Scientific user facilities present a unique set of challenges for image processing due to the large volume of data generated from experiments and simulations. Furthermore, developing and implementing algorithms for real-time processing and analysis while correcting for any artifacts or distortions in images remains a complex task, given the computational requirements of the processing algorithms. In a collaborative effort across multiple Department of Energy national laboratories, the "MLExchange" project is focused on addressing these challenges. MLExchange is a Machine Learning framework deploying interactive web interfaces to enhance and accelerate data analysis. The platform allows users to easily upload, visualize, label, and train networks. The resulting models can be deployed on real data while both results and models could be shared with the scientists. The MLExchange web-based application for image segmentation allows for training, testing, and evaluating multiple machine learning models on hand-labeled tomography data. This environment provides users with an intuitive interface for segmenting images using a variety of machine learning algorithms and deep-learning neural networks. Additionally, these tools have the potential to overcome limitations in traditional image segmentation techniques, particularly for complex and low-contrast images.
A study of the impact of image noise on well-known range image curvature determination methods is presented here. The study considers 12 methods, and each is analyzed based on its performance at varying levels of input noise. The performance analyses consider quality factors of (1) absolute error, (2) correlation with correct, expected curvature values, and (3) signal-tonoise ratio (SNR). Curvature-based renderings are also presented for some data to provide basic visualizations of the impact of noise on one curvature-based task. The work can benefit tasks using range data (e.g., from Kinect or commercial-grade sensors).