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Abstract: This paper seeks to advance the state-of-the-art in 3D face capture and processing via novel Photometric Stereo (PS) hardware and algorithms. The first contribution is a new high-speed 3D data capture system, which is capable of acquiring four raw images in approximately 20ms. The results presented in this paper demonstrate the feasibility of deploying the device in commercial settings. We show how the device can operate with either visible light or near infrared (NIR) light. The NIR light sources offer the advantages of being less intrusive and more covert than most existing face recognition methods allow. Furthermore, our experiments show that the accuracy of the reconstructions is also better using NIR light. The paper also presents a modified four-source PS algorithm which enhances the surface normal estimates by assigning a likelihood measure for each pixel being in a shadowed region. This likelihood measure is determined by the discrepancies between measured pixel brightnesses and expected values. Where the likelihood of shadow is high, then one light source is omitted from the computation for that pixel, otherwise a weighted combination of pixels is used to determine the surface normal. This means that the precise shadow boundary is not required by our method. The results section of the paper provides a detailed analysis of the methods presented and a comparison to ground truth. We also analyse the reflectance properties of a small number of skin samples to test the validity of the Lambertian model and point towards potential improvements to our method using the Oren–Nayar model. [Copyright &y& Elsevier]

Background Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). Results We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. Conclusions PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap. [ABSTRACT FROM AUTHOR]