class: center, middle, inverse, title-slide # Weekly Update for September 17 ### 2018/09/17 --- class: inverse # About Slides --- class: primary # New CSAFE slide template We're now using [`xaringan`](https://github.com/yihui/xaringan) What's changed: - New person slide: ```` --- class: inverse # Your Name ```` --- class: secondary - New content slide with title: ```` --- class: primary # Title of slide Slide content ```` - New content slide without title: ```` --- class: secondary Slide content with no title on slide ```` --- class: inverse # Sample User --- class: primary # Sample Slide - Sample Table. Sam Tyner talked about the three must-haves of your summary: | Must-have | It means | | :------ | :------- | | Context | Why are you doing what you're doing? "I'm working on X project in order to Y" | | Content | What are you doing? "I wrote X function that does Y" or "I ran a simulation of Z" | | Conclusion | What did you learn? "This will help me because it..." or "This important because it gets us to..." | --- class: secondary - If you are going to add an image, **create a directory** with your name within "images" folder. For example, "images/**guillermo**/sample_image.JPG" <img src = "images/guillermo/sample_image.JPG" width="35%"/> --- class: inverse # Amy --- class: primary # Fall Semester Goals - **Data Collection** <span style="color:red">Application is in 'IRB Staff Review' phase.</span> - Expand Bayesian hierarchical model - **Formal Model Selection** <span style="color:red">Deviance Information Criterion (DIC)</span> - Tests and Intervals - Validation - Write and submit a paper on that modeling. - Exploratory analysis of 'next level' features (AAFS in February) - Work with Ben and Nick --- class: secondary ## IRB Manager - <br>*"One form to rule them all"* <img src="images/amy/IRB_app.png" width="90%"> --- class: primary # DIC **Context:** Formal model selection with DIC. How many grapheme types to use in modeling? <br> <img src="images/amy/model_selection_example.png" width="90%"> --- class: primary # DIC **Content:** <img src="images/amy/DICbug_local.png" width="45%"/> <img src="images/amy/DICbug_server.png" width="43.8%"/> --- class: primary # DIC **Conclusion:** - Work with IT to update server - Other model selection techniques. - Compositional data. - Borrow from topic modeling literature? --- class: inverse # Ben --- class: primary # Progress! <img src = "images/ben/corrections.png" width="85%"/> - Checks binary image for predefined patterns. - Outputs coordinates for corrections, marked by dots. --- class: secondary <img src = "images/ben/pp1.png" width="35%"/> <img src = "images/ben/pp2.png" width="35%"/> - F. W. M. Stentiford and R. G. Mortimer, "Some new heuristics for thinning binary handprinted characters for OCR," in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-13, no. 1, pp. 81-84, Jan.-Feb. 1983. doi: 10.1109/TSMC.1983.6313034 - Checks for a variety of masks - Documentation on my branch (ben) --- class: inverse # Nick --- class: secondary ### Node Count Extraction  Node Counts: 4, 4, 4, 4, 6, 4 --- class: secondary ### Something is strange with the 2 node count -- Need to look at it  --- class: secondary ### Something is strange with the 2 node count -- Need to look at  --- class: inverse # Nate --- class: primary # Fall Semester Goals - Verify theoretical basis for current SLR goals and begin experiments on simulated data - Finish groove changepoint detection algorithm version 2 and test on all available data - Write paper with Kiegan on groove results --- class: inverse # Ganesh --- class: secondary - Got Comments back from the Reviewers for the first revision, some more minor changes in formatting and language. - Fixed most issues and almost ready to send back the responses, pondering on some weird points. - Implementing the diagnostics UI (shiny app) on a sample bullet database - almost there! --- class: inverse # Sam --- class: primary # Book progress - Confirmed book contributors: - Amanda Luby (human factors) - Chris Galbraith (digital) - Karen Pan (fingerprint, glass (?)) - Eric Hare (bullets) - Xiao Hui (casings, digital) - Nick Berry (handwriting) - Soyoung Park (glass, shoes) - Peter Vallone, Sarah Riman (DNA) - Chapter Outline: - Intro. to problem - Data collection and cleaning - R Packages available & key functions - Drawing conclusions (LR?, SLR?, etc.) - Case Study Other: glossary, hierarchy of propositions, LR chapter? --- class: primary # Practitioner survey  --- class: primary # Practitioner survey  --- class: primary # EDA glass paper  --- class: inverse # Guillermo --- class: primary # Plans for this Fall - Complete adaptation of registration of 3D scans in R and perform analysis - Complete shiny app for 2D outsole scans registration - Write paper of the database which must include some data analysis - Write paper on speaker recognition (w/Vianey Leos) --- class: inverse # James --- class: primary # This Fall * Steady Progress + 27 Pairs left to 3D Scan * Will be Using the Gilman Lab for all "four" Prints * Mud prints may be taken in Durham due to the 3D Scanner --- class: inverse # Kiegan --- class: primary # Fall Semester