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Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective of this study is to establish a rigorous procedure, from the colorimetric point of view, for the characterization of cameras, following different polynomial models. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. In this article we report on pyColorimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l' Eclairage (CIE). This software allows users to control the entire digital image processing and the colorimetric data workflow, including the rigorous processing of raw data. We applied the methodology on a picture targeting Levantine rock art motifs in Remigia Cave (Spain) that is considered part of a UNESCO World Heritage Site. Three polynomial models were tested for the transformation between color spaces. The outcomes obtained were satisfactory and promising, especially with RAW files. The best results were obtained with a second-order polynomial model, achieving residuals below three CIELAB units. We highlight several factors that must be taken into account, such as the geometry of the shot and the light conditions, which are determining factors for the correct characterization of a digital camera. K E Y W O R D S

Highlights  Convergent SfM provides reliable DEMs for microscale geomorphic change detection.  Reliable results require rigorous DEMs georeferencing in a local reference system.  Comparison with dragged sediment in runoff shows good agreement.  Basic LOD min thresholding shows right performance to detect tiny changes.

Smartphones have widened the possibilities for low-cost close-range image acquisition for three-dimensional (3D) modelling. They allow the rapid acquisition of large amounts of data for a wide range of applications. However, the accuracy of the models and the automation possibilities depend on the image acquisition conditions and application requirements. In this study, the accuracy and reliability of the derived photogrammetric 3D models are evaluated on a spherical set-up for close-range applications (c.30 cm). Different numbers of images, network configurations, targets, devices and camera calibration methodologies are tested and evaluated. Results show that for this close-range application high accuracy (0Á2 mm) and reliability can be achieved. The number of images did not significantly affect the accuracy but was vital for tie-point detection and image orientation. The use of artificial targets was found to be the key factor in increasing the final accuracy. In contrast, the image calibration strategy and the characteristics of the imaging device did not have a great impact on the results.Smartphone video (more specifically slow-motion video) is a useful tool for acquiring large numbers of images, suitable even for fast moving objects. These images can be used for the creation of 3D models of moving objects (Barbero-Garc ıa et al., 2017). With an image acquisition speed of 240 frames per second (fps) of many smartphones (still far below the ultra-high-speed cameras that reach up to 2000 fps), the computational cost is the main limitation given the number of images to be used for 3D modelling.Despite their advantages, smartphone cameras present high internal instability that hampers their correct calibration. This problem is common to all non-metric digital cameras (Fraser, 2013), but is especially exacerbated when working with smartphones. The radiometric accuracy of smartphone cameras is lower than that of single-lens reflex (SLR) cameras but, despite their limitations, studies have concluded that these cameras can be used for photogrammetric tasks with a required accuracy of 1:10 000 (Akca and Gruen, 2009).The development of useful tools, which could allow non-expert users to obtain accurate 3D models for different purposes, requires a high degree of automation (Remondino et al., 2014). However, most of the available automatic low-cost solutions provide low repeatability and reliability (Remondino et al., 2012). The development of fully automatic and reliable solutions for specific applications requires an extensive knowledge of the factors affecting the quality of the 3D models created using smartphones or other similar imaging devices, such as tablets. The most important factors include the determination of the ideal geometric network, the selection of the best video frames and their optimal number, as well as the accuracy requirements for camera calibration. These parameters can vary greatly depending on the characteristics and limitations of the image acquisition process for a specific application (suc...

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The use of smartphones cameras for photogrammetric purposes is increasing. However, the suitability of smartphones for 3D modelling for medical purposes in general, and for cranial deformation in particular, is still to be analysed. This paper investigates the suitability of smartphone video cameras to create 3D models for cranial deformation analysis compared to the digital single-lens reflex (SLR) cameras traditionally used in close-range photogrammetry. Two models are obtained, the first one from a slow-motion video recorded with a smartphone, and the second one from SLR camera imagery. The models are compared to evaluate the differences not only between themselves but also through the best fitting ellipsoid that allow the determination of the cranial deformations. The average distance between models is 0.5 mm, and below 1 mm for 86% of the model points. The maximum difference between the two fitted ellipsoid semiaxes is 1 mm. It can be stated that smartphones are a low-cost solution that can provide 3D models with a similar accuracy to that of SLR cameras for non-static objects in close range scenarios. More interestingly, slow-motion videos provide comparable results in real clinical conditions with infants in movement.