From 3D Point Clouds to Pose-Normalised Depth Maps

We consider the problem of generating either pairwise-aligned or pose-normalised depth maps from noisy 3D point clouds in a relatively unrestricted poses. Our system is deployed in a 3D face alignment application and consists of the following four stages: (i) data filtering, (ii) nose tip identification and sub-vertex localisation, (iii) computation of the (relative) face orientation, (iv) generation of either a pose aligned or a pose normalised depth map. We generate an implicit radial basis function (RBF) model of the facial surface and this is employed within all four stages of the process. For example, in stage (ii), construction of novel invariant features is based on sampling this RBF over a set of concentric spheres to give a spherically-sampled RBF (SSR) shape histogram. In stage (iii), a second novel descriptor, called an isoradius contour curvature signal, is defined, which allows rotational alignment to be determined using a simple process of 1D correlation. We test our system on both the University of York (UoY) 3D face dataset and the Face Recognition Grand Challenge (FRGC) 3D data. For the more challenging UoY data, our SSR descriptors significantly outperform three variants of spin images, successfully identifying nose vertices at a rate of 99.6%. Nose localisation performance on the higher quality FRGC data, which has only small pose variations, is 99.9%. Our best system successfully normalises the pose of 3D faces at rates of 99.1% (UoY data) and 99.6% (FRGC data)

Focal Spot, Fall 1983

https://digitalcommons.wustl.edu/focal_spot_archives/1035/thumbnail.jp

The chromatin remodeller ACF acts as a dimeric motor to space nucleosomes.

Evenly spaced nucleosomes directly correlate with condensed chromatin and gene silencing. The ATP-dependent chromatin assembly factor (ACF) forms such structures in vitro and is required for silencing in vivo. ACF generates and maintains nucleosome spacing by constantly moving a nucleosome towards the longer flanking DNA faster than the shorter flanking DNA. How the enzyme rapidly moves back and forth between both sides of a nucleosome to accomplish bidirectional movement is unknown. Here we show that nucleosome movement depends cooperatively on two ACF molecules, indicating that ACF functions as a dimer of ATPases. Further, the nucleotide state determines whether the dimer closely engages one or both sides of the nucleosome. Three-dimensional reconstruction by single-particle electron microscopy of the ATPase-nucleosome complex in an activated ATP state reveals a dimer architecture in which the two ATPases face each other. Our results indicate a model in which the two ATPases work in a coordinated manner, taking turns to engage either side of a nucleosome, thereby allowing processive bidirectional movement. This novel dimeric motor mechanism differs from that of dimeric motors such as kinesin and dimeric helicases that processively translocate unidirectionally and reflects the unique challenges faced by motors that move nucleosomes

Fully Automatic Expression-Invariant Face Correspondence

We consider the problem of computing accurate point-to-point correspondences among a set of human face scans with varying expressions. Our fully automatic approach does not require any manually placed markers on the scan. Instead, the approach learns the locations of a set of landmarks present in a database and uses this knowledge to automatically predict the locations of these landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. To accurately fit the expression of the template to the expression of the scan, we use as template a blendshape model. Our algorithm was tested on a database of human faces of different ethnic groups with strongly varying expressions. Experimental results show that the obtained point-to-point correspondence is both highly accurate and consistent for most of the tested 3D face models

A 3D descriptor to detect task-oriented grasping points in clothing

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor. In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft

Robust point correspondence applied to two and three-dimensional image registration

Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (correspondence by sensitivity to movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondence