Semi-automatic Foreground Extraction Martin & Andreas

The Work of Laerdal-Sophus Procedure –Breaking down images –Recomposition of images –Reshading Primary demand –Results must look realistic!

Present Segmentation Task Manual marking –Border marking –Tedious –Time consuming Labour intensive –The most time consuming part of the product line.

Our Task So, what can we do? –Some degree of automation –Faster of segmentation –Mile stone: 50% time cut-off on Segmenting and showing

Lazy-Snapping Speed –The method is fast enough to be interactive Easy user input –The operator uses mouse clicks to indicate which regions are inside/outside the obejct Based on energy minimisation via graphs –Min-cut / max-flow –Augmenting paths

Energy Minimisation We minimise a Gibbs energy to achieve a labeling Final labeling Data term Local Term

Constructing a Graph Final labeling Data term Local Term The data term is based on color information on background and foreground The local term is based on color differences between neighbouring pixels

Augmenting Paths st Bottleneck capacity = 5 We augment the flow by the bottleneck capacity, and get the residual capacities. st 72 0 Original capacities Saturated edge X st 72 The edges is now cut

Min-cut / max-flow We think of a network flow (S  T) Find the min-cut! while (path found S  T) /* augment the flow */ find the bottleneck capacity Δf of P /* saturate at least one edge */ subtract Δf from all edges in P end-while

Min-cut / max-flow The set of cut edges corresponds to the min-cut. The cut separates S from T and gives a labeling. The min-cut is found by pushing the biggest possible flow through the network: The max-flow