Wire Detection Version 2 Joshua Candamo Friday, February 29, 2008

Version 1 (TAES Algorithm) Motion Estimation Parameter Selection Feature Map Video Thin Lines Line Motion Model Next Frame Feature Map Sub-Windows Clutter Measure Pre-Processing Original Image Sub- Windows Canny Edge Detector Hough Transform Weak Feature Map Perhaps a mistake trying to maximize # pixels? Broad Domain Unlikely to generate robust training set Highly Sensitive to Noise

TAES Reviewers Critique Comment Addressed in Version 2 Low detection rate Paper was not concise 1.Too complex 2.Too many parameters –(3) Canny –(3) Pre-processing –(2) HT –(3) Tracking Model ________ Total: 11 parameters Trained on 7 1.Increase detection rate 2.No tracking 3.Reduced complexity, training & # parameters –(3) Canny –(2) Pre-processing –(4) Line Detection (h,T w,T f, T p ) ________ Total: 9 parameters Only training on Line Detection (4)

Biggest Problems Definition not concise 2 lines are equal if: Weak feature map led to high FA rate HT is a weak pattern recognition method in reality: trigonometric operations are slow, and only robust to noise if you have low clutter Line i=(ρ i, θ i ) Line j=(ρ j, θ j )

Defining a Wire Within the application domain: 2 image wires i & j are equivalent if they can be used interchangeably to describe the same true wire i & j are similar if they are likely to be perceived as equal by a human operator –We define equivalency of wires as –And line similarity as PAR_SIM_m = 0.2 (about 10 o ) PAR_SIM_c = 20px

New Premises “Most wire pixels are edge pixels” “Most of edge pixels are not wire pixels” Domain Definitions/Assumptions: – –A wire i is described by the 3-tuple weight, slope, and y-intercept –The weight is the # of pixels conforming the wire –PAR_SIM_m = 0.2 (about 10 o ) –PAR_SIM_c = 20px

Morphological Filtering Lines Wires Canny Connected Component Labeling Combine Similar Lines Threshold Image Line Fitting Support Points Global Line Direction Similarity Steger’s Line Profile Flowchart (Version 2) Low Level Image Understanding Strong Feature Map (No Training) High Level Image Understanding (Training) Kasturi’s Threshold: Base= control variable for ROC

Results only Dataset with Wires 95% Confidence Interval +/- 0.1 detection +/ FA

Low Parameter Sensitivity

No Wires Dataset Results 1.25 FA per image in the dataset of images without wires with a 95% confidence interval [1.23,1.27], i.e. error is 16.66% for a 16 images training set 1 st complete test run output: 84.28% detection 9.96% FA 95% Confidence Error 0.05% detection, 0.03% FA

1 second scene: Scene taken every 10 frames

What’s Next? Write paper? Which journal? This technique was borrowed from the work I was doing for human event detection I will use this result to finish my dissertation proposal, aiming for Spring Break