Authors:
Alia Aljasmi
1
and
Andrzej Śluzek
2
Affiliations:
1
Khalifa University, Abu Dhabi and U.A.E.
;
2
Khalifa University, Abu Dhabi, U.A.E., Warsaw University of Life Sciences-SGGW, Warsaw and Poland
Keyword(s):
Thermal Images, MSER, Object Detection, Shape Descriptors, Object Classification.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Perception and Awareness
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
In this paper, the problem of multi-class object recognition in thermal images is discussed. An alternative model of thermal objects is investigated, where an object is represented by multiple shapes extracted by MSER detectors. The shapes are nested within the largest MSER outlining the object (which might be the actual outline of the object, the outline of its thermal footprint or the outline of its largest prominent fragment). We show, using a multi-class dataset of thermal images captured in indoor environments, that the proposed methodology is a feasible solution for various object classification problems in thermal imaging. In particular, no object-specific algorithms are needed, so that the method is applicable to most of typical applications of thermal cameras (subject to general limitations of data captured by thermal imaging devices). The presented work is considered a preliminary feasibility study exploring potentials an limits of thermal image classification in more sophi
sticated machine vision problems.
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