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https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Feynman Liang updated SPARK-8486:
---------------------------------
Description:
Scale invariant feature transform (SIFT) is a method to transform images into
dense vectors describing local features which are invariant to scale and
rotation. (Lowe, IJCV 2004, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
We can implement SIFT in Spark ML pipelines as a
org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the SIFT
transformer should output an Array[Numeric] of the SIFT features present in the
image.
Depending on performance, approximating Laplacian of Gaussian by Difference of
Gaussian (traditional SIFT) as described by Lowe can be even further improved
using box filters (aka SURF, see Bay, ECCV 2006,
http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).
was:
Scale invariant feature transform (SIFT) is a method to transform images into
dense vectors describing local features which are invariant to scale and
rotation. (Lowe, IJCV 2004, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
We can implement SIFT in Spark ML pipelines as a
[[org.apache.spark.ml.Transformer]]. Given an image Array[Array[Numeric]], the
SIFT transformer should output an Array[Numeric] of the SIFT features present
in the image.
Depending on performance, approximating Laplacian of Gaussian by Difference of
Gaussian (traditional SIFT) as described by Lowe can be even further improved
using box filters (aka SURF, see Bay, ECCV 2006,
http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).
> SIFT/SURF Feature Extractor
> ---------------------------
>
> Key: SPARK-8486
> URL: https://issues.apache.org/jira/browse/SPARK-8486
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Feynman Liang
>
> Scale invariant feature transform (SIFT) is a method to transform images into
> dense vectors describing local features which are invariant to scale and
> rotation. (Lowe, IJCV 2004, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
> We can implement SIFT in Spark ML pipelines as a
> org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the
> SIFT transformer should output an Array[Numeric] of the SIFT features present
> in the image.
> Depending on performance, approximating Laplacian of Gaussian by Difference
> of Gaussian (traditional SIFT) as described by Lowe can be even further
> improved using box filters (aka SURF, see Bay, ECCV 2006,
> http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).
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