arXiv subjects

Kai Brehmer

Publications and source records attributed to Kai Brehmer.

4 recordsLinked to original sources

arXiv2026

A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings

Complex criminal investigations are often hindered by large volumes of unstructured evidence and by the semantic gap between natural language investigative intent and technical search logic. To address this challenge, we present a design and feasibility study of a cloud-native microservice architecture tailored to private-cloud deployments, contributing to research in secure cloud computing and leveraging modern cloud paradigms under high security and scalability requirements. The proposed system integrates Large Language Models into a "Human-in-Control" workflow that translates natural-language queries into syntactically valid OpenSearch Domain-Specific Language expressions. We describe the implementation of a hybrid retrieval strategy within OpenSearch that combines BM25-based lexical search with nested semantic vector embeddings. The paper focuses on system design and preliminary functional validation, establishing an architectural baseline for future empirical evaluation. Technical feasibility is demonstrated through a functional prototype, and a rigorous evaluation methodology is outlined using the Enron Email Dataset as a structural proxy for restricted investigative corpora.

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arXiv2019

Variational Registration of Multiple Images with the SVD based SqN Distance Measure

Image registration, especially the quantification of image similarity, is an important task in image processing. Various approaches for the comparison of two images are discussed in the literature. However, although most of these approaches perform very well in a two image scenario, an extension to a multiple images scenario deserves attention. In this article, we discuss and compare registration methods for multiple images. Our key assumption is, that information about the singular values of a feature matrix of images can be used for alignment. We introduce, discuss and relate three recent approaches from the literature: the Schatten q-norm based SqN distance measure, a rank based approach, and a feature volume based approach. We also present results for typical applications such as dynamic image sequences or stacks of histological sections. Our results indicate that the SqN approach is in fact a suitable distance measure for image registration. Moreover, our examples also indicate that the results obtained by SqN are superior to those obtained by its competitors.

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arXiv2019

A Novel Similarity Measure for Image Sequences

Quantification of image similarity is a common problem in image processing. For pairs of two images, a variety of options is available and well-understood. However, some applications such as dynamic imaging or serial sectioning involve the analysis of image sequences and thus require a simultaneous and unbiased comparison of many images. This paper proposes a new similarity measure, that takes a global perspective and involves all images at the same time. The key idea is to look at Schatten-q-norms of a matrix assembled from normalized gradient fields of the image sequence. In particular, for q = 0, the measure is minimized if the gradient information from the image sequence has a low rank. This global perspective of the novel SqN-measure does not only allow to register sequences from dynamic imaging, e.g. DCE-MRI, but is also a new opportunity to simultaneously register serial sections, e.g. in histology. In this way, an accumulation of small, local registration errors may be avoided. First numerical experiments show very promising results for a DCE-MRI sequence of a human kidney as well as for a set of serial sections. The global structure of the data used for registration with SqN is preserved in all cases.

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arXiv2019

Simultaneous Registration of Image Sequences -- a novel singular value based images similarity measure

The comparison of images is an important task in image processing. For a comparison of two images, a variety of measures has been suggested. However, applications such as dynamic imaging or serial sectioning provide a series of many images to be compared. When these images are to be registered, the standard approach is to sequentially align the j-th image with respect to its neighbours and sweep with respect to j. One of the disadvantages is that information is distributed only locally. We introduce an alternative so-called SqN approach. SqN is based on the Schatten-q-norm of the image sequence gradients, i.e. rank information of image gradients of the whole image sequence. With this approach, information is transported globally. Our experiments show that SqN gives at least comparable registration results to standard distance measures but its computation is about six times faster.

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