Novel Co-SIFT Detector for Scanned Images Differentiation

Stancelova, Paula; Cernekova, Zuzana; Ferko, Andrej

doi:10.1007/978-3-031-22025-8_7

Cited by 1 publication

(3 citation statements)

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“…Within a data state reference model in information visualization [ 17 ], we visualize and distinguish properties of scanning modes. This was done by transforming the image differencing problem to a well-studied assignment problem, using Harris corners [ 18 ] or Co-SIFT feature points [ 19 ] 3. We analyze the differences between various scans by multiple image quality metrics.…”

Section: Methodsmentioning

confidence: 99%

“…The wooden decors illustrate a sample from natural heritage [ 21 ]. From cultural heritage we selected hard-to-distinguish cases, such as ambrotypes, daguerreotypes [ 19 , 21 ], synagogue plans, or handmade papers [ 22 ]. For educational purposes, Comenius University students created virtual herbarium, 3D models using photogrammetry, depth of field, or shadows [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…The latter performed better on Cruse scans and already the density of Harris or coSIFT corners indicated “interesting parts” in scanned wooden decors. For more details on the Hungarian edges approach, refer to [ 19 , 21 ]. Methodologically, adding corners and edges can serve for image (or scanning mode) differentiation in terms of the data state reference model in information visualization [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

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Finding the Best Lighting Mode for Daguerreotype, Ambrotype, and Tintype Photographs and Their Deterioration on the Cruse Scanner Based on Selected Methods

Haladova

Bohdal

Cernekova

et al. 2023

Sensors

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In the digital heritage field, the accurate reproduction of hard-to-photograph items, such as daguerreotypes, ambrotypes, and tintypes, is an ongoing challenge. Industrial contactless sensors offer the potential to improve the quality of scanned images, but their capabilities and limitations have not been fully explored. In our research, a dataset of 48 scans was created using the hi-tech industrial contactless sensor CRUSE. Moreover, 3 rare original photographs were scanned in 16 different modes, the most suitable images were determined by specialists in the restoration, and validated through experiments involving eye-tracking, multiple computer vision, and image processing methods. Our study identified the Cruse scanning modes, which can be utilized to achieve the most accurate digital representation of scanned originals. Secondly, we proposed several methods for highlighting the degradation and minor scratches on photographs that otherwise might not be detected by the restorer’s naked eye. Our findings belong to four overlapping areas, i.e., image understanding, digital heritage, information visualization, and industrial sensors research. We publish the entire dataset under the CC BY-NC 4.0 license. The CRUSE sensor shows promise as a tool for improving the quality of scanned images of difficult-to-photograph items. Further research is necessary to fully understand its capabilities and limitations in this context.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%