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ID-Me - Tool to identify species
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ID-Me - Tool to identify species


The tool is in its initial stages. It has to yet tested against a huge repository, say around 300 species. If you have any suggestions please mail to Kishen.das@gmail.com

The tool is in its initial stages. It has to yet tested against a huge repository, say around 300 species. If you have any suggestions please mail to Kishen.das@gmail.com

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  • 1. Name: Common Banded Peacock Scientific Name: Papilio crino Species ID #: 12324343553354 Family: Papilionidae Genus: Papilio Distribution: Central and Southern India, and Srilanka Status: Locally Common Wing Span: 100 – 116mm Conservation Status: Not threatenedID-Me : Tool for identifying species from an image Kishen Das Kondabagilu Rajanna UT Arlington, Texas
  • 2. Planet Earth has huge diversity
  • 3. Credits: http://www.currentresults.com/Environment-Facts/Plants-Animals/number-species.php
  • 4. • There is a considerable reduction in number of taxonomists owing to the popularityof Genetics and Microbiology• Taxonomy is highly challenging and sometimes highly irritating• Collections are prohibited in many countries• Photography has become very popular• Collection of few endangered species has pushed them on the verge of extinction•Ex: Ediths Checkerspot in southern Sierra Nevada, California and Miami Blue in BahiaHonda State Park, Florida•Global warming and environmental pollution triggering human induced massextinction ( Every 20 minutes we are losing a species)•There is an immediate need for documenting biodiversity and taking measures toconserve it
  • 5. Computer Aided TaxonomyUsing computers for identification of a species inan image with the help of image processingtechniques and taxonomic keys Asian Elephant Map butterfly Indian Cobra - Snake
  • 6. AlgorithmQuery Image [keypoints1, Training Image descriptors1] = Set 1 vl_sift();Fine tune SIFT [keypoints2, Training ImageParameters ( Peak Peak Thresh=1.0 descriptors2] = Set 2Thresh, Edge Thresh, Edge Thresh=2000 vl_sift();and Match Thresh) Match Thresh=1.5[keypointsQuery,descriptorsQuery] = . . .vl_sift();Keypoint -> Descriptor -> [keypointsN, Training Image4 X 1 matrix 128 X 1 matrix descriptorsN] = Set N(Scale, Orientation, vl_sift();Translation along x,Translation along y)
  • 7. Algorithm ( Continued …)[matches1,scores1] =vl_ubmatch(descriptors1, descriptorsQuery, match_thresh)[matches2,scores2] =vl_ubmatch(descriptors2, descriptorsQuery, match_thresh)[matchesN,scoresN] =vl_ubmatch(descriptorsN, descriptorsQuery, match_thresh)Filter Descriptors based on scores, so that each matching keypoint of query image hasunique match with that of keypoints of training imagesRun Hough Transform on filtered matches. This is a modified hough transform which isperformed in 4D space i.e., Scale, Orientation and two TranslationsSelect the Cluster that has voted for the configuration with Maximum votes i.e.,[Scaling Bin with max votes, Orientation Bin with max votes, Translation Bins with max votes ] Perform RANSAC in 4D space on cluster of matches obtained in the previous step Esimate Homography Matrix and further filter bad matches Select the best possible match from training sets based on remaining matchings and plot the final matchings
  • 8. Scale Invariant Feature Transform(SIFT)ConsiderationsVL_FEATDavid Lowe’s SIFTASIFTSIFT in a nutshell•Keypoint detection and localization ( Gaussian Filtering)•Orientation Assignment•Keypoint descriptor matching( Nearest neighbough indexing)1. Extract descriptors for dt and dq2. Extract first closest descriptor d1 Keypoint -> Descriptor ->3. Extract second closest descriptor d2 4 X 1 matrix 128 X 1 matrix4. Accept d1, if dist(dq,d2) > dist(dq,, d1) (Scale, Orientation,Peak Thresh=1.0 Translation along x,Edge Thresh=2000 Translation along y)Match Thresh=1.5
  • 9. Edge threshold eliminates peaks of the DoG scale space whose curvature is too smallPeak threshold filters peaks of the DoG scale space that are too small (in absolute value) Credits: http://www.vlfeat.org/overview/sift.html
  • 10. 81Matches199Matches51Matches
  • 11. Hough Transform in 4D spaceStep 1) Prepare the bins based on David Lowe [3]Step 2) Each matching votes for 4 bins ( In David Lowe [3],each matching votes for 16bins, to avoid the boundary effects. I am considering only 4 bins for simplicity)Step 3) Find out the configuration [ Scale Bin with maximum votes, Orientation Binwith maximum votes, X Translation Bin with maximum votes, Y Translation Bin withmaximum votes]Step 4) Choose the cluster of keypoints that has voted for the above configurationand discard rest of the clusters.( Ideally you should consider all the other clusters aswell before discarding the keypoints, again for simplicity I am considering only oneconfiguration with maximum votes) .
  • 12. 10Matches25Matches 6 Matches
  • 13. Modifed RANSAC in 4D space for Affine TransformationStep 1) Group the remaining matches into all possible combinations of groupsof 3Step 2) For each group of 3-matches, find the differences of affinetransformations between each member of this group and rest of the matches.Check whether the differences are within the margin of "OriginalBinSize/3". Ifyes, thats an inlier.Step 3) Select group of 3-matches, if there are at least 10 inliers wrt thatgroup.Step 4) From the groups of 3-matches selected in previous step, pick the finalindividual matches, such that more than 50% of the groups have been pickedin step 3 where this match belongs to.
  • 14. Modified Homography for uncalibrated camers Step 1 ) Take 4 matches , estimate Homography matrix using the equation∑ [ x training_image]X H [ x_query_image] < t, where t is close to zero. If t cannot be closer to zero, then those set of matches dont belong tothe same plane and hence discard them.Here the above equation is converted into the form Ax = O ( Zero vector) andthen Homography matrix is estimated using SVD.Step 2) Keep repeating Step 1) till there are maximum number of inliers suchthat∑ [ x training_image]X H [ x_query_image] < t, where t -> 0Step 3) Discard outliers that will not fit into Homography constraint.
  • 15. 0Matches23Matches0Matches
  • 16. Related Workhttp://www.ifpindia.org/biotik/index.phpIn this tool, one can try to identify trees of ever green forest by building a query based ondifferent tree parts (Leaf, branch, flower, etchttp://ippcweb.science.oregonstate.edu/LepID/This software comes somewhat close to ID-Me. The major drawback is that the user has to knowwhich part of the wing is important in identifying that species.http://www.elec.york.ac.uk/research/projects/Automated_Identification_of_Insects_using_Image_Processing.htmlIn this approach tool will automatically extract the wing venation and later identification will bedone using 2 different types of artificial neural networks , multi-layer perceptron and learningvector quantisationhttp://ipmnet.org/bugwing/ Its a simple tool to assist the amateur ecologists with identification. Here veins and their basicsequence of branching have been made use of in distinguishing the insects to the family orsubfamily level.
  • 17. Future Work•Using Color descriptors•Removing current limitations•Using database to store keypoints and descriptors•More images for each Training Image•Fine tuning existing algorithms•Introducing confidence levels•Collaborations with taxonomists and computer scientists
  • 18. 0Matches11Matches 0 Matches
  • 19. 0Matches 18 Matches 0 Matches
  • 20. 0Matches7Matches0Matches
  • 21. Thank you Q&A kishen.das@gmail.comAcknowledgments:Dr. Gian-Luca Mariottini andGustavo Puerto