We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of mechanical and dynamical systems (reliability, probability of failure, stability, etc.). Our algorithm locates contours using information from multiple sources, which are available in the form of relatively inexpensive, biased, and possibly noisy approximations to the original function. Considering multiple information sources can lead to significant cost savings. We also introduce the concept of contour entropy, a formal measure of uncertainty about the location of the zero contour of a function approximated by a statistical surrogate model.
This paper presents representation and logic for labeling contrast edges and ridges in visual scenes in terms of both surface occlusion (border ownership) and thinline objects. In natural scenes, thinline objects include sticksand wires, while in human graphical communication thinlines include connectors, dividers, and other abstract devices. Our analysis is directed at both natural and graphical domains. The basic problem is to formulate the logic of the interactions among local image events, specifically contrast edges, ridges, junctions, and alignment relations, such as to encode the natural constraints among these events in visual scenes. In a sparse heterogeneous Markov Random Field framework, we define a set of interpretation nodes and energy/potential functions among them. The minimum energy configuration found by Loopy Belief Propagation isshown to correspond to preferred human interpretation across a wide range of prototypical examples including important illusory contour figuressuch as the Kanizsa Triangle, as well as more difficult examples. Inpractical terms, the approach delivers correct interpretations of inherently ambiguous hand-drawn box-and-connector diagrams at low computational cost.
For a beauty junkie, I shamefully have never given makeup contouring a shot for fear of looking like a walking plastic doll due to my less-than-deft blending skills. I've watched plenty of tutorials on YouTube and I also studied "clown contouring" when it was a thing last year, but it always seemed to require more effort that I was willing to put in. But celebrity tattoo artist Kat Von D's new contouring makeup range landed on my desk and I figured I had to jump on it to see how a noob would do. Von D's eponymous cosmetics line will launch in Singapore in July at Sephora, and I've heard from the great beauty grapevine that her contour palettes and lipsticks are expected to sell like hotcakes. The face contour palette comes with three light and three dark powder shades, and will suit several skin tones.
There has been considerable interest in recent years in the possibility of segmenting anatomical structures as seen in three-dimensional (3D) magnetic resonance (MR) scans. By "segment", we imply the labelling of the image at every voxel with the correct anatomical descriptor(s). It may be argued that such a labelling is ill-defined in that a crisp anatomical boundary may not exist at the resolution of the MR image. In this initial work we simply ignore difficulties associated with the notion of "ground truth". There are many possible applications for a successful segmentation.