Motion-compensated motion reconstruction (mcmr) is a powerful concept with considerable potential, consisting of two coupled sub-problems : motion estimation, assuming a known image, and image reconstruction, assuming known motion.
In this work, we propose a learning-based self-supervised framework for mcmr, to efficiently deal with nonrigid motion corruption in cardiac mrimaging.
We demonstrate a lensless three-dimensional microscope that forms image through a single layer of microlens array and reconstructs objects through a geometrical-optics-based pixel back projection algorithm and background suppressions.
Our system enables near real-time object reconstructions across a large volume of 23x23x5 mm^3, with a lateral resolution of 40 um and axial resolution of 300 um.
Pose tracking of tensegrity robots, which are composed of rigid compressive elements (rods) and flexible tensile elements (e.g., cables), has been recognized as a grand challenge in this domain.
This work aims to address what has been recognized as a grand challenge in this domain, i.e., the pose tracking of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot s cables.
This paper proposes a novel location-free camouflage generation network (lcg-net) that generates result by one inference of high-level features of foreground and background image, and is hundreds of times faster than previous methods.
Specifically, a position-aligned structure fusion (psf) module is devised to guide structure feature fusion based on the point-to-point structure similarity of foreground and background, and introduce local appearance features point-by-point.
Machine learning is an emerging field which holds the promise to bypass the fundamental computational bottleneck caused by traditional iterative solvers in critical applicationsrequiring near-real-time optimization.
We denote our method as, learning to optimize the optimization process (loop).
Local geometry optimization is an important part of both computational materialsand surface science because it is the path to finding ground state atomic structures and reaction pathways.
This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (dft), which are computationally expensive, and which limit the speed of the optimization algorithms.