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PublicationComposition and Symmetries - Computational Analysis of Fine-Art Aesthetics( 2022)This article deals with the problem of quantitative research of the aesthetic content of the fine-art object. The paper states that a fine-art object is a conceptually formed sequence of signs, and its composition is a structural form, that can be measured using mathematical models. The main approach is based on the perception of the formal order as a determinant of the aesthetic category of beauty. The composition of the image is directly related to the formation of aesthetic sensations and values, since it performs the function of controlling the viewer's perception of a work of art. The research is based on the studies of computational aesthetics by G. D. Birkhoff and M. Bense, as well as the studies of the receptive aesthetics of R. Ingarden, W. Iser, H. R. Jauss and Ya. Mukarzhovsky. The computational aesthetics methods, such as CNN-based object detectors, and gestalt-based symmetry analysis, are used to detect symmetry axes in fine-art images. Experimental analysis demonstrates that the applied computational approach is consistent with the philosophical analysis and the expert evaluations of the fine-art images, therefore it allows to obtain more detailed fine-art paintings description.
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PublicationOn the Depth of Gestalt Hierarchies in Common Imagery( 2021)Apart from machine learning and knowledge engineering, there is a third way of challenging machine vision - the Gestalt law school. In an interdisciplinary effort between psychology and cybernetics, compositionality in perception has been studied for at least a century along these lines. Hierarchical compositions of parts and aggregates are possible in this approach. This is particularly required for high-quality high-resolution imagery becoming more and more common, because tiny details may be important as well as large-scale interdependency over several thousand pixels distance. The contribution at hand studies the depth of Gestalt-hierarchies in a typical image genre - the group picture - exemplarily, and outlines technical means for their automatic extraction. The practical part applies bottom-up hierarchical Gestalt grouping as well as top-down search focusing, listing as well success as failure. In doing so, the paper discusses exemplarily the depth and nature of such compositions in imagery relevant to human beings.
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PublicationDesigning a fusion of visible and infra-red camera streams for remote tower operations( 2020)The research project INVIDEON evaluated requirements, technical solutions and the benefit of fusing visible (VIS) and infra-red (IR) spectrum camera streams into a single panorama video stream. In this paper, the design process for developing a usable and accepted fusion is described. As both sensors have strengthens and weaknesses, INVIDEON proposes a fused panorama optimized out of both sensors to be presented to the ATC officer (ATCO). This paper gives an overview of the project and reports results of acceptance and usability of the INVIDEON solution. The process of supporting the definition of requirements by means of rapid prototyping and taking a user-centered approach is described. Main findings of requirements for fusing VIS and IR camera data for remote tower operations are highlighted and set into context with the air traffic controller's tasks. A specific fusion approach was developed within the project and evaluated by means of recorded IR and VIS data. For evaluation, a testbed was set up at a regional airport and data representing different visibility conditions were selected out of 70 days data recordings. Five air traffic controllers participated in the final evaluation. Subjective data on perceived usability, situational awareness and trust in automation was assessed. Furthermore, qualitative data on HMI design and optimization potential from debriefings and comments was collected and clustered.
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PublicationDesign of orientation assessment functions for gestalt-grouping utilizing labeled sample-data( 2019)
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PublicationReconstructing lattices from permanent scatterers on facades( 2018)In man-made structures regularities and repetitions prevails. In particular in building facades lattices are common in which windows and other elements are repeated as well in vertical columns as in horizontal rows. In very-high-resolution space-borne radar images such lattices appear saliently. Even untrained arbitrary subjects see the structure instantaneously. However, automatic perceptual grouping is rarely attempted. This contribution applies a new lattice grouping method to such data. Utilization of knowledge about the particular mapping process of such radar data is distinguished from the use of Gestalt laws. The latter are universally applicable to all kinds of pictorial data. An example with so called permanent scatterers in the city of Berlin shows what can be achieved with automatic perceptual grouping alone, and what can be gained using domain knowledge.
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PublicationHierarchical grouping using gestalt assessments( 2017)Real images contain symmetric Gestalten with high probability. I.e. certain parts can be mapped on other certain parts by the usual Gestalt laws and are repeated there with high similarity. Moreover, such mapping comes in nested hierarchies - e.g. a reflection Gestalt that is made of repetition friezes, whose parts are again reflection symmetric compositions. This can be explicitly modelled by continuous assessment functions. Hard decisions on whether or not a law is fulfilled are avoided. Starting from primitive objects extracted from the input image successively aggregates are constructed: reflection pairs, rows, etc., forming a part-of-hierarchy and rising in scale. The work in this paper starts from super-pixel primitives, and the grouping ends when the Gestalten almost fill the whole image. Occasionally the results may not be in accordance with human perception. The parameters have not been adjusted specifically for the data at hand. Previous work only used the compulsory attributes location, scale, orientation and assessment for each object. A way to improve the recognition performance is utilizing additional features such as colors or eccentricity. Thus the recognition rates are a little better.
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PublicationHierarchical grouping - the gestalt assessments method( 2017)Real images contain reflection symmetry and repetition in rows with high probability. I.e. certain parts can be mapped on other certain parts by the usual Gestalt laws and are repeated there with high similarity. Moreover, such mapping comes in nested hierarchies - e.g. a reflection Gestalt that is made of repetition friezes, whose parts are again reflection symmetric compositions. It is our intention to develop and test methods that may automatically find, parametrize, and assess such nested hierarchies. This can be explicitly modelled by continuous assessment functions. The recognition performance is raised utilizing additional features such as colors. This paper reports examples from the 2017 data set.
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PublicationDesigning observer trials for image fusion experiments with Latin Squares( 2017)Multisensor image fusion (e.g. IR with visual) is the process of combining relevant information from two or more images into a single image. The aim is to find an objective quality measure, which can be used in automatic applications, that correlates best with subjective observer trials. Not all combinations of image, algorithm, and test observer can be worked out. In this paper R. Fisher's Design of Experiments approach based on Latin Squares is used for thinning out the number of experiments for each observer in such observer trials while preserving exactness and reliability of the result.
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PublicationImproved Linear Direct Solution for Asynchronous Radio Network Localization (RNL)( 2017)The linear least square solution is frequently used in the field of localization. Compared to nonlinear solvers, this solution is more affected by noise but able to provide a position estimation without knowing any starting condition. The linear least square solution is able to minimize Gaussian noise by solving an overdetermined equation with the Moore-Penrose pseudoinverse. Unfortunately, this solution fails in the case of non-Gaussian noise. This publication presents a direct solution using prefiltered data for the LPM (RNL) equation. The input used for linear position estimation will not be the raw data but data filtered over time and for this reason this solution will be called the direct solution. It will be shown that the symmetrical direct solution presented is superior to the non-symmetrical direct solution and in particular to the non-prefiltered linear least square solution.
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PublicationMultilateration of the local position measurement( 2016)The Local Position Measurement system (LPM) is one of the most precise systems for 3D position estimation. It is able to operate in- and outdoor and updates at a rate up to 1000 measurements per second. Previous scientific publications focused on the time of arrival equation (TOA) provided by the LPM and filtering after the numerical position estimation. This paper investigates the advantages of the TOA over the time difference of arrival equation transformation (TDOA) and the signal smoothing prior to its fitting. The LPM was designed under the general assumption that the position of the base station and position of the reference station are known. The information resulting from this research can prove vital for the system's self-calibration, providing data aiding in locating the relative position of the base station without prior knowledge of the transponder and reference station positions.
