Transcript Recognizing haul trucks

Matching MSERs Using Edge Information and
the Chamfer Distance Function
Pantelis Elinas
Australian Centre for Field Robotics
The University of Sydney
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Image Matching Basics
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Extract local features
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Select and normalize support region
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Harris corners, Salient Regions, MSERs, Edge-based Regions, etc.
Scale-space analysis, shape adaptation, affine normalization, etc.
Compute descriptors
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Moment invariants, histogram of gradient orientations, filter responses, etc.
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Compare descriptors
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Our focus
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Maximally Stable Extremal Regions
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Chamfer matching
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Maximally Stable Extremal Regions
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Proposed by Matas et. al. 2002
for matching images in wide
baseline stereo
Gray-scale connected
component regions stable over a
range of threshold values
Affine invariant and robust to
photometric transformations
Empirically shown to outperform
other local features in terms of
repeatability
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Orientation Normalization
•
Compute histogram of gradient
orientations over image patch
•
Select
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Dominant orientation
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Local maxima within 80% of the
global maximum
original
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Θ=4.15
Θ=1.4
Pantelis Elinas
Chamfer Matching
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Utilised mostly in shape-based object class recognition,
e.g., pedestrian detection, recognition by parts
Given two edge maps Eq and Em, we can define their
Chamfer distance
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Note: It is not symmetric and x = 0 in our case
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Also define the symmetric chamfer distance
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Chamfer Matching (cont.)
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Can be computed in 2 steps
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Step 1: Compute Distance Transform of query image
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Step 2: Compute Chamfer distance by a simple averaging operation
MSER
Canny
DT
QUERY
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Canny
MSER
MODEL
Pantelis Elinas
Evaluation Data
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Data from Mikolajczyk et. al. 2005 with known ground
truth homography
Viewpoint
(graf)
Rotation + scale
(boat)
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JPEG
Compression
(ubc)
Blurring
(bikes)
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Performance and Image Patch Resolution
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Deciding the resolution for the normalized MSER image
patch
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Pantelis Elinas
Chamfer Threshold
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What is a correct match?
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Performance
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Chamfer matching vs SIFT, GLOH and Moments
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Pantelis Elinas
Image Matching Examples
Correct matches after geometric constraint
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Homography
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Planar Object Recognition Example
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Correctly matched 52 out of 87 MSERs in the model
image
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Conclusions
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Presented a method for image recognition
using MSERs and chamfer matching
Advantages
–
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–
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Few parameters that are easy to determine
Easy to implement
Works well (for some scenes)
Disadvantages
–
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Performs worse than histogram-based
descriptors (for some scenes)
Finding correspondences is an O(N2)
operation
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Future Work
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Improve matching performance
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Extensions
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Multi-resolution MSERs (Forssen et. al. 2007)
Colour images (Forssen 2007)
Can Chamfer matching be used with other types of
local features, e.g., edge-based regions?
Improve computational performance
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GPU implementation
Rio Tinto Centre for Mine Automation
Pantelis Elinas
Thank you!
Rio Tinto Centre for Mine Automation
Pantelis Elinas