The Hungarian Constitutiones Synodales of 1309 and the ‘Holy Crown’: The Theological Use of an Art Object as a Political Symbol

Abstract The center of the baroque altarpiece of the Augustinian Monastery of Polling in South Germany forms the so-called Holy Cross. Its current presentation, dated from 1763, is the last of a sequence of four well-documented presentations of a Romanesque wooden cross since 1230. This cross is an excellent example for analyzing and comparing several methods of re-presenting a historic art object as well as for understanding the motivation for such re-presentations, which are grounded on changes of the spiritual function of the object. In its first reframing, the cross received a covering of gilded parchment and a painting of the crucifix on this ground coat. In a fundamental publication from 1994, this covering was compared with a reliquary holding the old venerated wooden cross. However, looking at the context of medieval instructions for painters and the material evidence of extant contemporary paintings, this interpretation becomes questionable: the covering with parchment was a common and technically motivated procedure rather than a spiritually motivated one.

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Art and Value – Museum Collections as Commons

Historical Materialism ◽

10.1163/1569206x-12341551 ◽

2017 ◽

Vol 25 (4) ◽

pp. 183-200

Author(s):

Nizan Shaked

Keyword(s):

Intrinsic Value ◽

Museum Collections ◽

Monetary Value ◽

Art Object ◽

Symbolic Value ◽

Definition Of ◽

The Individual

Abstract Art and Value: Art’s Economic Exceptionalism in Classical, Neoclassical and Marxist Economics reveals the irreconcilable differences between the Marxist economic definition of the term ‘value’ and its other uses in relation to the art object. It corrects the faulty assumption, symptomatic of a capitalist worldview, that rare or historic objects bear intrinsic value. Beech’s analysis of art’s value-form is critical to unpacking the double ontological condition of art as both an object of collective symbolic value and as a hoard of monetary value, since the two operate in mutually exclusive spheres, yet function to constitute one another. The book can help us understand the capitalist sleight of hand that allows art to flicker between two forms of being, making profit appear as value, and value appear as significance (and vice versa), the toggling between the two facilitating the transfer of commonly-held symbolic value in support of the individual accumulation of wealth.

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Asbestos Detection with Fluorescence Microscopy Images and Deep Learning

Sensors ◽

10.3390/s21134582 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4582

Author(s):

Changjie Cai ◽

Tomoki Nishimura ◽

Jooyeon Hwang ◽

Xiao-Ming Hu ◽

Akio Kuroda

Keyword(s):

Deep Learning ◽

Fluorescence Microscopy ◽

Occupational Safety ◽

Graphics Processing Unit ◽

Reference Method ◽

Processing Unit ◽

Fiber Concentration ◽

Art Object ◽

Wide Range ◽

Microscopy Images

Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0–50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 ([email protected]) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.

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An Experience in Constructing a Qualimetric Assessment of the Significance of Urban Art Objects

Observatory of Culture ◽

10.25281/2072-3156-2021-18-2-116-126 ◽

2021 ◽

Vol 18 (2) ◽

pp. 116-126

Author(s):

Marina E. Vilchinskaya-Butenko ◽

Nikolai N. Rozhkov

Keyword(s):

Pilot Study ◽

Scientific Basis ◽

Relative Significance ◽

Urban Art ◽

Art Object ◽

Art Objects ◽

Emotional Dimension ◽

Artistic Interpretation ◽

Methodological Principles ◽

Necessary And Sufficient

The article attempts to ensure the unity of views on the implementation of urban art projects in local contexts. The paper aims to discuss the results of a pilot study obtained through a comprehensive assessment of the significance of urban art objects using qualimetric scales. The authors selected seven art objects that meet the four requirements: a) the art objects exist in the urban environment at the time of their assessment by experts; b) the art objects have a high communicative potential, that is, they are interesting to the viewer; c) there are discussions in the media and social networks about the prospects for preserving the art objects; d) the sample is heterogeneous. The experimental group included ten experts, both art theorists and practitioners. The experts were asked to evaluate the significance of each of the art objects by ranking them according to eight “rational” and two “emotional” criteria. The existence of consistency of the experts’ opinions was checked using the concordance coefficient. The pilot study showed that the most significant among the rational criteria for evaluating an artwork were technography (the degree of qualitative impact of the art object on the environment, the degree of the work’s conditionality with the context) and iconography (the uniqueness/brightness of the author’s message). The significance of the other principles (of technology and iconology) is considerably lower, which means that they can be ignored when constructing the final assessment by linear convolution. There was also a fairly high relative significance of the two emotional criteria that had been proposed for the experts’ consideration (the emotional dimension of the work in the artist’s experience and the emotional dimension of the work in the viewer’s experience). The scientific novelty of the research is determined by the fact that a systematic approach to assessing the rational aspects of the artistic interpretation of an urban art object makes it necessary and sufficient to rely on the two methodological principles for evaluating an artwork — technography and iconography. When evaluating the emotional aspects of artistic interpretation, it is necessary and sufficient to rely on the emotional dimension of the work in the experience of the artist and the viewer. The results obtained suggest finding an objective scientific basis for regulating the visual culture of public spaces.

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Putting Current State of the art Object Detectors to the Test: Towards Industry Applicable Leather Surface Defect Detection

10.1109/dicta52665.2021.9647409 ◽

2021 ◽

Author(s):

Masood Aslam ◽

Tariq Mehmood Khan ◽

Syed Saud Naqvi ◽

Geoff Holmes

Keyword(s):

Defect Detection ◽

Surface Defect ◽

State Of The Art ◽

Art Object ◽

Current State ◽

Surface Defect Detection

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Fashion Window: A Conceptual Analysis Of Department Store Holiday Windows

10.32920/ryerson.14655003.v1 ◽

2021 ◽

Author(s):

McKenzie Bohn

Keyword(s):

Research Design ◽

Conceptual Analysis ◽

Dimensional Space ◽

Three Dimensional ◽

Gestalt Theory ◽

Fashion Industry ◽

Art Object ◽

The Subject ◽

Window Displays ◽

Three Dimensional Space

Window displays in the fashion industry are unique sites of meaning that combine advertising and artwork in a three-dimensional space. The current body of research surrounding window displays approaches the subject from a marketer’s position and attempts to evaluate performance. This project shifts the focus to the artistic qualities of window displays as they are used by fashion retailers. The primary theoretical lens is gestalt theory, which has applications in both psychology and design. The specific windows examined are the Christmas windows at retailer Saks Fifth Avenue Toronto in December of 2018. An autoethnographic research design is employed, resulting in an exploratory empirical analysis that serves as an entry into an under-represented area of study: the fashion window as an art object. The key findings of the project are the application of gestalt theory to the design of the windows and the researcher’s observations to suggest an explanation of the public’s response to the displays.

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Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

10.21203/rs.3.rs-685762/v1 ◽

2021 ◽

Author(s):

Da-Ren Chen ◽

Wei-Min Chiu

Keyword(s):

Object Detection ◽

Large Scale ◽

Crack Detection ◽

State Of The Art ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Machine Learning Techniques ◽

Detection Accuracy ◽

The Road ◽

Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

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