demonstration study for applying aved to still images from station m update, next steps, workflow...
DESCRIPTION
MBARI May 13rd, Updates Modifications completed to core AVED software for still frame processing Modifications completed to core AVED software for still frame processing Required changes in segmentation and saliency modelRequired changes in segmentation and saliency model Added customized cmd-line option for processing time-lapse imagesAdded customized cmd-line option for processing time-lapse images --mbari-timelapse-stills --mbari-timelapse-stills Examples StaM4211PsychroSeries/WhiteEchSeries: Examples StaM4211PsychroSeries/WhiteEchSeries: Working on getting more compute resources to process a demonstration data set Working on getting more compute resources to process a demonstration data setTRANSCRIPT
Demonstration Study for Applying AVED to Demonstration Study for Applying AVED to Still Images from Station M Still Images from Station M
MBARI Internal Project 900905
Update, next steps, workflow overviewUpdate, next steps, workflow overview
Danelle ClineDanelle Cline
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AgendaAgenda UpdatesUpdates
Next steps discussionNext steps discussion
Data workflow overview Data workflow overview
Action itemsAction items
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UpdatesUpdates Modifications completed to core AVED software for still frame Modifications completed to core AVED software for still frame
processingprocessing• Required changes in segmentation and saliency modelRequired changes in segmentation and saliency model• Added customized cmd-line option for processing time-lapse imagesAdded customized cmd-line option for processing time-lapse images
--mbari-timelapse-stills--mbari-timelapse-stills
Examples StaM4211PsychroSeries/WhiteEchSeries:Examples StaM4211PsychroSeries/WhiteEchSeries:
Working on getting more compute resources to process a Working on getting more compute resources to process a demonstration data setdemonstration data set
QuickTime™ and aMPEG-4 Video decompressor
are needed to see this picture. QuickTime™ and aMPEG-4 Video decompressor
are needed to see this picture.
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Next StepsNext Steps Now that we know the types of possible Now that we know the types of possible
detections…detections…
Decide use case for demonstrationDecide use case for demonstration• This will drive what and how much data to This will drive what and how much data to
process, and what kind of training libraries to process, and what kind of training libraries to create for image classification.create for image classification.
Example use cases:Example use cases:• Process a collection looking for temporal Process a collection looking for temporal
changes in fauna and structures on the changes in fauna and structures on the seafloor, focusing on the sessile fauna seafloor, focusing on the sessile fauna polychaete(Paradiopatra) burrows and glass polychaete(Paradiopatra) burrows and glass sponge (Hyalinacea) stalks .sponge (Hyalinacea) stalks .
• Process a collection around major El Niño La Niña events between 1997, searching for a few select animals previously analyzed by hand to ground truth against AVED
• Other ideas? Other ideas?
Example AVED events from Sta4211 image set
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Data Workflow Data Workflow
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AVED ProcessAVED Process
Image Preprocessing
•Scale and reformat•Histogram equalize•Mask equipment,
•time code overlays, •black bars, etc.
Post-processing
Segmentation and
Tracking
Detectionevents.XML
Every frame
Every frame
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AVED EditorAVED Editor
• Optional, but can useful for removing “false Optional, but can useful for removing “false detections”, or combining events detections”, or combining events
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ClassificationClassification Matlab program developed by Perona student Marc’Aurelio Matlab program developed by Perona student Marc’Aurelio
Ranzato at Caltech and Universita’ degli studi di PadovaRanzato at Caltech and Universita’ degli studi di Padova• Developed to analyze biological particlesDeveloped to analyze biological particles• Based on extracting features using Based on extracting features using
local jets (Schmid et al. 1997) (local jets (Schmid et al. 1997) (convolution of convolution of the image with a derivative of Gaussian kernel)the image with a derivative of Gaussian kernel)
image and power spectrum principal image and power spectrum principal components (Torralba et al. 2003)components (Torralba et al. 2003)
• Model training data with mixture of Gaussians (Choudrey Model training data with mixture of Gaussians (Choudrey and Roberts 2003)and Roberts 2003)
Implemented in MatlabImplemented in Matlab• processes grayscale square subimages of the processes grayscale square subimages of the
segmented scene containing the object to be classifiedsegmented scene containing the object to be classified
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Classifier ExampleClassifier Examplesmall benthic image setsmall benthic image set
QuickTime™ and aNone decompressor
are needed to see this picture.
Example training imagesExample training images
LeukotheleLeukothele
otherother
RathbunasterRathbunaster
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Action ItemsAction Items
1.1. Provide the data set (Ken, Jake, Mike)Provide the data set (Ken, Jake, Mike)
2.2. Engineer the workflow to process the Engineer the workflow to process the data set (Danelle) data set (Danelle)
When steps 1 and 2 complete, can start When steps 1 and 2 complete, can start on creating a training library (Linda)on creating a training library (Linda)
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Q&AQ&A Project #900905Project #900905
Project wiki: Project wiki: https://oceana.mbari.org/confluence/display/AVEDSTILL/