Abstract: Techniques are presented for controlling the amount of temporal noise in certain animation sequences. Sketchy animation sequences are received in an input in a digital form and used to create an altered version of the same animation with temporal coherence enforced down to the stroke level, resulting in a reduction of the perceived noise. The amount of reduction is variable and can be controlled via a single parameter to achieve a desired artistic effect.

Abstract: A method is provided for sketch segmentation via smart scribbles, the results of which are especially suitable for interactive real-time graphics editing applications. A vector-based drawing may be segmented into labels based on input scribbles provided by a user. By organizing the labeling as an energy minimization problem, an approximate solution can be found using a sequence of binary graph cuts for an equivalent graph, providing an optimized implementation in a polynomial time suitable for real-time drawing applications. The energy function may include time, proximity, direction, and curvature between strokes as smoothness terms, and proximity, direction, and oriented curvature between strokes and scribbles as data terms. Additionally, the energy function may be modified to provide for user control over locality control, allowing the selection of appropriately sized labeling regions by scribble input speed or scribble input pressure.

Abstract: Approaches for optimizing computation of minimum cut or maximum flow on graphs comprising a plurality of nodes and edges with grid-like topologies are disclosed. Embodiments exploit the regular structure of input graphs to reduce the memory bandwidth—a main bottleneck of popular max-flow/min-cut algorithms based on finding augmenting paths on a residual graph (such as Ford-Fulkerson [1956] or Boykov-Kolmogorov [2004]). Disclosed embodiments allow more than 200% speed-up without sacrificing optimality of the final solution, which is crucial for many computer vision and graphics applications. Method and system embodiments replace standard linked list representation of general graphs with a set of compact data structures with blocked memory layout that enables fixed ordering of edges and implicit branchless addressing of nodes.

Abstract: Approaches for optimizing computation of minimum cut or maximum flow on graphs comprising a plurality of nodes and edges with grid-like topologies are disclosed. Embodiments exploit the regular structure of input graphs to reduce the memory bandwidth—a main bottleneck of popular max-flow/min-cut algorithms based on finding augmenting paths on a residual graph (such as Ford-Fulkerson [1956] or Boykov-Kolmogorov [2004]). Disclosed embodiments allow more than 200% speed-up without sacrificing optimality of the final solution, which is crucial for many computer vision and graphics applications. Method and system embodiments replace standard linked list representation of general graphs with a set of compact data structures with blocked memory layout that enables fixed ordering of edges and implicit branchless addressing of nodes.