Stature Estimation from Body Dimensions in Han Population of Southern China

Stature estimation is widely used for individual identification in forensic field.Previous studies have proposed several regression equations derived from a single population for this purpose.However,this may not be suitable for other populations because of different hereditary and environmental conditions.In this study,stature estimation equations for southern China Han population have been provided.The study was conducted on a sample population of 121 men and women aged 18–25 years.A total of 19 parameters,including stature,head,torso,and parts of upper limbs and lower limbs,were measured according to standard anthropometric procedures.Herein,the anterior superior spine–malleolus medialis line showed the highest correlation coefficient(r=0.817)and was the most reliable predictor(R^(2)=0.667)in men,while the best predictor for women was total leg length(R^(2)=0.746)with the highest correlation coefficient(r=0.863).The regression analysis results via multiple predictors showed a high accuracy in stature estimation.Moreover,the analysis of multiple regression predictors showed that the dimensions of lower limbs were more reliable for stature estimation compared to head,torso,and upper limb measurements.This study provided equations of stature estimation for southern China Han population which can be useful in cases of dismembered body.

Extracting body surface dimensions from top-view images of pigs

Continuous live weight and carcass traits estimation are important for the pig production and breeding industry.It is widely known that top-view images of a pig’s body(excluding its head and neck)reveal surface dimension parameters,which are correlated with live weight and carcass traits.However,because a pig is not constrained when an image is captured,the body does not always have a straight posture.This creates a big challenge when extracting the body surface dimension parameters,and consequently the live weight and carcass traits estimation has a high level of uncertainty.The primary goal of this study is to propose an algorithm to automatically extract pig body surface dimension parameters,with a better accuracy,from top-view pig images.Firstly,the backbone line of a pig was extracted.Secondly,lengths of line segments perpendicular to the backbone line were calculated,and then feature points on the pig’s contour line were extracted based on the lengths variation of the perpendicular line segments.Thirdly,the head and neck of the pig were removed from the pig’s contour by an ellipse.Finally,four length and one area parameters were calculated.The proposed algorithm was implemented in Matlab®(R2012b)and applied to 126 depth images of pigs.Taking the results of the manual labeling tool as the gold standard,the length and area parameters could be obtained by the proposed algorithm with an accuracy of 97.71%(SE=1.64%)and 97.06%(SE=1.82%),respectively.These parameters can be used to improve pig live weight and carcass traits estimation accuracy in the future work.