Abdullah, L.Halfon, S.Sönmez, E.B.2024-07-182024-07-1820239798350322972https://doi.org/10.1109/ICECCME57830.2023.10253058https://hdl.handle.net/11411/6405Aksaray University;IEEE Seccion Espana;University de La Laguna2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 -- 19 July 2023 through 21 July 2023 -- -- 192890The House-Tree-Person (HTP) test is extensively used in different contexts to assess personality related issues. At test time, the respondent is requested to draw a house, a tree and a person. The drawings are later interpreted by therapists to assess emotional indicators. Due to its simplicity, this test is used with individuals aged over 3 years. This paper presents the first results of a bigger project, which aims to use artificial intelligence and image processing techniques to classify House-Tree-Person pictures, to extract psychological features out of those drawings and to find the optimal combination of those signs that may be indicative of psychological maladjustment. Having done the initial HTP classification task, this research focuses on the pictures of houses, searching for the presence/absence of the roof. This work used sketches provided by the department of Psychology of Istanbul Bilgi University and other drawings retrieved from multiple resources and from the internet. In the HTP classification task, the reached performance is 96.81% and 98.84% in the validation and test sets, respectively. In the roof detection task, the achieved accuracy is 70.73%, over a test set of 41 "house"images from which 29 were correctly classified. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessArt TherapyArtificial IntelligenceDeep LearningHouse-Tree-Person TestImage ProcessingProjective Drawing TestClassification (Of İnformation)Deep LearningImage ProcessingRoofsArt TherapyClassification TasksDeep LearningHouse-Tree-Person TestImages ProcessingIntelligence ProcessingProjective Drawing TestPsychological FeaturesTest SetsTest TimeHousesConference Object2-s2.0-8517405096910.1109/ICECCME57830.2023.10253058N/A