13,692 research outputs found
FPGA-Based Multimodal Embedded Sensor System Integrating Low- and Mid-Level Vision
Motion estimation is a low-level vision task that is especially relevant due to its wide range of applications in the real world. Many of the best motion estimation algorithms include some of the features that are found in mammalians, which would demand huge computational resources and therefore are not usually available in real-time. In this paper we present a novel bioinspired sensor based on the synergy between optical flow and orthogonal variant moments. The bioinspired sensor has been designed for Very Large Scale Integration (VLSI) using properties of the mammalian cortical motion pathway. This sensor combines low-level primitives (optical flow and image moments) in order to produce a mid-level vision abstraction layer. The results are described trough experiments showing the validity of the proposed system and an analysis of the computational resources and performance of the applied algorithms
Fast space-variant elliptical filtering using box splines
The efficient realization of linear space-variant (non-convolution) filters
is a challenging computational problem in image processing. In this paper, we
demonstrate that it is possible to filter an image with a Gaussian-like
elliptic window of varying size, elongation and orientation using a fixed
number of computations per pixel. The associated algorithm, which is based on a
family of smooth compactly supported piecewise polynomials, the
radially-uniform box splines, is realized using pre-integration and local
finite-differences. The radially-uniform box splines are constructed through
the repeated convolution of a fixed number of box distributions, which have
been suitably scaled and distributed radially in an uniform fashion. The
attractive features of these box splines are their asymptotic behavior, their
simple covariance structure, and their quasi-separability. They converge to
Gaussians with the increase of their order, and are used to approximate
anisotropic Gaussians of varying covariance simply by controlling the scales of
the constituent box distributions. Based on the second feature, we develop a
technique for continuously controlling the size, elongation and orientation of
these Gaussian-like functions. Finally, the quasi-separable structure, along
with a certain scaling property of box distributions, is used to efficiently
realize the associated space-variant elliptical filtering, which requires O(1)
computations per pixel irrespective of the shape and size of the filter.Comment: 12 figures; IEEE Transactions on Image Processing, vol. 19, 201
Partially Coherent Ptychography by Gradient Decomposition of the Probe
Coherent ptychographic imaging experiments often discard over 99.9 % of the
flux from a light source to define the coherence of an illumination. Even when
coherent flux is sufficient, the stability required during an exposure is
another important limiting factor. Partial coherence analysis can considerably
reduce these limitations. A partially coherent illumination can often be
written as the superposition of a single coherent illumination convolved with a
separable translational kernel. In this paper we propose the Gradient
Decomposition of the Probe (GDP), a model that exploits translational kernel
separability, coupling the variances of the kernel with the transverse
coherence. We describe an efficient first-order splitting algorithm GDP-ADMM to
solve the proposed nonlinear optimization problem. Numerical experiments
demonstrate the effectiveness of the proposed method with Gaussian and binary
kernel functions in fly-scan measurements. Remarkably, GDP-ADMM produces
satisfactory results even when the ratio between kernel width and beam size is
more than one, or when the distance between successive acquisitions is twice as
large as the beam width.Comment: 11 pages, 9 figure
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