Classic photometric stereo is often extended to deal with real-world materials and work with unknown lighting conditions for practicability. To quantitatively evaluate non-Lambertian and uncalibrated photometric stereo, a photometric stereo image dataset containing objects of various shapes with complex reflectance properties and high-quality ground truth normals is still missing. In this paper, we introduce the 'DiLiGenT' dataset with calibrated Directional Lightings, objects of General reflectance with different shininess, and 'ground Truth' normals from high-precision laser scanning. We use our dataset to quantitatively evaluate state-of-the-art photometric stereo methods for general materials and unknown lighting conditions, selected from a newly proposed photometric stereo taxonomy emphasizing on non-Lambertian and uncalibrated methods. The dataset and evaluation results are made publicly available, and we hope it can serve as a benchmark platform that inspires future research.

This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on a data set generated from extensive open-loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov-based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed-loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed-loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.

Biomechanical properties of soft tissues are important for a wide range of medical applications, such as surgical simulation and planning and detection of lesions by elasticity imaging modalities. Currently, the data in the literature is limited and conflicting. Furthermore, to assess the biomechanical properties of living tissue in vivo, reliable imaging-based estimators must be developed and verified. For these reasons we developed and compared two independent quantitative methods -crawling wave estimator (CRE) and mechanical measurement (MM) for soft tissue characterization. The CRE method images shear wave interference patterns from which the shear wave velocity can be determined and hence the Young's modulus can be obtained. The MM method provides the complex Young's modulus of the soft tissue from which both elastic and viscous behavior can be extracted. This article presents the systematic comparison between these two techniques on the measurement of gelatin phantom, veal liver, thermal-treated veal liver, and human prostate. It was observed that the Young's moduli of liver and prostate tissues slightly increase with frequency. The experimental results of the two methods are highly congruent, suggesting CRE and MM methods can be reliably used to investigate viscoelastic properties of other soft tissues, with CRE having the advantages of operating in nearly real time and in situ.

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.

The shear wave velocity is one of a few important parameters that characterize the mechanical properties of bio-materials. In this paper, two noninvasive methods are proposed to measure the shear velocity by inspecting the shear wave interference patterns. In one method, two shear wave sources are placed on the opposite two sides of a sample, driven by the identical sinusoidal signals. The shear waves from the two sources interact to create interference patterns, which are visualized by the vibration sonoelastography technique. The spacing between the pattern bands equals half of the shear wavelength. The shear velocity can be obtained by taking the product of the wavelength and the frequency. An alternative method is to drive the two vibration sources at slightly different frequencies. In this case, the interference patterns no longer remain stationary. It is proved that the apparent velocity of the moving patterns is proportional to the shear velocity in the medium. Since the apparent velocity of the patterns can be measured by analysing the video sequence, the shear velocity can be obtained thereafter. These approaches are validated by a conventional shear wave time-of-flight approach, and they are accurate within 4% on various homogeneous tissue-mimicking phantoms.