Environmental Object Recognition in a Natural Image

Author:

Aryal Jagannath1,Josselin Didier2

Affiliation:

1. University of Tasmania, Australia

2. University of Avignon, France

Abstract

Natural images, which are filled with intriguing stimuli of spatial objects, represent our cognition and are rich in spatial information. Accurate extraction of spatial objects is challenging due to the associated spatial and spectral complexities in object recognition. In this paper, the authors tackle the problem of spatial object extraction in a GEOgraphic Object Based Image Analysis framework taking psychological and mathematical complexities into account. In doing so, the authors experimented with human and GEOBIA based recognition and segmentation in an image of an area of natural importance, the Ventoux Mountain, France. Focus was given to scales, color, and texture properties at multiple levels in delineating the candidate spatial objects from the natural image. Such objects along with the original image were provided to the human subjects in two stages and three different groups of samples. The results of two stages were collated and analyzed. The analysis showed that there exist different ways to comprehend the geographical objects according to priori knowledge.

Publisher

IGI Global

Reference29 articles.

1. Aryal, J. (2013). Advancing in spatial object recognition: GEOBIA approach. In Geographic Information Science at the Heart of Europe. Proceedings of the AGILE'2013 International Conference on Geographic Information Science, Leuven. Retrieved October 10th, 2013, http://www.agile-online.org/Conference_Paper/CDs/agile_2013/Posters/P_Aryal.pdf

2. Aryal, J., & Josselin, D. (2012). Association of spatial object recognition and GEOBIA: An experimental approach. In Multidisciplinary Research on Geographical Information in Europe and Beyond. Proceedings of the AGILE'2012 International Conference on Geographic Information Science, Avignon, France (pp. 19-24).

3. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

4. Object based image analysis for remote sensing

5. Accuracy Assessment Measures for Object-based Image Segmentation Goodness

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