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https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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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). Furthermore, the
implementation should support various approximations for approximating the
Laplacian of Gaussian using Difference of Gaussian (as described by Lowe).
was:
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). Furthermore, the
implementation should support various approximations for approximating the
Laplacian of Gaussian. In addition to approximating using Difference of
Gaussian (as described by Lowe), we should support
* SURF approximation using box filters (Bay, ECCV 2006,
http://www.vision.ee.ethz.ch/~surf/eccv06.pdf) should also be supported.
* DAISY
> SIFT/SURF/DAISY 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). Furthermore, the
> implementation should support various approximations for approximating the
> Laplacian of Gaussian using Difference of Gaussian (as described by Lowe).
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