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.
Jon A. Wcbbt and Edward Pervint Dfy2rtmc ut of Computer Scicuce Carnegie-Mellon University, Pittsburgh, PA 15217 tPerq Systems Corporation Pittsburgh, PA 15217 ABSTRACT We develop a theoretical framework for interpolating visual contours and apply it to subjective contours. The theory is based on the idea of consistency: a curve fitting algorithm must give consistent answers when presented with more data consistent with its hypothesis, or the same data under different conditions. Using this assumption, we prove that the subjective contour through two point-tangents is a parabola. Sample output of programs implementing the theory is provided. I. INTRODUCTION Subjective contours are curves filled in by the visual system in the absence of an explicit curve.