2026-05-12T08:08:59Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112312026-01-17T01:05:30Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

Martínez Sanllorente, Jonás 649648df-13a5-4a89-b8c7-2c7f29284236 600 López Nozal, Carlos 322 600 0000-0001-8462-212X Latorre Carmona, Pedro 815 600 0000-0001-6984-5173 Marticorena Sánchez, Raúl 338 600 0000-0002-2633-635X 2026-01-16T12:12:39Z 2026-01-16T12:12:39Z 2025-09 2352-7110 https://hdl.handle.net/10259/11231 10.1016/j.softx.2025.102199 The automation of industrial quality control based on artificial (computer) vision can avoid some of the problems associated with tedious and repetitive manual procedures that will often originate operator errors. Automatic quality control can also be applied uninterruptedly. However, strategies of that sort have some drawbacks. One is associated with image acquisition under controlled illumination conditions. The material characteristics of an object for analysis will also influence the final result. For example, the illumination of metallic objects or objects with metallic finishes will generate specular reflection and shadow, which must be minimized. The illumination effect on subsequent processing stages may be analysed by applying segmentation techniques (based, for instance, on clustering strategies), to identify the number of objects. In this study, a MATLAB desktop application for image processing was developed, where illumination-invariant transforms were applied prior to image segmentation, to improve the quality of segmentation results. A set of illumination-invariant transforms and clustering-based segmentation methods were applied and the segmentation quality (if there was a groundtruth image) was quantified. The experimental results obtained with 4 illumination-invariant algorithms, 4 clustering-based segmentation algorithms, and 29 images of metal parts acquired by factory operators and manually segmented by researchers, demonstrated significant improvement to image segmentation following the application of illumination-invariant transforms. This work has been partially funded by project 𝑀� 𝐼� 𝐺�− 20221059, from the Centro para el Desarrollo Tecnológico y la Innovación (CDTI), and also partially by the indirect costs with code CILT.00 from the university of Burgos. application/pdf eng Elsevier SoftwareX. 2025, V. 31, p. 102199 https://doi.org/10.1016/j.softx.2025.102199 Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Image processing Image segmentation Illumination invariants Metallic objects Specular reflection Industrial manufacturing Automatización Automation Ingeniería industrial Industrial engineering InvIPM: Toolbox for segmentation optimization of images of metallic objects using illumination-invariant transforms info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion SoftwareX 31 102199 THUMBNAIL Martínez-SoftwareX_2025.pdf.jpg Martínez-SoftwareX_2025.pdf.jpg IM Thumbnail image/jpeg 4897 https://riubu.ubu.es/bitstream/10259/11231/4/Mart%c3%adnez-SoftwareX_2025.pdf.jpg 12f24dc926d59b689835e32195aa85ba MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/11231/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 914 https://riubu.ubu.es/bitstream/10259/11231/2/license_rdf 24013099e9e6abb1575dc6ce0855efd5 MD5 2 ORIGINAL Martínez-SoftwareX_2025.pdf Martínez-SoftwareX_2025.pdf application/pdf 2126637 https://riubu.ubu.es/bitstream/10259/11231/1/Mart%c3%adnez-SoftwareX_2025.pdf 6d04c03d4bfc09a162123b1af9e84b08 MD5 1 10259/11231 oai:riubu.ubu.es:10259/11231 2026-01-17 02:05:30.4 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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