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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=130485"><dc:title>Distance-dependent multimodal image registration for agriculture tasks</dc:title><dc:creator>Berenstein,	Ron	(Avtor)
	</dc:creator><dc:creator>Hočevar,	Marko	(Avtor)
	</dc:creator><dc:creator>Godeša,	Tone	(Avtor)
	</dc:creator><dc:creator>Edan,	Yael	(Avtor)
	</dc:creator><dc:creator>Ben-Shahar,	Ohad	(Avtor)
	</dc:creator><dc:subject>sensor registration</dc:subject><dc:subject>control points</dc:subject><dc:subject>artificial control points</dc:subject><dc:subject>sensor fusion</dc:subject><dc:description>Image registration is the process of aligning two or more images of the same scene taken at different times; from different viewpoints; and/or by different sensors. This research focuses on developing a practical method for automatic image registration for agricultural systems that use multimodal sensory systems and operate in natural environments. While not limited to any particular modalities; here we focus on systems with visual and thermal sensory inputs. Our approach is based on pre-calibrating a distance-dependent transformation matrix (DDTM) between the sensors; and representing it in a compact way by regressing the distance-dependent coefficients as distance-dependent functions. The DDTM is measured by calculating a projective transformation matrix for varying distances between the sensors and possible targets. To do so we designed a unique experimental setup including unique Artificial Control Points (ACPs) and their detection algorithms for the two sensors. We demonstrate the utility of our approach using different experiments and evaluation criteria.</dc:description><dc:date>2015</dc:date><dc:date>2021-09-15 11:37:48</dc:date><dc:type>Članek v reviji</dc:type><dc:identifier>130485</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
