3D corrective nose reconstruction from a single image

There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

KERNEL ESTIMATION WHEN DENSITY MAY NOT EXIST: A CORRIGENDUM

The paper “Kernel estimation when density may not exist” (Zinde-Walsh, 2008) considered density as a generalized function given by a functional on a space of smooth functions; this made it possible to establish the limit properties of the kernel estimator without assuming the existence of the density function. This note corrects an error in that paper in the derivation of the variance of the kernel estimator. The corrected result is that in the space of generalized functions the parametric rate of convergence of the kernel density estimator to the limit Gaussian process is achievable.

Color Calibration of Remote Sensing Imagery Based on Orthogonal Space Transformation

Due to different atmospheric conditions, seasonal changes in vegetation characteristics and other reasons, the remote sensing images captured at different time may be quite different in color, brightness and so on. In this paper, coupled with statistics classification, three common orthogonal space transformations were used to calibrate the color difference respectively. Compared with conventional methods such as the overlapping region correction and histogram matching, the results show that orthogonal transform could achieve better correction effects. The lαβ transform gets the best corrected result among three orthogonal space transform methods.

Overestimation of Hemoglobin in a Patient With an IgA-κ Monoclonal Gammopathy

A patient with multiple myeloma had an automated blood count performed on a Coulter STK-S counter that repeatedly failed internal limits for both mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration. The calculated hematocrit agreed with a spun hematocrit, suggesting that the hemoglobin concentration was being overestimated by the automated counter. Measurement of the plasma hemoglobin concentration of the sample, which showed no visible hemolysis, gave a hemoglobin concentration of 32 g/L on the STK-S analyzer. Correction of the whole blood hemoglobin using the plasma hemoglobin gave a value consistent with the hematocrit. The corrected mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration values were within standard limits. This patient's paraprotein was characterized as IgA-κ and was present at a concentration of 61 g/L. The hemoglobin concentration measured on whole blood by Sysmex NE 8000 and Technicon H*1E autoanalyzers agreed reasonably well with the corrected result from the STK-S.