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The impact of curviness on four different image sensor forms and structures
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.ORCID iD: 0000-0003-3887-5972
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.ORCID iD: 0000-0003-4327-117x
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 2, article id 429Article in journal (Refereed) Published
Abstract [en]

The arrangement and form of the image sensor have a fundamental effect on any further image processing operation and image visualization. In this paper, we present a software-based method to change the arrangement and form of pixel sensors that generate hexagonal pixel forms on a hexagonal grid. We evaluate four different image sensor forms and structures, including the proposed method. A set of 23 pairs of images; randomly chosen, from a database of 280 pairs of images are used in the evaluation. Each pair of images have the same semantic meaning and general appearance, the major difference between them being the sharp transitions in their contours. The curviness variation is estimated by effect of the first and second order gradient operations, Hessian matrix and critical points detection on the generated images; having different grid structures, different pixel forms and virtual increased of fill factor as three major properties of sensor characteristics. The results show that the grid structure and pixel form are the first and second most important properties. Several dissimilarity parameters are presented for curviness quantification in which using extremum point showed to achieve distinctive results. The results also show that the hexagonal image is the best image type for distinguishing the contours in the images. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 18, no 2, article id 429
Keywords [en]
Critical points, Curviness quantification, Fill factor, Grid structure, Hessian matrix, Hexagonal image, Pixel form, Software-based, Virtual, Image processing, Image sensors, Semantics, Grid structures, Hessian matrices, Pixels
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-15920DOI: 10.3390/s18020429ISI: 000427544000112Scopus ID: 2-s2.0-85041511195OAI: oai:DiVA.org:bth-15920DiVA, id: diva2:1184783
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open access

Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-04-26Bibliographically approved

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Wen, WeiKhatibi, Siamak

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