Automatic measurement extraction for apparel from a three-dimensional body scan

Pargas, Roy P.; Staples, Nancy J.; Davis, John S.

doi:10.1016/s0143-8166(97)00009-2

Cited by 43 publications

(30 citation statements)

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“…Exhaustive results corresponding to standard postures have been published in [18]. (4,5,6). We can observe that the scans are segmented with reasonable accuracy.…”

Section: Methodsmentioning

confidence: 86%

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A functional-based segmentation of human body scans in arbitrary postures

Werghi¹,

Xiao

Siebert

2006

IEEE Trans. Syst., Man, Cybern. B

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Abstract-This paper presents a general framework that aims to address the task of segmenting three-dimensional (3-D) scan data representing the human form into subsets which correspond to functional human body parts. Such a task is challenging due to the articulated and deformable nature of the human body. A salient feature of this framework is that it is able to cope with various body postures and is in addition robust to noise, holes, irregular sampling and rigid transformations. Although whole human body scanners are now capable of routinely capturing the shape of the whole body in machine readable format, they have not yet realized their potential to provide automatic extraction of key body measurements. Automated production of anthropometric databases is a prerequisite to satisfying the needs of certain industrial sectors (e.g., the clothing industry). This implies that in order to extract specific measurements of interest, whole body 3-D scan data must be segmented by machine into subsets corresponding to functional human body parts. However, previously reported attempts at automating the segmentation process suffer from various limitations, such as being restricted to a standard specific posture and being vulnerable to scan data artifacts. Our human body segmentation algorithm advances the state of the art to overcome the above limitations and we present experimental results obtained using both real and synthetic data that confirm the validity, effectiveness, and robustness of our approach.Index Terms-Human body shape analysis, Reeb graph, scattered 3-D range data segmentation, 3-D surface topology, 3-D whole-body scanners.

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“…Exhaustive results corresponding to standard postures have been published in [18]. (4,5,6). We can observe that the scans are segmented with reasonable accuracy.…”

Section: Methodsmentioning

confidence: 86%

“…This advance facilitates the exploitation of the HB form in various areas such as anthropometrical research [1]- [3], clothing design [4]- [6], and virtual human animation [7], [8]. Although the raw data generated by the HB scanner requires substantial main memory and storage resources, it contains little semantic information.…”

Section: Introductionmentioning

confidence: 99%

A functional-based segmentation of human body scans in arbitrary postures

Werghi¹,

Xiao

Siebert

2006

IEEE Trans. Syst., Man, Cybern. B

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“…In entertainment , scans of real persons can be mapped to generic models and then integrated in video games, TV or cinema production [1]. In clothing industry, human body scans can substitute the real person for extracting measurements [2,3,4]. Data bases of HB scans can be useful for medical and anthropological surveys [5].…”

Section: Recognizing a 3d Human Body Posturementioning

confidence: 99%

“…There have been some attempts to segment automatically human body shape [2,3,4] however these works treated the particular case of a standard posture ( Figure 1.(a)). Their techniques were restrictive and cannot handle arbitrary postures.…”

Section: Recognizing a 3d Human Body Posturementioning

confidence: 99%

Recognition of human body posture from a cloud of 3D data points using wavelet transform coefficients

Werghi

Xiao

Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition

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“…This advance facilitates the exploitation of the HB form in various areas such as anthropometrical research [1,2], clothing design [3,4] and virtual human animation [5]. Although the raw data delivered by the HB scanner requires substantial main memory and backing store resources, this data contains little semantic information.…”

Section: Introduction and Previous Workmentioning

confidence: 99%