Gilad Tsur

FasT-Match: Fast Affine Template Matching

Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sub linear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results. To the best of our knowledge, this is the first template matching algorithm which is guaranteed to handle arbitrary 2D affine transformations.

Acquaintance Time of a Graph

We define the following parameter of connected graphs. For a given graph

Fast-Match: Fast Affine Template Matching

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The Power of an Example: Hidden Set Size Approximation Using Group Queries and Conditional Sampling

we place one agent in each vertex of

Testing Properties of Sparse Images

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Testing computability by width-2 OBDDs where the variable order is unknown

. Every pair of agents sharing a common edge is declared to be acquainted. In each round we choose some matching of

On Approximating the Number of Relevant Variables in a Function

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Testing computability by width-two OBDDs

(not necessarily a maximal matching), and for each edge in the matching the agents on this edge swap places. After the swap, again, every pair of agents sharing a common edge become acquainted, and the process continues. We define the \emph{acquaintance time} of a graph

Testing Computability by Width Two OBDDs

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On approximating the number of relevant variables in a function

, denoted by