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Crucial to variety improvement programs is the reliable and accurate prediction of genotype’s performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4–33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

 

This paper presents a calibration method for a microscopic structured light system with an extended depth of field (DOF). We first employed the focal sweep technique to achieve large enough depth measurement range, and then developed a computational framework to alleviate the impact of phase errors caused by the standard off-the-shelf calibration target (black circles with a white background). Specifically, we developed a polynomial interpolation algorithm to correct phase errors near the black circles to obtain more accurate phase maps for projector feature points determination. Experimental results indicate that the proposed method can achieve a measurement accuracy of approximately 1.0 μ m for a measurement volume of approximately 2,500 μ m (W) × 2,000 μ m (H) × 500 μ m (D). 

Three-dimensional (3D) shape measurement based on the fringe projection technique has been extensively used for scientific discoveries and industrial practices. Yet, one of the most challenging issues is its limited depth of field (DOF). This paper presents a method to drastically increase DOF of 3D shape measurement technique by employing the focal sweep method. The proposed method employs an electrically tunable lens (ETL) to rapidly sweep the focal plane during image integration and the post deconvolution algorithm to reconstruct focused images for 3D reconstruction. Experimental results demonstrated that our proposed method can achieve high-resolution and high-accuracy 3D shape measurement with greatly improved DOF in real time.

 

This paper presents a novel technique to achieve autofocusing for a three-dimensional (3D) profilometry system with dual projectors. The proposed system uses a camera that is attached with an electronically focus-tunable lens (ETL) that allows dynamic change of camera’s focal plane such that the camera can focus on the object; the camera captures fringe patterns projected by each projector to establish corresponding points between two projectors, and two pre-calibrated projectors form triangulation for 3D reconstruction. We pre-calibrate the relationship between the depth and the current being used for each focal plane, perform a 3D shape measurement with an unknown focus level, and calculate the desired current value based on the initial 3D result. We developed a prototype system that can automatically focus on an object positioned between 450 mm to 850 mm.

 

State-of-the-art high-accuracy three-dimensional (3D) profilometry systems typically use a lens with a fixed focal length, making it difficult for them to measure scenes with large depth variations, especially dynamically changing ones. To address this need, this Letter proposes a novel, to the best of our knowledge, autofocusing method for high-resolution 3D profilometry with a digital fringe projection technique by (1) developing a novel continuous geometric parameter model for systems using electrically tunable lenses and (2) employing a focal plane detection algorithm. The validity of the proposed method is confirmed by experiments.