Background The rise of autonomous machines navigating the changing outdoor environment requires not only the use of line-of-sight tracking, but also non-line of sight (NLOS) tracking. NLOS imaging techniques are divided into active and passive approaches. Active methods employ high-temporal-resolution light sources and time-resolved detectors which...

Background Foreground and background segmentation is an important part of computer vision tasks including surveillance, remote sensing, and environmental monitoring. However, in long-range imaging for outdoor environments, substantial image distortions can occur due to atmospheric turbulence. These distortions limit the effectiveness of segmentation...

Synthetic aperture sonar (SAS) is an active acoustic imaging technique that coherently combines data from a moving array to form high-resolution imagery, especially of underwater environments. The moving array in SAS collects both magnitude and phase information which allows coherent integration methods to achieve resolution parallel to the sensor...

­Background The Earth recorded its largest surface temperature increase between the years of 2010 and 2020. The temperature increased by nearly 1.2°F over that period which resulted in the sixth highest surface temperature on record. In urban environments with large condensed populations, there is a growing need to examine the effects that...

Removing interferences and improving quality of degraded speech via speech enhancement (SE) is important in many applications such as telecommunications, speech recognition, voice over IP, hearing aids, and more. One major class of SE techniques includes machine learning, which is effective at enhancing speech quality, however, they require complex...

Background Hyperspectral unmixing—a method of imaging where light radiance is densely sampled at multiple wavelengths—is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying...

Background Object tracking is one of the most common applications for computer vision. In particular, it is utilized in surveillance, autonomous vehicles, robotics, and human-computer interaction. One main challenge for object tracking is the hardware limitations of tracking fast-moving objects. Fast-moving objects can induce motion blur at the image...

Background A critical performance requirement for embedded computer vision is energy-efficiency in order to preserve battery life for mobile and autonomous platforms. In particular, the image sensor and readout can take up a significant amount of energy in a computer vision pipeline, particularly if the sensor is capturing and processing video data...

Background Non-line-of-sight (NLOS) imaging is an emerging field of research with applications that include autonomous vehicle collision avoidance, search and rescue operations, industrial inspection, and endoscopy. Because NLOS objects are outside of the line-of-sight (LOS) of both the camera and illumination source(s), computational algorithms are...