Developing an Intelligent Model for the Construction a Hip Shape Recognition System Based on 3D Body Measurement

• Autorzy: Jin J.-F. , Yang Y.-C. , Zou F.-Y.

Identyfikatory

DOI

10.5604/12303666.1215535

Warianty tytułu

PL

Opracowanie inteligentnego modelu dla rozpoznania konstrukcji kształtu bioder

• Języki publikacji: EN

Abstrakty

EN

The purpose of this paper was to develop an intelligent recognition system consisting of a feature reduction method combining cluster and correlation analyses, and a probabilistic neural network (PNN) classifier to identify different types of hip shape from 3D measurement for each person. Firstly 28 items reflecting lower body part information of 300 female university students aging from 20 to 24 years were selected. The feature reduction method was employed to extract typical indices. Secondly hip shapes were subdivided into five types by a K-means cluster and analysis of variance (ANOVA). Finally the PNN was then trained to serve as a classifier for identifying five different hip shape types. The average classification accuracy of the scheme proposed was 97.37%, and its effectiveness was successfully validated by comparing with the BP and Support Vector Machine (SVM) scheme. Thus an intelligent recognition system was developed to make hip shape type classification of high-precision and time saving.

PL

Model łączy analizę skupień i korelacji oraz probabilistyczną sztuczną sieć neuronową dla identyfikacji różnych typów kształtów bioder opartą o pomiary 3D poszczególnych osób. Wyselekcjonowano 28 przypadków odzwierciedlających dolną część sylwetki 300 studentek w wieku od 20 do 24 lat. Zastosowano metodę redukcji poszczególnych właściwości dla wybrania typowych wskaźników. Następnie kształt bioder podzielono na 5 typów za pomocą algorytmu klastrowego i systemu ANOVA (analiza wariancji). Następnie przeprowadzono trening sieci neuronowej aby mogła posłużyć jako klasyfikator identyfikacji 5 różnych kształtów bioder. Przeciętna dokładność klasyfikacji proponowanego systemu wynosiła 97,37%, a efektywność była sukcesywnie sprawdzana przez porównanie schematów BP i SVM. W ten sposób stworzono inteligentny system rozpoznania typu kształtu bioder o dużej precyzji, pozwalający na oszczędność czasu.

Słowa kluczowe

EN

intelligent recognition system; probabilistic neural network; classification accuracy; feature reduction; typical index; cluster analysis

PL

inteligentny system rozpoznawania; sieć neuronowa; dokładność klasyfikacji; funkcja redukcji; typy kształtu bioder

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2016
• Tom: Vol. 24, no. 5 (119)
• Strony: 110--118
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Jin J.-F.

• Fashion Institute, Zhejiang Sci-Tech University (ZSTU), Hangzhou, P. R. China

autor

Yang Y.-C.

autor

Zou F.-Y.

• Fashion Institute, Zhejiang Sci-Tech University (ZSTU), Hangzhou, P. R. China

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.