Abstract
The paper demonstrates how to transform (using a combination of techniques reported in our previous papers) a collection of random images gathered in an unknown environment into a limited-scale visual model of that environment. The model generally consists of the template images of the typical “visual objects” identified in the explored world. Both the concepts of objects and their templates are formed without any assumptions about the content of acquired images, i.e. the semantics is built using the pictorial data only (although users may subsequently identify the real-world semantics of the formed objects). From the image processing perspective, the method consists in detecting near-duplicate (i.e. photometric/geometric distortions and partial occlusions are allowed) fragments in random images. It is envisaged that such a proposal can be instrumental in assisting both autonomous agents and visually impaired humans (including both blind people and people unable to understand perceived visual data) facing unfamiliar worlds. The paper focuses on the practical aspects of the problem (exemplary results, computational efficiency, etc.) although a substantial amount of theoretical background is also included.
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Śluzek, A., Paradowski, M. (2012). Towards Vision-Based Understanding of Unknown Environments. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds) Human – Computer Systems Interaction: Backgrounds and Applications 2. Advances in Intelligent and Soft Computing, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23187-2_25
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DOI: https://doi.org/10.1007/978-3-642-23187-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23186-5
Online ISBN: 978-3-642-23187-2
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