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A GENERALISED APPROXIMATE NEAREST NEIGHBOUR FIELD (ANNF) COMPUTATION FRAMEWORK BETWEEN SOURCE AND TARGET IMAGE

Approximate Nearest Neighbour Field (ANNF) computations are a recent development in the image processing community which have gained wide popularity, especially in the graphics community, due to their fast computation times. In this abstract, we propose FeatureMatch, a generalised approximate nearest-neighbour field (ANNF) computation framework, between a source and target ...

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BVCS: SHARING VISUAL SECRETS IN SINGLE IMAGE RANDOM DOT STEREOGRAMS

Visual cryptography (VC) is a technique that encrypts a secret image into n shares, with each participant holding one  share;  Conventional   VCSs   suffer   from   a   transmission   risk problem  because  the  noise-like  shares  will  raise  the  suspicion of  attackers  and  the  attackers  might  intercept the  transmission. Previous   research   has   involved   in   ...

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EFFICIENT LEARNING FOR IMAGE STITCHING WITH MOVING DIRECT LINEAR TRANSFORMATION

This abstract propose as-projective-as-possible warps, i.e., warps that aim to be globally projective, yet allow local non-projective deviations to account for violations to the assumed imaging conditions.  Based on a novel estimation technique called Moving Direct Linear Transformation (Moving DLT), our method seamlessly bridges image regions that are inconsistent with ...

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A NOVEL HUB-BASED CLUSTERING FOR HIGH-DIMENSIONAL DATA

Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points. Hubness is  the  tendency  of  some  data  points  in  high-dimensional data  sets  to  occur   much  more  frequently  in k-nearest-neighbor lists of other points than the rest of ...

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