Goals - **Submit to AFTE journal** - Finishing up edits this week. - **Complete writing of Chapman & Hall book** - Finishing Chapter 3 draft *this week* - "Bake-off" of get_grooves methods coming soon. - Ran into an issue with robust LOESS on Houston set - Reworking functions to iterate how we want. --- class: primary # Locfit.robust updates  --- class: primary # Locfit.robust updates  --- class: primary # Locfit.robust updates  --- class: inverse # Danica --- class: primary # Update - Submit draft BF vs. LR paper to AOS -> should be done this week! - Write a follow-up paper to LPR -> have a nice draft - Continue NIJ grant to validate FDE conclusions - Started work on the second phase - Trying to determine a good way to combine kinematic measures - Working on my Top 10 lists - Things statisticians should know before working in forensics -> have a nice draft - Things forensic practioners should know before working with statistics -> have a rough draft --- class: inverse # Miranda --- class: primary # Neural Networks - Continue quality control on labeled images - Go through annotations to verify *all* shapes in a given polygon are labeled - Implement image augmentation (in particular, stretching) to prevent over-fitting of specific shapes - Regular polygons are identified with high accuracy, but elongated polygons are not - Very close to being implemented! --- class: inverse # Heike --- class: primary # Scans - Hamby set 36 scans done, <span style="color:orange">analysis done, results are a bit strange</span> - Hamby clone set 224 <span style="color:orange">scans done </span> - Hamby set 224 <span style="color:orange">scans done </span> - <span style="color:orange">Hamby set 10 arrived from St Louis, in scanning process. </span> - several other sets of bullets and cartridge cases --- class: primary # Hamby 224 - Clone - Hamby 224 Clone - organized as test set: two known bullets from the same barrel are paired with an unknown - total of fifteen test sets (one for each unknown): some clones have replicates   --- class: primary # Hamby 224 - Clone and Original Which barrel in the original is Test Set barrel X? Test set 1 (Clone 224) versus Barrel 1 (Set 224)  Still todo: Matching individual clones and their respective bullets --- class: primary # Scans Original  Clone  --- class: primary # Hamby 224 - Clone and Original Test set 11 (Clone 224) versus Barrel 1 (Set 224)  Set 224: Bullet 1 - Bullet 2: 0.98 Set/Clone 224: Bullet 1: 0.97 --- class: primary # Strange scan Scan of original and corresponding clone - middle part of clone does not show any striae   --- class: primary # Programming - bulletxtrctr re-factoring ~~close to~~ done - starting on comparisons of before/after feature values - [x3ptools](https://heike.github.io/x3ptools/) and [bulletxtrctr](https://heike.github.io/bulletxtrctr/) --- class: secondary [Issues!!](https://github.com/CSAFE-ISU/slides/issues) --- class: inverse # Soyoung --- class: primary # Fall semester plans: - Shoes + Wrap up shoe analysis using edges and SURF + Compare the performance with other methods such as phase only correlations, Fourier-Mellin transformation correlation + Do analysis with CSAFE shoe data + Submit papers regarding shoe analysis soon! - Research on "uncertainty pyramid" with Steve and Hari at NIST - Glass analysis with Sam + Exploratory analysis + Various covariance estimations + Submit papers! - Working on book chapters of glass and shoes funded by ROpenSci Fellowship --- class: primary # Analysis result with CSAFE shoe data - All nike shoes with size of 8.5 and 10.5 at time 4 (6 months) - KM (716) : Between replicates - KNM (599) : Between shoe IDs - Edge matching with three circles <img src="images/soyoung/nike_6plots.png" width="80%"> --- class: inverse # Susan --- class: primary # Bullets - Hamby 44 analysis - Goal is to compare Hamby 36 to Hamby 44 - Hopefully will be finished later today --- class: primary # Image Alignment - `ShoeAlignR` package renamed to `ImageAlignR` - useful for fingerprints as well - algorithms aren't working with color images yet... not sure why - Implemented an alignment algorithm from a [Kaggle post](https://www.kaggle.com/vicensgaitan/image-registration-the-r-way/notebook) - Harris Corner detection + KNN features + RANSAC to find a matching transformation for alignment --- class: primary # Image Alignment <img src = "images/susan/ShoeAlignNikeByCheckin.png" width = "100%"/> --- class: primary # Image Alignment <img src = "images/susan/ShoeRANSACNikeByCheckin.png" width = "100%"/> - Orange points - interesting feature for the first image - Purple points - similar feature in 2nd image --- class: primary # Image Alignment <img src = "images/susan/ShoeOverlayNikeByCheckin.png" width = "100%"/> - Red = appears in warped image only - Blue = appears in unwarped image only - Black = appears in both images (overlapped points) --- class: primary # Image Alignment - Still to do: - Need to add a better feature detector - corner detection misses some shoe features - Figure out whether the algorithm isn't working on certain shoes: - Image cleaning? - Poor feature detection? - Different people wearing the shoes?