By Zhiqing Cheng, Kathleen Robinette (auth.), Vincent G. Duffy (eds.)
This booklet constitutes the refereed lawsuits of the second one foreign convention on electronic Human Modeling, ICDHM 2009, held in San Diego, CA, united states in July 2009.
The eighty revised papers provided have been conscientiously reviewed and chosen from a variety of submissions. The papers approved for presentation completely hide the thematic zone of face, head and physique modeling, modeling movement, modeling habit, emotion and cognition, human modeling in delivery purposes, human modeling functions in health and wellbeing and rehabilitation, ergonomic and business purposes, and advances in electronic human modeling.
Read or Download Digital Human Modeling: Second International Conference, ICDHM 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings PDF
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Extra info for Digital Human Modeling: Second International Conference, ICDHM 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings
8% of sample finds shopping time consuming yet spends relatively more on fashion; they are uncertain about their interest in trying the body scanner but will patronize a retail offering styling recommendations. 5 Conclusions and Recommendations Our results show that the group of consumers most interested in the body scan technology and in patronizing stores offering styling recommendations is comprised of individuals at the lower end of the spending spectrum on fashion. This finding begs the question: is it worthwhile investing in 3D scanning technology and in providing styling recommendations.
As time goes by, the research of facial image processing has become the focal point of many researchers’ attention. Facial images have a lot of information including information about gender, age, expression, and ethnic origin. In this paper a method for gender classification is proposed. Not surprisingly, a lot of research on facial gender classification has been done by researchers from in the field of computer science. Our gender classification method consists of three major steps; extraction of facial sub-images, construction of LUT, and calculation of DIE.
Our gender classification method consists of three major steps; extraction of facial sub-images, construction of LUT, and calculation of DIE. We propose a new gender classification system using Shannon’s entropy-based DIE and LUT-based sub-images. The difference images are computed with pixel subtraction between input images and average images from reference images. , PAL , BioID , FERET , PIC , and Caltech . In addition, the proposed method is compared to the methods of using Euclidean with PCA and sobel image among the edge detection methods.
Digital Human Modeling: Second International Conference, ICDHM 2009, Held as Part of HCI International 2009, San Diego, CA, USA, July 19-24, 2009. Proceedings by Zhiqing Cheng, Kathleen Robinette (auth.), Vincent G. Duffy (eds.)