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https://issues.apache.org/jira/browse/SPARK-8491?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feynman Liang updated SPARK-8491:
---------------------------------
Description:
DAISY (Tola et al, PAMI 2010,
http://infoscience.epfl.ch/record/138785/files/tola_daisy_pami_1.pdf) is
another local image descriptor utilizing histograms of local orientation
similar to SIFT. However, one key difference is that the weighted sum of
gradient norms used in SIFT's orientation assignment is replaced by convolution
with Gaussian kernels. This provides a significant speedup in computing dense
descriptors.
We can implement DAISY 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 DAISY features for
the provided image.
The convolution operation can leverage GPU parallelism for efficiency.
A C++/MATLAB reference implementation is available at
http://cvlab.epfl.ch/software/daisy.
was:
DAISY is another local image descriptor utilizing histograms of local
orientation similar to SIFT. However, one key difference is that the weighted
sum of gradient norms used in SIFT's orientation assignment is replaced by
convolution with Gaussian kernels. This provides a significant speedup in
computing dense descriptors.
We can implement DAISY 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 DAISY features for
the provided image.
The convolution operation can leverage GPU parallelism for efficiency.
> DAISY Feature Transformer
> -------------------------
>
> Key: SPARK-8491
> URL: https://issues.apache.org/jira/browse/SPARK-8491
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Feynman Liang
>
> DAISY (Tola et al, PAMI 2010,
> http://infoscience.epfl.ch/record/138785/files/tola_daisy_pami_1.pdf) is
> another local image descriptor utilizing histograms of local orientation
> similar to SIFT. However, one key difference is that the weighted sum of
> gradient norms used in SIFT's orientation assignment is replaced by
> convolution with Gaussian kernels. This provides a significant speedup in
> computing dense descriptors.
> We can implement DAISY 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 DAISY features
> for the provided image.
> The convolution operation can leverage GPU parallelism for efficiency.
> A C++/MATLAB reference implementation is available at
> http://cvlab.epfl.ch/software/daisy.
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