):893–912

    Article  Google Scholar 

  2. Kulkarni AD (1993) Artificial neural networks for image understanding. John Wiley Sons Inc, New York

    MATH  Google Scholar 

  3. Grimnes M, Aamodt A (1996) A two-layered case-based reasoning architecture for image understanding. Adv Case-Based Reasoning, Lect Notes in Computer Science 1168:164–178

    Article  Google Scholar 

  4. Bichindaritz I (2012) Research themes in the case-based reasoning in health sciences core literature. In: Advances in data mining, applications and theoretical aspects. Lecture notes in computer science, vol 7377, pp 9–23

    Google Scholar 

  5. Perner P (2008) Case-based reasoning for signals and images. Springer, Berlin Heidelberg

    Book  MATH  Google Scholar 

  6. Bowyer KW, Hollingsworth K, Flynn PJ (2008) Image understanding for Iris biometrics: a survey. Comput Vis Image Underst 110(2):281–307

    Article  Google Scholar 

  7. Flom L, Safir A (1987) Iris recognition system. U.S. Patent 4,641,349

    Google Scholar 

  8. Daugman J (1994) Biometric personal identification system based on iris analysis. U.S. Patent No. 5,291,560

    Google Scholar 

  9. Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363

    Google Scholar 

  10. Weems C, Riseman E, Hanson A (1991) The DARPA image understanding benchmark for parallel computers. J Parallel Distrib Comput 11(1):1–24

    Article  Google Scholar 

  11. Sochera G, Sagerer G, Perona P (2000) Bayesian reasoning on qualitative descriptions from images and speech. Image Vis Comput 18(2):155–172

    Article  Google Scholar 

  12. Ogiela MR, Tadeusiewicz R (2002) Syntactic reasoning and pattern recognition for analysis of coronary artery images. Artif Intell Med 26(1–2):145–159

    Google Scholar 

  13. Tadeusiewicz R, Ogiela MR (2004) Medical image understanting technology: artificial intelligence and soft-computing for image understanding. Springer-Verlag, Berlin Heidelberg

    Book  MATH  Google Scholar 

  14. Knauff M (2009) A neuro-cognitive theory of deductive relational reasoning with mental models and visual images. Spat Cogn Comput: Interdisc J 9(2):109–137

    Google Scholar 

  15. Feldman J (2006) An algebra of human concept learning. J Math Psychol 50(4):339–368

    Article  MathSciNet  MATH  Google Scholar 

  16. Wang Y (2008) On concept algebra: a denotational mathematical structure for knowledge and software modelling. Int J Cogn Inform Nat Intell 2(2):1–19

    Article  Google Scholar 

  17. Wang Y (2006) On concept algebra and knowledge representation. In: 5th IEEE international conference on cognitive informatics, vol 1, pp 320–331

    Google Scholar 

  18. Wang Y (2010) On concept algebra for computing with words (CWW). Int J Seman Comput 4(3):331

    Article  MATH  Google Scholar 

  19. Wang Y, Tian Y, Hu K (2011) Semantic manipulations and formal ontology for machine learning based on concept algebra. Int J Cogn Inform Nat Intell 5(3):1–29

    Article  Google Scholar 

  20. Hu K, Wang Y (2007) A web knowledge discovery engine based on concept algebra. In: Canadian conference on electrical and computer engineering, pp 1255–1258

    Google Scholar 

  21. Tian Y, Wang Y, Hu K (2009) A knowledge representation tool for autonomous machine learning based on concept algebra. Trans Comput Sci V, Lecture Notes in Computer Science 5540:143–160

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. SOP HRD/159/1.5/S/133675 Project, Institute of Computer Science, Romanian Academy, Iasi Branch, Iasi, Romania

    Violeta Tulceanu

  2. “Al.I.Cuza” University of Iasi, General Berthelot Street no. 16, 700483, Iasi, Romania

    Violeta Tulceanu

Authors
  1. Violeta Tulceanu
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Correspondence to Violeta Tulceanu .

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Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  2. Faculty of Science and Technology, Data Science Institute, Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. University of Belgrade, Belgrade, Serbia

    Branko Kovačević

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Tulceanu, V. (2016). Considerations Regarding an Algebraic Model for Inference and Decision on Heterogeneous Sensory Input. In: Balas, V., C. Jain, L., Kovačević, B. (eds) Soft Computing Applications. SOFA 2014. Advances in Intelligent Systems and Computing, vol 356. Springer, Cham. https://doi.org/10.1007/978-3-319-18296-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-18296-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18295-7

  • Online ISBN: 978-3-319-18296-4

  • eBook Packages: EngineeringEngineering (R0)

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