Multimodal imaging is used by conservators and scientists to study the composition of paintings. To aid the combined analysis of these scans, such images must first be aligned. Rather than proposing a new domain-specific descriptor, we explore and evaluate how existing feature descriptors from related fields can improve the performance of feature-based painting scan registration. We benchmark these descriptors on pixel-precise, manually aligned scans of “Girl with a Pearl Earring” by Johannes Vermeer (c. 1665, Mauritshuis) and of “18th Century Portrait of a Woman”. As a baseline we compare against the well-established classical SIFT descriptor. We consider two recent descriptors: the handcrafted multimodal MFD descriptor, and the learned unimodal SuperPoint descriptor. Experiments show that SuperPoint starkly increases description matching accuracy by 40% for modalities with little modality-specific artefacts. Further, performing craquelure segmentation and using the MFD descriptor results in significant description matching accuracy improvements for modalities with many modality-specific artefacts.

This research investigates the efficacy and reliability of geometric matching for the specific case of aligning non-exact copies of artistic works with the original from which they were derived. The purpose of which is to provide a foundation for comparison in any further analysis conducted by conservators and art historians. An overview of the image alignment field as a whole is provided as well as a discussion of the particulars of aligning artworks. A variety of demonstrative results indicate the strengths and weaknesses of the technique in this domain and are accompanied by a discussion of the headline conclusions.

Aligning sketches to their corresponding painting could give more insight into the creative process of an artist. This is a difficult task that cannot be solved directly with classical image registration techniques. Typically, features such as cracks and brushstrokes are used to match different modalities. A sketch does not contain such artifacts and is often roughly similar to the final painting. Therefore the following question rises what suitable feature detection, feature extraction/description, and transform model estimation methods can be used to align sketches to their corresponding painting. This paper provides a proof of concept by taking a manual feature detection, and a histogram of orientations as a feature description. We demonstrate that our algorithm can automatically align sketches with up to 1.4 percent of the accuracy of manual alignment.

The current state-of-the-art image alignment techniques and their input parameters are often unintuitive to those without the required background knowledge.
This work aims to provide users with a graphical user interface through which they can intuitively influence the parameters and results of the algorithm by excluding certain areas from the images.
The application is also capable of automatically optimizing the parameters of the underlying registration algorithm.
Experimental results indicate that the automatic parameter optimization results in alignments at least as good as the default parameters, but that the introduced user interaction does not provide any major benefits.

Image registration is the process that overlays two or more images from different sources taken at different times and angles. Art conservators take various scans of paintings and then register them against the original in order to learn more about the working style of the artist, materials used and physical changes throughout time. This paper describes how scale space theory could be applied with a variant of cross-correlation to find the right scale at which to register the input images. From there, the standard state-of-the-art approach is used to register the images. This added step allows automatically registering a wider range of images.

In this paper, we investigate the problem of segmenting the foreground objects from the known background. The target application is a setting where a large display serves as the backdrop for a scene. The method proposed in this paper solves this problem in two steps. We try to separate the foreground and the background with the modified background subtraction base technique, which involves taking the per-pixel difference of the image with and without the foreground. The modification includes blurring the image before taking the difference and finding contours to separate noise and small objects in the background from the foreground. If the background subtraction fails, the chroma-key method is used: we put the solid colour screen behind the foreground object to ease the separation. To minimize the adverse effects of the chroma-key on the lighting of the objects, we propose the method for the background screen minimization using the pinhole camera theory.

We present a technique for enhancing high-dynamic-range tone mapping algorithms by adding the bloom effect to bright areas. Bloom is based on the fact that real-life lenses convolve light and make bright areas emit a glow. The algorithm takes a set of images with different exposures as input, and performs a tone mapping algorithm on these. It then takes the image with the lowest exposure value to create the bloom effect. It then perform a convolution on this image with with a kernel that represents the response to one point of light. The resulting image is then added on top of the tone mapped image. We also present parameters to change the spread of the glowing effect, how bright an area needs to be to get a significant glow, and the intensity of the glow when applied. Furthermore, the kernel can be changed to create different types of glow and highlights. These things make the technique versatile and allows the photographer to customize the effect.

In this paper, a method is proposed to artificially expand the dynamic range of screens with a limited dynamic range. This research is linked to a new film-making technology where, instead of using a green screen, the background of a scene is displayed on a screen in real time using a computer generated background. This provides real time lighting in the studio; however, due to the limited dynamic range of the screen, it can not fully replicate the brightness of light sources. Overcoming this problem involves capturing and synchronize frames that each display a small section of the wider dynamic range, defined as illumination maps. The method uses a pipeline in which the illumination maps are displayed on a monitor in a grouped order, which are then captured with a camera. The recording is processed by labeling the frames and selecting key frames. The key frames are then additively combined with compatible illumination maps, which result in a video of the full dynamic range.
A program was developed as a proof of concept, providing expected results. For various recording inputs, It was also found that the implemented program discarded a lot of the frames of the recordings. A variation of the proposed method also yielded a slight speed-up, for practically the same results.
The proposed method provides a good starting point tackling the problem of artificially extending the dynamic range. The program used is a step in the right direction, but has flaws that limit its usefulness.

With a large move to virtual production in films, where a large screen displaying a rendered scene is used as an alternative to replacing a green screen, there is a need for simple and effective camera tracking. Infrared tracking is widely used in the industry however it creates a barrier to entry for small productions without limited budgets. As an alternative, visual tracking markers (tags) can be used however they come with limitations as they are highly visible and distract the viewer from the film. This paper aims to answer "Can camera tracking fiducial markers (tags) be blended into the background to reduce their visibility whilst still being tracked?". We describe a method to find locations where tags can be placed to reduce their obtrusiveness. Additionally, we explore various blending methods to blend tags whilst retaining their tracking capability. Our findings indicate that blending methods are effective in reducing the visibility of tags yet still be detected but improvements are needed in determining the optimal blending method for different scenarios.