Artificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Test

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dc.authorscopusid58644160400
dc.authorscopusid58645062900
dc.authorscopusid36782998200
dc.authorscopusid58644160500
dc.contributor.authorSalar, A.A.
dc.contributor.authorFaiyad, H.
dc.contributor.authorSönmez, E.B.
dc.contributor.authorHafton, S.
dc.date.accessioned2024-07-18T20:17:05Z
dc.date.available2024-07-18T20:17:05Z
dc.date.issued2023
dc.descriptionAksaray University;IEEE Seccion Espana;University de La Lagunaen_US
dc.description2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890en_US
dc.description.abstractThis paper applies computer vision and artificial intelligence algorithms to the HTP (House-Tree-Person) test, a projective test intended to measure different aspects of personality using drawings. The drawn pictures are assumed to represent the subject's attitudes and feelings regarding themselves, other and their family. The House-Tree-Person evaluation uses "Qualitative Scoring,"which is a subjective analysis influenced by the therapists that can be used to infer aggressive, depressive or anxious characteristics in the drawings. This paper is part of a larger project that aims to use artificial intelligence and image-processing techniques to support this process, hence reducing the bias factor from the equation. With the collaboration of the Department of Psychology at Istanbul Bilgi University, the project investigates on possible approaches to extract discriminative features out of HTP sketch images and it searches for a meaningful combination, which will support therapists in their diagnostic assessments. After data pre-processing, image classification of clinical HTP data was conducted using the ResNet152 model and achieving a test accuracy of 66%. Furthermore, the experiment of the detection of the "pen pressure"feature was performed using Skeletonization and morphological image processing; however, due to a lack of ground truth, the performance of the proposed algorithm is not determined yet. © 2023 IEEE.en_US
dc.identifier.doi10.1109/ICECCME57830.2023.10252218
dc.identifier.isbn9798350322972
dc.identifier.scopus2-s2.0-85173988753en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ICECCME57830.2023.10252218
dc.identifier.urihttps://hdl.handle.net/11411/6404
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArt Therapyen_US
dc.subjectComputer Visionen_US
dc.subjectHouse-Tree-Person Testen_US
dc.subjectİmage Classificationen_US
dc.subjectMachine Learningen_US
dc.subjectPsychological Feature Extractionen_US
dc.subjectSketch Drawing By Childrenen_US
dc.subjectComputer Visionen_US
dc.subjectData Handlingen_US
dc.subjectFeature Extractionen_US
dc.subjectHousesen_US
dc.subjectArt Therapyen_US
dc.subjectArtificial İntelligence Algorithmsen_US
dc.subjectFeatures Extractionen_US
dc.subjectHouse-Tree-Person Testen_US
dc.subjectImages Classificationen_US
dc.subjectMachine-Learningen_US
dc.subjectPsychological Feature Extractionen_US
dc.subjectPsychological Featuresen_US
dc.subjectSketch Drawing By Childen_US
dc.subjectTree Testsen_US
dc.subjectImage Classificationen_US
dc.titleArtificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Testen_US
dc.typeConference Objecten_US

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