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https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14648712#comment-14648712
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K S Sreenivasa Raghavan commented on SPARK-8486:
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Hello,
I am new to contribution, but I have some experience in PySpark as
I took some courses in Edx. Can you explain me about this? Please mentor me.
> SIFT 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
> Priority: Minor
>
> 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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