Defining resolution within satellite imagery is normally achieved through the observation of edge targets or is visually graded (e.g., National Imagery Interpretability Rating Scale (NIIRS)) for the level of detail observed. These methods are significantly disadvantaged by not directly measuring fundamental quantities related to the imaging system. Recently, ground mirror-based systems have been developed which can mimic an ideal point source observable by satellite systems allowing direct observation of an imaging system point response function (PRF). This fundamental quantity of an imaging system defines the end-to-end performance of the optics and detector. In this paper, we illustrate the use of the PRF in a new approach called the point-pair resolution technique (PPRT) which characterizes separability between two ideal point sources. We compare real and simulated point-pairs to demonstrate validity.

The FLARE Network provides NIST traceable radiometric calibration for Earth Observation sensors in the 350 - 2500 nm range. This is achieved with convex mirrors which relay an image of the sun to the sensor under test. Network arrays function as calibrated stars on the ground, providing point sources which can also be used to derive spatial and resolution performance metrics. Instrumented nodes are active at locations in the US, with planned global expansion. The performance of the network has been validated against sensors with varied radiometric performances and Ground Sample Distances, including multiple commercial sensors, Landsat 8, and Sentinel 2 craft. The FLARE Network web portal allows on-demand calibration for any satellite at costs fractional to traditional diffuse-target campaigns.

Vectorization turns photographs into vector art. Manual vectorization, where the artist traces over the image by hand, requires skill and time. On the other hand, automatic approaches allow users to generate a result by setting a few global parameters. However, global settings often leave too much detail/complexity in some parts of the image while missing important details in others. We propose interactive vectorization tools that offer more local control than automatic systems, but are more powerful and high-level than simple curve editing. Our system enables novices to vectorize images significantly faster than even experts with state-of-the-art tools.

We consider the problem of two-frame depth from defocus in conditions unsuitable for existing methods yet typical of everyday photography: a non-stationary scene, a handheld cellphone camera, a small aperture, and sparse scene texture. The key idea of our approach is to combine local estimation of depth and flow in very small patches with a global analysis of image content-3D surfaces, deformations, figure-ground relations, textures. To enable local estimation we (1) derive novel defocus-equalization filters that induce brightness constancy across frames and (2) impose a tight upper bound on defocus blur-just three pixels in radius-by appropriately refocusing the camera for the second input frame. For global analysis we use a novel splinebased scene representation that can propagate depth and flow across large irregularly-shaped regions. Our experiments show that this combination preserves sharp boundaries and yields good depth and flow maps in the face of significant noise, non-rigidity, and data sparsity.

We present a method for constructing almost-everywhere curvature-continuous, piecewise-quadratic curves that interpolate a list of control points and have local maxima of curvature only at the control points. Our premise is that salient features of the curve should occur only at control points to avoid the creation of features unintended by the artist. While many artists prefer to use interpolated control points, the creation of artifacts, such as loops and cusps, away from control points has limited the use of these types of curves. By enforcing the maximum curvature property, loops and cusps cannot be created unless the artist intends for them to be. To create such curves, we focus on piecewise quadratic curves, which can have only one maximum curvature point. We provide a simple, iterative optimization that creates quadratic curves, one per interior control point, that meet with G 2 continuity everywhere except at inflection points of the curve where the curves are G 1 . Despite the nonlinear nature of curvature, our curves only obtain local maxima of the absolute value of curvature only at interpolated control points.

Estimating the amount of blur in a given image is important for computer vision applications. More specifically, the spatially varying defocus point-spread-functions (PSFs) over an image reveal geometric information of the scene, and their estimate can also be used to recover an all-in-focus image. A PSF for a defocus blur can be specified by a single parameter indicating its scale. Most existing algorithms can only select an optimal blur from a finite set of candidate PSFs for each pixel. Some of those methods require a coded aperture filter inserted in the camera. In this paper, we present an algorithm estimating a defocus scale map from a single image, which is applicable to conventional cameras. This method is capable of measuring the probability of local defocus scale in the continuous domain. It also takes smoothness and color edge information into consideration to generate a coherent blur map indicating the amount of blur at each pixel. Simulated and real data experiments illustrate excellent performance and its successful applications in foreground/background segmentation.