Conference proceeding
REKRIATE: A Knowledge Representation System for Object Recognition and Scene Interpretation
INTELLIGENT CONTROL AND ADAPTIVE SYSTEMS, v 1196, pp 117-131
01 Feb 1990
Abstract
What humans actually observe and how they comprehend this information is complex due to Gestalt processes and interaction of context in predicting the course of thinking and enforcing one idea while repressing another. How we extract the knowledge from the scene, what we get from the scene indeed and what we bring from our mechanisms of perception are areas separated by a thin, ill-defined line. The purpose of this paper is to present a system for Representing Knowledge and Recognizing and Interpreting Attention Trailed Entities dubbed as REKRIATE. It will be used as a tool for discovering the underlying principles involved in knowledge representation required for conceptual learning. REKRIATE has some inherited knowledge and is given a vocabulary which is used to form rules for identification of the object. It has various modalities of sensing and has the ability to measure the distance between the objects in the image as well as the similarity between different images of presumably the same object. All sensations received from matrix of different sensors put into an adequate form. The methodology proposed is applicable to not only the pictorial or visual world representation, but to any sensing modality. It is based upon the two premises: a) inseparability of all domains of the world representation including linguistic, as well as those formed by various sensor modalities. and b) representativity of the object at several levels of resolution simultaneously.
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1 citations in Scopus
Details
- Title
- REKRIATE: A Knowledge Representation System for Object Recognition and Scene Interpretation
- Creators
- A Meystel - Drexel UniversityS Bhasin - Drexel UniversityX Chen - Drexel University
- Publication Details
- INTELLIGENT CONTROL AND ADAPTIVE SYSTEMS, v 1196, pp 117-131
- Publisher
- SPIE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Pathology (and Laboratory Medicine)
- Web of Science ID
- WOS:A1990BQ37K00011
- Scopus ID
- 2-s2.0-84958492883
- Other Identifier
- 991019173877204721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Electrical & Electronic
- Optics