A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

Application of psychoacoustic analyses according to ECMA-418-2

Assessing and assuring sound quality has become a very important task for product design. Customers expect product sounds without disturbing noises. This is a challenge because spectro-temporal noise patterns (such as tonal sounds or modulated signals that generate a roughness sensation) must be taken into account, in addition to frequency-weighted values like dB(A) and loudness. If the sound of a technical product exhibits these characteristics, it is most likely perceived as having poor quality. The new standard ECMA-418-2 describes methods for the automatic quantification of tonal sounds and modulated sounds, which generate a sensation of roughness. The methods are based on a psychoacoustic hearing model and thus emulate human perception very closely. This paper describes the application of these methods. Several examples show how these parameters can be used for sound engineering and how to interpret the results.

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior to that of conventional methods based on experimental results.

A method for measuring digital camera noise by automatic segmentation of a striped target

Currently, cameras are widely used in scientific, industrial and amateur tasks. Thus, one needs to be able to quickly evaluate characteristics and capabilities of a particular camera. A method for measuring noise components of the camera photosensor is proposed. It allows one to estimate shot noise, dark temporal noise, photo response non-uniformity and dark signal non-uniformity. For noise measurement, just two images of the same scene need to be registered. The scene consists of several stripes (quasihomogeneous regions). Then the images are processed by automatic signal segmentation. The performance and accuracy of the proposed method are higher than or equal to other fast methods. The experimental results obtained are similar to those derived using a time-consuming standard method within a measurement error.

Estimation of efficiency of measurement of digital camera photosensor noise by automatic segmentation of non-uniform target method and the standard EMVA 1288

In this paper important task of estimation of digital camera’s noise parameters is considered. Relation of accuracy of data obtained with digital camera and photosensor noise is discussed. Both standard European machine vision association EMVA 1288 and fast automatic segmentation of non-uniform target (ASNT) noise estimation methods are compared. Noise characteristics of machine vision PixeLink PL-B781F, scientific Retiga R6 and amateur mirrorless Canon EOS M100 cameras have been investigated. Accuracy of measurements, speed of calculation and experimental realization has been analyzed. Accuracy of temporal noise estimation by modified ASNT method is no less than that one for standard EMVA 1288. But the ASNT method can be implemented much faster than the standard EMVA 1288 even with additional frames for accuracy improvement.