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Removing tiling artefacts in ToF-SIMS data

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The raising number of elderly people urges the
research of systems able to monitor and support people inside
their domestic environment. An automatic system capturing
data about the position of a person in the house, through
accelerometers and RGBd cameras can monitor the person
activities and produce outputs associating the movements
to a given tasks or predicting the set of activities that will
be executes. We considered, for the task the classification
of the activities a Deep Convolutional Neural Network. We
compared two different deep network and analyzed their
outputs.Classification of indoor actions through deep neural networks

Classification of indoor actions through deep neural networksCognitive Robotics and Social Sensing Lab - CNR - ICAR

The raising number of elderly people urges the
research of systems able to monitor and support people inside
their domestic environment. An automatic system capturing
data about the position of a person in the house, through
accelerometers and RGBd cameras can monitor the person
activities and produce outputs associating the movements
to a given tasks or predicting the set of activities that will
be executes. We considered, for the task the classification
of the activities a Deep Convolutional Neural Network. We
compared two different deep network and analyzed their
outputs.Classification of indoor actions through deep neural networks

Classification of indoor actions through deep neural networksCognitive Robotics and Social Sensing Lab - CNR - ICAR

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- 1. The painful removal of tiling artefacts in hypersprectral data Sevvandi Kandanaarachchi, Wil Gardner, David Alexander, Ben Muir, Philippe Chouinard, Sheila Crewther, David Scurr, Mark Halliday, Paul Pigram AI-OPT 2023 15-12-2023
- 3. What do you get? https://onlinelibrary.wiley.com/doi/epdf/10.1002/jex2.110 • A data cube • 2 spatial dimensions • 1 spectral dimension • 10 mm x 10 mm -> 1GB data
- 4. What are tiling artefacts? A noise only portion A mouse brain sample
- 5. What’s the story? • ToF-SIMS data have tiling artefacts • We want to remove them – to make the data cleaner • Multiple methods to remove tiling artefacts • Didn’t pick one, instead we did a comparison study • We did a statistical analysis and a survey
- 6. Comparison study • 4 Datasets • 1 biological dataset (mouse brain), 2 dye datasets, 1 resistor • 6 Methods • 1 tailored method for tiling removal (Seamless Stitching) – (Legesse et al, 2015) • 3 statistical methods (LDA, tensor decomposition, linear multiplicative) • 2 simple benchmarks (averaging and interpolating)
- 8. Mouse Dataset • Biological dataset • Curves • Intensity variation
- 9. Dye and Polymer datasets • The same shape - circles • Repetition • More regular repetition in Dye compared to polymer • No intricate curves
- 10. Resistor dataset • Same structure repeated twice • No intricate curves • Bigger tiles compared to the other datasets
- 11. Tiling artefact removal methods • Seamless stitching (Legesse at al, 2015) • Linear Discriminant Analysis • Tensor Decomposition • Linear Multiplicative method • Simple Averaging • Simple Interpolation
