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Keywords = colour photographic process identification

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15 pages, 1905 KiB  
Article
On the Identification of Colour Photographic Processes
by Ambra Cattaneo, Beatrice Sarti, Alice Plutino and Alessandro Rizzi
Heritage 2022, 5(4), 4074-4088; https://doi.org/10.3390/heritage5040210 - 9 Dec 2022
Cited by 1 | Viewed by 1894
Abstract
To determine the best investigations for restoration and storage procedures, a visual inspection can provide a preliminary screen for the colour processes used to print the photographs. The high costs of the instruments required to follow the protocols present in the literature make [...] Read more.
To determine the best investigations for restoration and storage procedures, a visual inspection can provide a preliminary screen for the colour processes used to print the photographs. The high costs of the instruments required to follow the protocols present in the literature make these methodologies challenging to reproduce, especially for institutions with limited resources. Hence, a cheap and advanced investigation protocol is needed. This work proposes a protocol that, besides having this characteristic, observes the degradation of the material as a factor in identifying printing processes. The procedure proposed is composed of four steps: I. print observation: a preliminary examination of the object; II. surface observation: an examination of the surface; III. magnified observation: examination with microscope; and IV. decay and damage: alteration and degradation analysis. A set of photographs from the 1960s to the 2000s were analysed following the proposed protocol. From these prints, it was possible to observe the typical forms of degradation deriving from inappropriate conservation and determine the different materials and formats, proving the protocol’s effectiveness and easy applicability.In addition, the scientific community may access this protocol through the open-access website Colour photographic processes-Preliminary identification by visual exam. Full article
(This article belongs to the Special Issue Colour Photography and Film: Analysis, Preservation, and Conservation of Analogue and Digital Materials)
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19 pages, 18454 KiB  
Article
Extracting Quantitative Information from Images Taken in the Wild: A Case Study of Two Vicariants of the Ophrys aveyronensis Species Complex
by Anais Gibert, Florian Louty, Roselyne Buscail, Michel Baguette, Bertrand Schatz and Joris A. M. Bertrand
Diversity 2022, 14(5), 400; https://doi.org/10.3390/d14050400 - 19 May 2022
Cited by 9 | Viewed by 3612
Abstract
Characterising phenotypic differentiation is crucial to understand which traits are involved in population divergence and establish the evolutionary scenario underlying the speciation process. Species harbouring a disjunct spatial distribution or cryptic taxa suggest that scientists often fail to detect subtle phenotypic differentiation at [...] Read more.
Characterising phenotypic differentiation is crucial to understand which traits are involved in population divergence and establish the evolutionary scenario underlying the speciation process. Species harbouring a disjunct spatial distribution or cryptic taxa suggest that scientists often fail to detect subtle phenotypic differentiation at first sight. We used image-based analyses coupled with a simple machine learning algorithm to test whether we could distinguish two vicariant population groups of an orchid species complex known to be difficult to tease apart based on morphological criteria. To assess whether these groups can be distinguished on the basis of their phenotypes, and to highlight the traits likely to be the most informative in supporting a putative differentiation, we (i) photographed and measured a set of 109 individuals in the field, (ii) extracted morphometric, colour, and colour pattern information from pictures, and (iii) used random forest algorithms for classification. When combined, field- and image-based information provided identification accuracy of 95%. Interestingly, the variables used by random forests to discriminate the groups were different from those suggested in the literature. Our results demonstrate the interest of field-captured pictures coupled with machine learning classification approaches to improve taxon identification and highlight candidate traits for further eco-evolutionary studies. Full article
(This article belongs to the Section Biogeography and Macroecology)
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