Automatic feature extraction from front and side images

Lin, Yueh-Ling; Wang, M.-J.J.

doi:10.1109/ieem.2008.4738212

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The FPs from a human body can be used to determine the anthropometric parameters which can, in turn, be used in the construction of 3D human models [2,3] and in recognizing human actions [4][5][6]. Additionally, it can be used for non-contact body size measurements [7] in the apparel industry, where it can, for example, automatically determine the clothing sizes fit for a consumer [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Automated Fiducial Points Detection Using Human Body Segmentation

et al. 2017

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Accurately detected human body fiducial points provide an easy and efficient method for human body posture analysis and the extraction of anthropometric parameters. In the proposed work, an efficient algorithm for automated and accurate detection of fiducial points is developed for both the frontal and the lateral images. An algorithm for automatic human body segmentation of the frontal image is also developed using automatically detected set of primary fiducial points. Additional fiducial points are obtained by applying peak and valley algorithm on the silhouettes of each segment. The detection accuracy of the automatically detected fiducial points is calculated by comparing their locations with the manually marked fiducial points. The proposed algorithm is tested on 45 subjects including both male and female genders and variable Body Mass Indexes. In most cases, the algorithm successfully detects seventy fiducial points for each subject in the testing set. A quantitative analysis of the error in the position of the detected fiducial points shows that the algorithm performs better than the state-of-the-art algorithms found in the existing literature. In the evaluation of the algorithm, the percentage accuracy of the detected fiducial points is calculated and it is observed that the proposed algorithm performs better for the majority of the fiducial points.

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Section: Introductionmentioning

confidence: 99%

Automated Fiducial Points Detection Using Human Body Segmentation

et al. 2017

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A design process for texture segmentation

Khoo

Ong

2009

2009 IEEE International Conference on Industrial Engineering and Engineering Management

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Classification technique has become the necessary tool to detect products which has texture patterns in machine vision. The design used for the process of texture discrimination is critical to the performance of the outcome. In this study we propose a design process model which can systematically maintain and improve on the performance of texture segmentation in terms of speed and segmentation accuracy. The overall accuracy and time tested for the synthetic textures segmentation has shown significant improvement throughout the parameters tuning of the chosen methods.Keywords -Pattern recognition, image segmentation, system design, texture analysis, feature extraction, clustering algorithm 978-1-4244-4870-8/09/$26.00 ©2009 IEEE Proceedings of the 2009 IEEE IEEM

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Learning Anthropometry from Rendered Humans

Yan¹,

Kämäräinen

2021

Preprint

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Accurate estimation of anthropometric body measurements from RGB imags has many potential applications in industrial design, online clothing, medical diagnosis and ergonomics. Research on this topic is limited by the fact that there exist only generated datasets which are based on fitting a 3D body mesh to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are generated. To circumvent the data bottleneck, we introduce a new 3D scan dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset of 200 RGB images and tape measured ground truth. With the help of the two new datasets we propose a part-based shape model and a deep neural network for estimating anthropometric measurements from 2D images. All data will be made publicly available.

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Automatic feature extraction from front and side images

Cited by 3 publications

References 9 publications

Automated Fiducial Points Detection Using Human Body Segmentation

Automated Fiducial Points Detection Using Human Body Segmentation

A design process for texture segmentation

Learning Anthropometry from Rendered Humans

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