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Cultural heritage is increasingly put through imaging systems such as multispectral cameras and 3D scanners. Though these acquisition systems are often used independently, they collect complementary information (spectral vs. spatial) used for the study, archiving and visualization of cultural heritage. Recording 3D and multispectral data in a single coordinate system enhances the potential insights in data analysis.We present the state of the art of such acquisition systems and their applications for the study of cultural heritage. We also describe existing registration techniques that can be used to obtain 3D models with multispectral texture and explore the idea of optically tracking acquisition systems to ensure an easy and precise registration.

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Modern optical measuring systems are able to record objects with high spatial and spectral precision. The acquisition of spatial data is possible with resolutions of a few hundredths of a millimeter using active projection-based camera systems, while spectral data can be obtained using filter-based multispectral camera systems that can capture surface spectral reflectance with high spatial resolution. We present a methodology for combining data from these two discrete optical measuring systems by registering their individual measurements into a common geometrical frame. Furthermore, the potential for its application as a tool for the non-invasive monitoring of paintings and polychromy is evaluated. The integration of time-referenced spatial and spectral datasets is beneficial to record and monitor cultural heritage. This enables the type and extent of surface and colorimetric change to be precisely characterized and quantified over time. Together, these could facilitate the study of deterioration mechanisms or the efficacy of conservation treatments by measuring the rate, type, and amount of change over time. An interdisciplinary team of imaging scientists and art scholars was assembled to undertake a trial program of repeated data acquisitions of several valuable historic surfaces of cultural heritage objects. The preliminary results are presented and discussed.

We present a technique for the multi-sensor registration of featureless datasets based on the photogrammetric tracking of the acquisition systems in use. This method is developed for the in situ study of cultural heritage objects and is tested by digitizing a small canvas successively with a 3D digitization system and a multispectral camera while simultaneously tracking the acquisition systems with four cameras and using a cubic target frame with a side length of 500 mm. The achieved tracking accuracy is better than 0.03 mm spatially and 0.150 mrad angularly. This allows us to seamlessly register the 3D acquisitions and to project the multispectral acquisitions on the 3D model.

In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.

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