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PublicationFew-parameter learning for a hierarchical perceptual grouping system( 2023)Perceptual grouping along well-established Gestalt laws provides one set of traditional methods that provide a tiny set of meaningful parameters to be adjusted for each application field. More complex and challenging tasks require a hierarchical setting, where the results aggregated by a first grouping process are later subject to further processing on a larger scale and with more abstract objects. This can be several steps deep. An example from the domain of forestry provides insight into the search for suitable parameter settings providing sufficient performance for the machine-vision module to be of practical use within a larger robotic control setting in this application domain. This sets a stark contrast in comparison to the state-of-the-art deep-learning neural nets, where many millions of obscure parameters must be adjusted properly before the performance suffices. It is the opinion of the author that the huge freedom for possible settings in such a high-dimensional inscrutable parameter space poses an unnecessary risk. Moreover, few-parameter learning is getting along with less training material. Whereas the state-of-the-art networks require millions of images with expert labels, a single image can already provide good insight into the nature of the parameter domain of the Gestalt laws, and a domain expert labeling just a handful of salient contours in said image yields already a proper goal function, so that a well working sweet spot in the parameter domain can be found in a few steps. As compared to the state-of-the-art neural nets, a reduction of six orders of magnitude in the number of parameters results. Almost parameter-free statistical test methods can reduce the number of parameters to be trained further by one order of magnitude, but they are less flexible and currently lack the advantages of hierarchical feature processing.
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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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PublicationExplorations on the Depth of Gestalt Hierarchies in Social 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 typical social image genres-portraits and group pictures-exemplarily, and outlines technical means for their automatic extraction. The practical part applies bottom-up hierarchical Gestalt grouping as well as topdown search focusing and constraint enforcement, 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)