- 12. Methods Approximate Tiling Artefacts and Remove Smoothen around Edges Hybrid 1. Tensor Decomposition 2. LDA 3. Linear Multiplicative method 1. Simple averaging 2. Simple Interpolation Seamless Stitching Approximation Original
- 13. Candecomp/PARAFAC tensor decomposition 𝑎𝑖𝑗𝑘 ≈ 𝑥𝑖 𝑦𝑗𝑧𝑘 𝑥 𝑦 𝑧 1 component
- 14. Candecomp/PARAFAC tensor decomposition 𝑎𝑖𝑗𝑘 ≈ 𝑥𝑖 1 𝑦𝑗 1 𝑧𝑘 1 + 𝑥𝑖 2 𝑦𝑗 2 𝑧𝑘 2 𝑥(1) 𝑦(1) 𝑧(1) 𝑥(2) 𝑦(2) 𝑧(2) + 2 components
- 15. Tensor decomposition method • Find the tensor decomposition of the data using 1 set of components • The first component generally gives the tiling artefact • So remove it • 𝑧𝑖𝑗𝑘 = 𝑎𝑖𝑗𝑘 − 𝑥𝑖 1 𝑦𝑗 1 𝑧𝑘 1
- 16. Linear multiplicative method • Model the noise • In this example noise increases as x and y increases • Can we model this? • 𝑧𝑥,𝑦 = 𝑧𝑥,𝑦 × 𝑛𝑥,𝑦 • where 𝑧𝑥,𝑦 = observed value at 𝑥, 𝑦 • 𝑧𝑥,𝑦 is the true value at 𝑥, 𝑦 • 𝑛𝑥,𝑦 is the noise at 𝑥, 𝑦 is the relative position on the tile • 𝑥 = 𝑥 𝑚𝑜𝑑 𝑡𝑖𝑙𝑒𝑠𝑖𝑧𝑒 • 𝑦 = 𝑦 𝑚𝑜𝑑 𝑡𝑖𝑙𝑒𝑠𝑖𝑧𝑒
- 17. Model • 𝑧𝑥,𝑦 = 𝑧𝑥,𝑦 × 𝑛𝑥,𝑦 • Using all the tiles model 𝑛𝑥,𝑦 • 𝑛𝑥,𝑦 = 𝑎0 + 𝑎𝑥 𝑥 + 𝑎𝑦 𝑦 • Then 𝑧𝑥,𝑦/𝑛𝑥,𝑦 = 𝑧𝑥,𝑦 • Instead of modelling using one tile, we can use all the tiles to model it
- 19. Dye data
- 20. Polymer data
- 21. Resistor data
- 22. How do we evaluate these methods? • Mutual information between position and intensity • Correlation • Measure periodic nature and its reduction – time series seasonality Statistical methods Survey • Ask two types of questions. • How well the information was retained after tiling artefact removal. • How well the artefacts were removed No standard evaluation methods in literature for ToF-SIMS tiling artefact removal.
- 24. Results from the survey Rank Brain - Remove artefacts Brain - Retain information • Depending on the question asked participants selected different methods.
- 25. Summary of results • Overall Linear Multiplicative and Seamless Stitching methods were preferred more. • Seamless stitching was popular for the mouse brain dataset • No method was always supreme for all datasets– No free lunch! • Depending on the dataset characteristics, the preferred methods were different. • Message: caution researchers not to latch on to one method. Try out many methods and select the best one for the dataset.
- 26. Resources • Code: https://github.com/sevvandi/supplementary_material/tree/master/ToF- SIMS • Paper: https://pubs.acs.org/doi/10.1021/acs.analchem.3c03887
- 27. Thank you!
- 29. kk = 1998
- 31. Strong periodic signal • In a time series context seasonality • Time series features can capture this • 𝑦𝑡 = 𝑓𝑡 + 𝑠𝑡 + 𝑒𝑡 (STL decomposition) • 𝑓𝑡 - trend • 𝑠𝑡 - seasonality • 𝑒𝑡 - error term • Strength of seasonality = 1 − 𝑣𝑎𝑟 𝑒𝑡 𝑣𝑎𝑟 𝑠𝑡+𝑒𝑡 • Strength of seasonality = 1 − 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑜𝑓 𝑒𝑟𝑟𝑜𝑟 𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒 𝑜𝑓( 𝑒𝑟𝑟𝑜𝑟+𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙𝑖𝑡𝑦)
- 32. For this dataset • We need to set the period. In this case it is 10. • X-seasonality = 0.93 • Y-seasonality = 0.89 • There is a strong seasonal component. • Of the remaining data if we recompute parafac and find seasonality we get • X-remaining-seasonality = 0.02 • Y-remaining-seasonality = 0.009
- 33. k = 56 - more subtle
- 34. k = 964
- 35. New linear, multiplicative method A noise only portion