@misc{KoehlerBreussShabani, author = {K{\"o}hler, Alexander and Breuß, Michael and Shabani, Shima}, title = {Dictionary learning with the K-SVD algorithm for recovery of highly textured images : an experimental analysis}, series = {Proceedings of the Conference Algoritmy 2024}, journal = {Proceedings of the Conference Algoritmy 2024}, editor = {Frolkovič, P. and Mikula, K. and Ševčovič, D.}, publisher = {Jednota slovensk{\´y}ch matematikov a fyzikov}, address = {Bratislava}, isbn = {978-80-89829-33-0}, pages = {264 -- 273}, abstract = {Image recovery by dictionary learning is of potential interest for many possible applications. To learn a dictionary, one needs to solve a minimization problem where the solution should be sparse. The K-SVD formalism, which is a generalization of the K-means algorithm, is one of the most popular methods to achieve this aim. We explain the preprocessing that is needed to bring images into a manageable format for the optimization problem. The learning process then takes place in terms of solving for sparse representations of the image batches. The main contribution of this paper is to give an experimental analysis of the recovery for highly textured imagery. For our study, we employ a subset of the Brodatz database. We show that the recovery of sharp edges plays a considerable role. Additionally, we study the effects of varying the number dictionary elements for that purpose.}, language = {en} } @misc{KhanMohammadiSchneidereitMansouriYarahmadietal., author = {Khan Mohammadi, Mohsen and Schneidereit, Toni and Mansouri Yarahmadi, Ashkan and Breuß, Michael}, title = {Investigating Training Datasets of Real and Synthetic Images for Outdoor Swimmer Localisation with YOLO}, series = {AI}, volume = {5}, journal = {AI}, number = {2}, publisher = {MDPI AG}, address = {Basel}, issn = {2673-2688}, doi = {10.3390/ai5020030}, pages = {576 -- 593}, abstract = {In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real-world training data of such outdoor environments. Natural lighting changes, dynamic water textures, and barely visible swimming persons are key issues to address. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images, and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets were used to train YOLO architectures for possible future applications in real-time detection. The trained frameworks were then tested and evaluated on outdoor environment imagery acquired using a safety drone to investigate and confirm their usefulness for outdoor swimmer localisation.}, language = {en} } @misc{KoehlerKahraBreuss, author = {K{\"o}hler, Alexander and Kahra, Marvin and Breuß, Michael}, title = {A First Approach to Quantum Logical Shape Classification Framework}, series = {Mathematics}, volume = {12}, journal = {Mathematics}, number = {11}, publisher = {MDPI AG}, address = {Basel}, issn = {2227-7390}, doi = {10.3390/math12111646}, abstract = {Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to devise suitable applications. This article aims to propose for the first time an approach using quantum logic in image processing for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way, we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification, we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables us to initiate the principal component analysis. Through experiments, we explore the possibility of distinguishing between different geometrical objects and discuss the results in terms of quantum logical interpretation.}, language = {en} } @misc{KahraBreussKleefeldetal., author = {Kahra, Marvin and Breuß, Michael and Kleefeld, Andreas and Welk, Martin}, title = {An Approach to Colour Morphological Supremum Formation Using the LogSumExp Approximation}, series = {Lecture Notes in Computer Science}, volume = {14605}, journal = {Lecture Notes in Computer Science}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031577925}, issn = {0302-9743}, doi = {10.1007/978-3-031-57793-2_25}, pages = {325 -- 337}, abstract = {Mathematical morphology is a part of image processing that has proven to be fruitful for numerous applications. Two main operations in mathematical morphology are dilation and erosion. These are based on the construction of a supremum or infimum with respect to an order over the tonal range in a certain section of the image. The tonal ordering can easily be realised in grey-scale morphology, and some morphological methods have been proposed for colour morphology. However, all of these have certain limitations. In this paper we present a novel approach to colour morphology extending upon previous work in the field based on the Loewner order. We propose to consider an approximation of the supremum by means of a log-sum exponentiation introduced by Maslov. We apply this to the embedding of an RGB image in a field of symmetric matrices. In this way we obtain nearly isotropic matrices representing colours and the structural advantage of transitivity. In numerical experiments we highlight some remarkable properties of the proposed approach.