This research-to-practice WIP paper describes an approach to engage high school students in research through the utilization of citizen science tools embedded with Machine Learning (ML) models. In the context of fostering early engagement in scientific research among high school students, this paper explores the integration of citizen science and ML using SmartCS, an existing platform for creating citizen science smartphone applications. The process requires no prior programming knowledge, making it accessible to a broad range of students. For our approach, a group of high school students participated in a two-month-long summer research program, where they were introduced to the principles of citizen science as a method for data collection across diverse scientific projects from different research domains. The program's initial task involved students in the conceptualization of a citizen science project, adopted based on a thorough literature review, followed by the practical task of developing a smartphone application for data collection and educational purposes. Students either created new datasets or curated existing ones to train lightweight ML models for computer vision tasks, specifically focused on providing visual guidance within these mobile apps. The final task involved deploying these applications for public use and collecting user feedback. Our experience suggests that this approach not only enabled students to learn aspects of computer science and engineering, particularly in the area of ML model training and mobile application software development, but also allowed them to experience firsthand the significant role citizen science can play in collecting and analyzing scientific data.

We present a hybrid machine learning and flow analysis feature detection method, RipViz, to extract rip currents from stationary videos. Rip currents are dangerous strong currents that can drag beachgoers out to sea. Most people are either unaware of them or do not know what they look like. In some instances, even trained personnel such as lifeguards have difficulty identifying them. RipViz produces a simple, easy to understand visualization of rip location overlaid on the source video. With RipViz, we first obtain an unsteady 2D vector field from the stationary video using optical flow. Movement at each pixel is analyzed over time. At each seed point, sequences of short pathlines, rather a single long pathline, are traced across the frames of the video to better capture the quasi-periodic flow behavior of wave activity. Because of the motion on the beach, the surf zone, and the surrounding areas, these pathlines may still appear very cluttered and incomprehensible. Furthermore, lay audiences are not familiar with pathlines and may not know how to interpret them. To address this, we treat rip currents as a flow anomaly in an otherwise normal flow. To learn about the normal flow behavior, we train an LSTM autoencoder with pathline sequences from normal ocean, foreground, and background movements. During test time, we use the trained LSTM autoencoder to detect anomalous pathlines (i.e., those in the rip zone). The origination points of such anomalous pathlines, over the course of the video, are then presented as points within the rip zone. RipViz is fully automated and does not require user input. Feedback from domain expert suggests that RipViz has the potential for wider use.

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are underconstrained. As a result, reconstruction methods include embedded priors encoding the valid domain of faces. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? Does the input data need to be 3D, or will 2D data suffice? This paper takes a step toward answering these questions using synthetic data. A ground truth dataset is used to analyze accuracy obtainable from 2D landmarks, 3D landmarks, low quality 3D, high quality 3D, texture color, normals, dense 2D image data, and when regions of the face are missing. Since the data is synthetic it can be analyzed both with and without measurement error. This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs. The experimental results suggest that accuracy is severely limited when only 2D raw input data exists.

Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. System designers must choose a source of input data and then choose a reconstruction method to obtain a usable 3D face. If a particular application domain requires accuracy X, which kinds of input data are suitable? This paper takes a step toward answering this question. A variety of common input data types such as 2D landmarks and 3D scans are constructed from an existing high quality dataset. A morphable face model is then used to reconstruct 3D faces. By comparing to ground truth, an analysis of the relative error between different data types is obtained.