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Cynthia Atherton.  Methodology  Code  Results  Problems  Plans.

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Presentation on theme: "Cynthia Atherton.  Methodology  Code  Results  Problems  Plans."— Presentation transcript:

1 Cynthia Atherton

2  Methodology  Code  Results  Problems  Plans

3 “Hand Detection and Segmentation in Cluttered Environments” by Christopher Schwarz and Niels da Vitoria Lobo School of Computer Science, University of Central Florida, Orlando, FL

4  Hand tracking system  Detect and segment straight fingered hands ▪ Any complex background ▪ All reasonable lighting conditions ▪ Single frame analysis  Existing systems ▪ Work with silhouettes ▪ Work under normal lighting conditions

5  Preprocessing  Detecting candidate fingers  Grouping finger detections into candidate hand detections  Post-processing candidate hand groups  Generating output

6  Break down image into lines and curves  Search for a pair of nearly parallel lines and a fingertip curve to constitute a candidate finger  Run Kanade-Lucas-Tomasi (KLT) Feature Tracker to find locations of textured regions

7 Combination of Burns Line Finder and Canny Edge Detection Curvefinder software is separate program written by Jan Prokaj


9 Used in phase 4 to strengthen the detection confidence in finger and hand detections in regions of low texture and weaken those in regions of more complex texture.

10  Build the initial candidate list  Remove duplicates  Correct bases  Reapply Area Tests

11  Tip is found correctly, but base does not extend to location where finger meets palm  Method of base correction on each finger:  Sample gradient magnitude in region around original base point.  Move sampling bounding box along median line past the original base in increments of several pixels.  Compare strength of data in region to previous.  Assume new base when magnitude drastically change.

12  When a finger is extended through base correction, should the original finger be discarded?  Apply length-to-width ratio to original  If ratio is in range, keep original and add duplicate detection with “corrected” base.

13  Form initial groups  Score groups for correctness  Reinstate candidates  Remove false positive hand groups  Merge similar hand groups  Remove fingers from hand groups Repeat once before continuing to phase 4

14  To get an estimated palm center: 1. Draw a ray along the medial axis of the finger through the center of its base and beyond 2. Take a point in the ray estimated as the palm center by following it for a distance past the base that is a percentage of the length of the finger (0.6x)

15  To group candidate fingers into candidate hands:  2 candidate fingers are connected if they meet several criteria, for example: ▪ Similar median brightness values ▪ Estimated palm centers are close (Euclidean distance)  Results in cliques of fingers

16  For a detected hand group  Average all of the estimated palm centers for the fingers to get the palm center  Estimate palm region as a circle around the center, with a radius proportion to the average length of the detected fingers

17  2 levels of promotion  Promotion 1 ▪ Stronger method of promotion ▪ Candidate must match every finger in the group  Promotion 2 ▪ Candidate’s tip-base median ray must come within some distance of group’s palm radius ▪ Candidate must point in same direction as other fingers in group

18  Correct palm center locations  Reapply Remove fingers from hand groups  Calculate openness rating of each group  Reapply Remove false positive hand groups  Remove false positive groups according to the positions of others  Reapply Correct palm center locations

19  Improve location of palm center by looking for internal wedges  Curves found in negative space of hand and open fingers  Points along perimeter of the palm  Wedges used to nudge original palm center in the right direction

20  Detected hands  Pink – palm center  Blue – detected fingers  Green – Maybe class

21  Curvefinder written in C by Jan Prokaj  KLT written in C by Stan Birchfield of Clemson University  Line sketch/Finger finder written in Java by Chris Schwarz  Curve sketches generated prior to running Fingerfinder

22  System is good at detecting palm locations, but not straight fingers.  Curvefinder not designed to run on Windows  Learning how to run Linux image in VMWare Player

23  Curved fingers  Finding fingers using silhouettes and AdaBoosting  Get with Dr. Lobo for info on head detection  Learn about AdaBoosting  Apply new method for finger detection

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