Detection of sculpted faces on building facades

Shalunts, Gayane; Cerman, Martin; Albertini, Daniel

doi:10.1109/apsipa.2017.8282119

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Real estate websites fare generally better in that regard, owing to their narrower application scope, but their images typically cannot be redistributed [106], and building locations can be purposefully inaccurate for privacy reasons. Some image data is captured by the researchers themselves [85,93], even including physical synthetic data such as pictures of artificial avian faeces [56]. This approach creates an opportunity for a highlycollaborative approach to producing custom-made datasets where architectural experts can advise on an appropriate representation of specific architectural features.…”

Section: Acquisition Methodmentioning

confidence: 99%

“…For example, traditionally highpitched roofs are found in regions affected by heavy snowfall. The detection and classification of building elements involves prior feature extraction and, like style classifications might use indicators such as window designs [86] or face-recognition algorithms (applied to sculpted heads of humans and gargoyles) as a determining feature [85]. A set of stylistic elements extracted from street-view images is used to determine features typical for Paris (or those untypical) in Doersch et al [24].…”

Section: Building Detail Detection and Classificationmentioning

confidence: 99%

“…poverty levels [47] or visual indicators of architectural styles [24,33,61,75,85,86,102]. This includes the qualitative analysis of visual clues and perceived attributes that, for example, might indicate urban gentrification [45] or specific urban environmental conditions [25,73,82].…”

Section: Research Trendsmentioning

confidence: 99%

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Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

et al. 2023

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Analysing 88 sources published from 2011 to 2021, this paper presents a first systematic review of the computer vision-based analysis of buildings and the built environment. Its aim is to assess the potential of this research for architectural studies and the implications of a shift to a crossdisciplinarity approach between architecture and computer science for research problems, aims, processes, and applications. To this end, the types of algorithms and data sources used in the reviewed studies are discussed in respect to architectural applications such as a building classification, detail classification, qualitative environmental analysis, building condition survey, and building value estimation. Based on this, current research gaps and trends are identified, with two main research aims emerging. First, studies that use or optimise computer vision methods to automate time-consuming, labour-intensive, or complex tasks when analysing architectural image data. Second, work that explores the methodological benefits of machine learning approaches to overcome limitations of conventional analysis in order to investigate new questions about the built environment by finding patterns and relationships between visual, statistical, and qualitative data. The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.

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Section: Acquisition Methodmentioning

confidence: 99%

Section: Building Detail Detection and Classificationmentioning

confidence: 99%

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Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

et al. 2023

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NICE: Navagraha Iconography Classification Engine

Mehta,

Sindhu,

Dube

et al. 2023

Lecture Notes in Networks and Systems

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The Classification of Cultural Heritage Buildings in Athens Using Deep Learning Techniques

Siountri

Anagnostopoulos

2023

Heritage

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Architectural structures, the basic elements of the urban web, are an aggregation of buildings that have been built at different times, with different materials, and in different styles. Through research, they can be divided into groups that present common morphological attributes and refer to different historical periods with particular social, economic, and cultural characteristics. The identification of these common repeating elements and organizational construction structures leads to the identification of the “type” of the building, which until now has required specialized knowledge, time, and customized proof checking. Recent developments in the field of artificial intelligence (AI) and, more specifically, in deep learning (DL) appear to contribute gradually to the study of the typological evolution of buildings, especially those of cultural heritage (CH). In this paper, we present a deep-learning-based method for the classification of modern Athenian architecture (since 1830) using the YOLO algorithm. This research work can contribute to the digital management of the existing urban building stock, the autonomous large-scale categorization of data that are available from street view images, and the enhancement of the tangible CH.

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Detection of sculpted faces on building facades

Cited by 3 publications

References 21 publications

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

NICE: Navagraha Iconography Classification Engine

The Classification of Cultural Heritage Buildings in Athens Using Deep Learning Techniques

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