Projects: Algorithms & Architectures
- Closed-form change detection from moving cameras
- Model-free handling of nonuniform apparent motion from 3D scenes
- Handles failure modes from competing single-camera methods
- Approach generalizes to simplifying other moving-camera problems
- A linear filter that focuses on a volume instead of a plane
- Enhanced imaging in low light and through murky water and particulate
- Derivation of the hypercone / hyperfan as the fundamental shape of the light field in the frequency domain
Light Field Depth-Velocity Filtering
- A 5D light field + time filter that selects for depth and velocity
- C. U. S. Edussooriya, D. G. Dansereau, L. T. Bruton, and P. Agathoklis, “Five-dimensional (5-D) depth-velocity filtering for enhancing moving objects in light field videos,” IEEE Transactions on Signal Processing (TSP), vol. 63, no. 8, pp. 2151–2163, April 2015. Available here.
Gradient-based depth estimation from light fields
- Depth from local gradients
- Simple ratio of differences, easily parallelized
- D. G. Dansereau and L. T. Bruton, “Gradient-based depth estimation from 4D light fields,” in Intl. Symposium on Circuits and Systems (ISCAS), 2004, vol. 3, pp. 549–552. Available here.
Plenoptic flow for closed-form visual odometry
- Generalization of 2D Lucas–Kanade optical flow to 4D light fields
- Links camera motion and apparent motion via first-order light field derivatives
- Solves for 6-degree-of-freedom camera motion in 3D scenes, without explicit depth models
- D. G. Dansereau, I. Mahon, O. Pizarro, and S. B. Williams, “Plenoptic flow: Closed-form visual odometry for light field cameras,” in Intelligent Robots and Systems (IROS), 2011, pp. 4455–4462. Available here.
- see also Ch.5 of D. G. Dansereau, “Plenoptic signal processing for robust vision in field robotics,” PhD thesis, Australian Centre for Field Robotics, School of Aerospace, Mechanical; Mechatronic Engineering, The University of Sydney, 2014. Available here.