Fully vision-based automatic human body measurement system for apparel application

Uhm, Taeyoung; Park, Hanhoon; Park, Jong-Il

doi:10.1016/j.measurement.2014.10.044

Cited by 25 publications

(19 citation statements)

References 11 publications

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“…In spite of such limitations, Kinect has been used in a wide spectrum of applications that require the automatic extraction of anthropometric or soft biometric measures, e.g. in clothing (Wang et al 2014;Uhm et al 2015), person re-identification (Lorenzo-Navarro et al 2013;Mogelmose et al 2013), face recognition (Gupta et al 2010), rehabilitation (Reyes et al 2013;Cippitelli et al 2015), and workplace ergonomics (Huang and Pan 2014). Nguyen et al (2014) recently proposed a novel method to estimate human weight, height and gender.…”

Section: Related Workmentioning

confidence: 99%

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Palmero

Esquirol²,

Bayo³

et al. 2016

Int J Comput Vis

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This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.

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Section: Related Workmentioning

confidence: 99%

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Palmero

Esquirol²,

Bayo³

et al. 2016

Int J Comput Vis

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show abstract

“…However, since the subjects are required to be naked or wear underwear during the process of body scanning, numerous consumers refuse to be measured. Some other disadvantages of the devices, including the bulky design, high price and huge storage capacity for 3D images, have also influenced their further application in the garment industry, particularly in middle and small-scale garment enterprises [2,3]. Obviously, in the context of sales over the internet, it is unrealistic to utilize the two anthropometric measurement manners aforementioned [4].…”

mentioning

confidence: 99%

Estimating Human Body Dimensions Using RBF Artificial Neural Networks Technology and Its Application in Activewear Pattern Making

et al. 2019

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Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.

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“…Fuente: (Uhm, 2015) El método de medición corporal consta de cuatro pasos. El primer paso es inicializado en el primer plano, o región del cliente, mediante la extracción de su silueta corporal, en la sustracción de fondo, gracias a la pantalla de fondo (BS / background screen) gracias al reconocimiento de los marcadores que miden la estatura, así como la distancia de hombro de los clientes (figura 107).…”

Section: Estado Del Arteunclassified

“…Fuente: (Uhm, 2015) Figura 108 Marcadores RGB-IR Fuente: (Uhm, 2015) Figura 107 Inicialización por sustracción de fondo y marcadores BS: a) Imagen de la pantalla de fondo original, con la impresión de los marcadores BS b) Inicialización digital normas basadas en marcadores BS c) Interpretación digital referencias color azul para la estatura y rojo en la medida del hombro .ambientales óptimas.…”

Section: Estado Del Arteunclassified

“…Fuente: (Uhm, 2015) Figura 109 El método de visión RGB-IR, a pesar de ser fácil de instalar y de transportar en su embalaje (figura 110), compuesto por una cámara heterogénea, una pantalla, un punto de apoyo, y un ordenador portátil, necesita al menos 3 m 2 de superficie espacial. En definitiva el equipo de ingenieros con ayuda de expertos profesionales en sastrería, han construido un entorno experimental que hace que sea posible medir rápidamente el tamaño corporal de cualquier cliente vestido y sin necesidad de contacto.…”

Section: Estado Del Arteunclassified

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BODYMAP. El futuro de la moda, nuevos modelos de mapeado corporal aplicados a sistemas de medida

Calero¹

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Fully vision-based automatic human body measurement system for apparel application

Cited by 25 publications

References 11 publications

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis

Estimating Human Body Dimensions Using RBF Artificial Neural Networks Technology and Its Application in Activewear Pattern Making

BODYMAP. El futuro de la moda, nuevos modelos de mapeado corporal aplicados a sistemas de medida

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