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Artificial intelligence (AI) and its teaching in the K-12 grades has been championed as a vital need for the United States due to the technology's future prominence in the 21st century. However, there remain several barriers to effective AI lessons at these age groups including the broad range of interdisciplinary knowledge needed and the lack of formal training or preparation for teachers to implement these lessons. In this experience report, we present ImageSTEAM, a teacher professional development for creating lessons surrounding computer vision, machine learning, and computational photography/cameras targeted for middle school grades 6-8 classes. Teacher professional development workshops were conducted in the states of Arizona and Georgia from 2021-2023 where lessons were co-created with teachers to introduce various specific visual computing concepts while aligning to state and national standards. In addition, the use of a variety of computer vision and image processing software including custom designed Python notebooks were created as technology activities and demonstrations to be used in the classroom. Educational research showed that teachers improved their self-efficacy and outcomes for concepts in computer vision, machine learning, and artificial intelligence when participating in the program. Results from the professional development workshops highlight key opportunities and challenges in integrating this content into the standard curriculum, the benefits of a co-creation pedagogy, and the positive impact on teacher and student's learning experiences. The open-source program curriculum is available at www.imagesteam.org. 

Light-transport represents the complex interactions of light in a scene. Fast, compressed, and accurate light-transport capture for dynamic scenes is an open challenge in vision and graphics. In this paper, we integrate the classical idea of Lissajous sampling with novel control strategies fordynamic light-transport applicationssuch as relighting water drops and seeing around corners. In particular, this paper introduces an improved Lissajous projector hardware design and discusses calibration and capture for a microelectromechanical (MEMS) mirror-based projector. Further, we show progress towards speeding up the hardware-based Lissajous subsampling for dual light transport frames, and investigate interpolation algorithms for recovering back the missing data. Our captured dynamic light transport results show complex light scattering effects for dense angular sampling, and we also show dual non-line-of-sight (NLoS) capture of dynamic scenes. This work is the first step towards adaptive Lissajous control for dynamic light-transport. 

In the next 50 years, the rise of computing and artificial intelligence (AI) will transform our society and it is clear that students will be forced to engage with AI in their careers. Currently, the United States does not have the infrastructure or capacity in place to support the teaching of AI in the K-12 curriculum. To deal with the above challenges, we introduce the use of visual media as a key bridge technology to engage students in grades 6-8 with AI topics, through a recently NSF funded ITEST program, labeled ImageSTEAM. Specifically, we focus on the idea of a computational camera, which rethinks the sensing interface between the physical world and intelligent machines, and enables students to ponder how sensors and perception fundamentally will augment science and technology in the future. Our 1st set of workshops (summer 2021) with teachers and students were conducted virtually due to recent pandemic, and the results and experiences will be shared and discussed in the conference. 

Lensless imaging is a new, emerging modality where image sensors utilize optical elements in front of the sensor to perform multiplexed imaging. There have been several recent papers to reconstruct images from lensless imagers, including methods that utilize deep learning for state-of-the-art performance. However, many of these methods require explicit knowledge of the optical element, such as the point spread function, or learn the reconstruction mapping for a single fixed PSF. In this paper, we explore a neural network architecture that performs joint image reconstruction and PSF estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. 

Visual simultaneous localization and mapping (SLAM) is an emerging technology that enables low-power devices with a single camera to perform robotic navigation. However, most visual SLAM algorithms are tuned for images produced through the image sensor processing (ISP) pipeline optimized for highly aesthetic photography. In this paper, we investigate the feasibility of varying sensor quantization on RAW images directly from the sensor to save energy for visual SLAM. In particular, we compare linear and logarithmic image quantization and show visual SLAM is robust to the latter. Further, we introduce a new gradient-based image quantization scheme that outperforms logarithmic quantization’s energy savings while preserving accuracy for feature-based visual SLAM algorithms. This work opens a new direction in energy-efficient image sensing for SLAM in the future 

As part of a professional development experience, inservice teachers were provided with lessons and activities emphasizing computer vision. Lessons focused on image processing, machine learning for visual data, and applications in robotics and visual media. Personal Construct Theory (Kelly, 1955) was used to answer the following research question: After middle school teachers engaged in professional development emphasizing computer vision, what changes in thinking occurred in relation to computer vision? Results showed changes in thinking in the form of a more refined understanding of computer vision and its role in STEM education.