[
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 scale and rotation invariant
method to transform images into matrices describing local features. (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 ArrayArray[[Numeric]] of the SIFT features for the
provided image.
The implementation should support computation of SIFT at predefined interest
points, every kth pixel, and densely (over all pixels).
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 ArrayArray[[Numeric]] of the SIFT features for the
provided 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 Transformer
> -----------------------------
>
> 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 scale and rotation invariant
> method to transform images into matrices describing local features. (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 ArrayArray[[Numeric]] of the SIFT features
> for the provided image.
> The implementation should support computation of SIFT at predefined interest
> points, every kth pixel, and densely (over all pixels).
> 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).
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