}, language = {en} } @misc{SchneidereitYarahmadiSchneidereitetal., author = {Schneidereit, Slavomira and Yarahmadi, Ashkan Mansouri and Schneidereit, Toni and Breuß, Michael and Gebauer, Marc}, title = {YOLO-Based Object Detection in Industry 4.0 Fischertechnik Model Environment}, series = {Lecture Notes in Networks and Systems}, volume = {823}, journal = {Lecture Notes in Networks and Systems}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031477232}, issn = {2367-3370}, doi = {10.1007/978-3-031-47724-9_1}, pages = {1 -- 20}, abstract = {In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik Industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.}, language = {en} } @misc{HajighasemiBreuss, author = {Hajighasemi, Saeide and Breuß, Michael}, title = {An Improved Frankot and Chellappa Method for Surface Normal Integration Using Fuzzy Concepts}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202411.0742.v1}, abstract = {In this paper, we propose the Fuzzy formulation of the classic Frankot-Chellappa method by which surfaces can be reconstructed using normal vectors. In the Fuzzy formulation, the surface normal vectors may be uncertain or ambiguous. The underlying model yields a Fuzzy Poisson partial differential equation, where it is imperative to give meaningful representations of Fuzzy derivatives. The solution of the resulting Fuzzy model is approached numerically. To this end, a fuzzy formulation for the discrete sine transform method is explored, which results in a fast, accurate and robust method for surface reconstruction. In experiments we consider specifically the robustness with respect to noisy surface normal vectors.}, language = {en} } @misc{KoehlerBreussShabani, author = {K{\"o}hler, Alexander and Breuß, Michael and Shabani, Shima}, title = {Dictionary Learning with the K-SVDAlgorithm for Recovery of Highly Textured Images}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202406.0355.v1}, abstract = {Image recovery by dictionary learning is of potential interest for many possible applications. To learn a dictionary, one needs to solve a minimization problem where the solution should be sparse. The K-SVD formalism, which is a generalization of the K-means algorithm, is one of the most popular methods to achieve this aim. We explain the preprocessing that is needed to bring images into a manageable format for the optimization problem.The learning process then takes place in terms of solving for sparse representations of the image batches. The main contribution of this paper is to give an experimental analysis of the recovery for highly textured imagery. For our study, we employ a subset of the Brodatz database. We show that the recovery of sharp edges plays a considerable role. Additionally, we study the effects of varying the number dictionary elements for that purpose.}, language = {en} } @misc{KoehlerKahraBreuss, author = {K{\"o}hler, Alexander and Kahra, Marvin and Breuß, Michael}, title = {A First Approach to Quantum Logical Shape Classification Framework}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202402.1042.v1}, abstract = {Quantum logic is a well-structured theory, which has recently received some attention because of its fundamental relation to quantum computing. However, the complex foundation of quantum logic borrowing concepts from different branches of mathematics as well as its peculiar settings have made it a non-trivial task to device suitable applications. This article aims to propose for the first time an approach to use quantum logic in image processing at hand of a process for shape classification. We show how to make use of the principal component analysis to realize quantum logical propositions. In this way we are able to assign a concrete meaning to the rather abstract quantum logical concepts, and we are able to compute a probability measure from the principal components. For shape classification we consider encrypting given point clouds of different objects by making use of specific distance histograms. This enables to initiate the principal component analysis. At hand of experiments, we explore the possibility to distinguish between different geometrical objects and discuss the results in terms of quantum logical interpretation.}, language = {en} } @misc{KhanMohammadiSchneidereitMansouriYarahmadietal., author = {Khan Mohammadi, Mohsen and Schneidereit, Toni and Mansouri Yarahmadi, Ashkan and Breuß, Michael}, title = {Investigating training datasets of real and synthetic images for swimmer localisation with YOLO}, series = {Preprints.org}, journal = {Preprints.org}, publisher = {MDPI AG}, doi = {10.20944/preprints202402.0446.v1}, abstract = {In this paper we develop and explore a methodical pipeline for swimmer localisation in outdoor environments. The developed framework is intended to be used for enhancing swimmer safety. A main issue we deal with by the proposed approach is the lack of real world training data in such outdoor environments. Natural lighting changes, dynamic water textures and possibly barely visible swimming persons are key elements to approach. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for the use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets are used to train a YOLO architecture for the possible future application in real-time detection. The trained framework is then tested and evaluated on outdoor environment imagery acquired by a safety drone to investigate and confirm the usefulness for outdoor swimmer localisation.}, language = {en} }