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Feynman Liang updated SPARK-8486: --------------------------------- Summary: SIFT/SURF Feature Extractor (was: SIFT Feature Extractor) > 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 as described by Lowe can be even further improved using box > filters (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