SEMANTIC FEATURE ANALYSIS IN RASTER MAPS Trevor Linton,
University of Utah
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Acknowledgements Thomas Henderson Ross Whitaker Tolga Tasdizen
The support of IAVO Research, Inc. through contract
FA9550-08-C-005.
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Field of Study Geographical Information Systems Part of
Document Recognition and Registration. What are USGS Maps? A set of
55,000 1:24,000 scale images of the U.S. with a wealth of data. Why
study it? To extract new information (features) from USGS maps and
register information with existing G.I.S and satellite/aerial
imagery.
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Problems Degradation and scanning produces noise. Overlapping
features cause gaps. Metadata has the same texture as features.
Closely grouped features makes discerning between features
difficult.
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Problems Noisy Data Scanning artifact which introduces
noise
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Problems Overlapping Features Metadata and Features overlap
with similar textures. Gaps in data.
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Problems Closely Grouped Features Closely grouped features make
discerning features difficult.
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Thesis & Goals Using Gestalt principles to extract features
and overcome some of the problems described. Quantitatively extract
95% recall and 95% precision for intersections. Quantitatively
extract 99% recall and 90% precision for intersections. Current
best method produces 75% recall and 84% precision for
intersections.
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Approach Gestalt Principles Organizes perception, useful for
extracting features. Law of Similarity Law of Proximity Law of
Continuity
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Approach Gestalt Principles Law of Similarity Grouping of
similar elements into whole features. Reinforced with histogram
models.
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Approach Gestalt Principles Law of Proximity Spatial proximity
of elements groups them together. Reinforced through Tensor Voting
System
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Approach Gestalt Principles Law of Continuity Features with
small gaps should be viewed as continuous. Idea of multiple layers
of features that overlap. Reinforced by Tensor Voting System.
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Approach Framework Overview
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Pre-Processing Class Conditional Density Classifier Uses
statistical means and histogram models. = Histogram model vector.
Find class k with the smallest is the class of x.
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Pre-Processing k-Nearest Neighbors Uses the class that is found
most often out of k closest neighbors in the histogram model.
Closeness is defined by Euclidian distance of the histogram
models.
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Pre-Processing Knowledge Based Classifier Uses logic that is
based on our knowledge of the problem to determine classes. Based
on information on the textures each class has.
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Pre-Processing Original Image with Features Estimated
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Pre-Processing Original Image with Roads Extracted Class
condition classifier k-Nearest Neighbors Knowledge Based
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Tensor Voting System Overview
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Tensor Voting System Uses an idea of Voting Each point in the
image is a tensor. Each point votes how other points should be
oriented. Uses tensors as mathematical representations of points.
Tensors describe the direction of the curve. Tensors represent
confidence that the point is a curve or junction. Tensors describe
a saliency of whether the feature (whether curve or junction)
actually exists.
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Tensor Voting System What is a tensor? Two vectors that are
orthogonal to one another packed into a 2x2 matrix.
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Tensor Voting System Creating estimates of tensors from input
tokens. Principal Component Analysis Canny edge detection Ball
Voting
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Tensor Voting System Voting For each tensor in the sparse field
Create a voting field based on the sigma parameter. Align the
voting field to the direction of the tensor. Add the voting field
to the sparse field. Produces a dense voting field.
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Tensor Voting System Voting Fields A window size is calculated
from Direction of each tensor in the field is calculated from
Attenuation derived from
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Tensor Voting System Voting Fields (Attenuation) Red and yellow
are higher votes, blue and turquoise lower. Shape related to
continuation vs. proximity.
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Tensor Voting System Extracting features from dense voting
field. determines the likelihood of being on a curve. determines
the likelihood of being a junction. If both 1 and 2 are small then
the curve or junction has a small amount of confidence in existing
or being relevant.
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Tensor Voting System Extracting features from dense voting
field. Original Image Curve Map Junction Map
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Post-processing Extracting features from curve map and junction
map. Global Threshold and Thinning Local Threshold and Thinning
Local Normal Maximum Knowledge Based Approach
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Post-processing Global threshold on curve map. Applied
Threshold Thinned Image
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Post-processing Local threshold on curve map. Applied Threshold
Thinned Image
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Post-processing Local Normal Maximum Looks for maximum over the
normal of the tensor at each point. Applied Threshold Thinned
Image
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Post-processing Knowledge Based Approach Uses knowledge of
types of artifacts of the local threshold to clean and prep the
image. Original Image Knowledge Based Approach
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Experiments Determine adequate parameters. Identify weaknesses
and strengths of each method. Determine best performing methods.
Quantify the contributions of tensor voting. Characterize
distortion of methods on perfect inputs. Determine the impact of
misclassification of text on roads.
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Experiments Quantitative analysis done with recall and
precision measurements. Relevant is the set of all features that
are in the ground truth. Retrieved is the set of is all features
found by the system. tp = True Positive, fn = False Negative, fp =
False Positive Recall measures the systems capability to find
features. Precision characterizes whether it was able to find only
those features. For both recall and precision, 100% is best, 0% is
worst.
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Experiments Data Selection Data set must be large enough to
adequately represent features (above or equal to 100 samples). One
sub-image of the data must not be biased by the selector. One
sub-image may not overlap another. A sub-image may not be a portion
of the map which contains borders, margins or the legend.
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Experiments Ground Truth Manually generated from samples. Roads
and intersections manually identified. Ground Truth is generated
twice, those with more than 5% of a difference are re-examined for
accuracy. Ground truth Original Image
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Experiments Best Pre-Processing Method All pre-processing
methods examined without tensor voting or post processing for
effectiveness. Best window size parameter for k-Nearest Neighbors
was qualitatively found to be 3x3. The best k parameter for
k-Nearest Neighbors was quantitatively found to be 10. The best
pre-processing method found was the Knowledge Based Classifier
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Experiments Tensor Voting System Results from test show the
best value for is between 10 and 16 with little difference in
performance.
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Experiments Tensor Voting System Contributions from tensor
voting were mixed. Thresholding methods performed worse. Knowledge
based method improved 10% road recall, road precision dropped by
2%, intersection recall increased by 22% and intersection precision
increased by 20%.
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Experiments Best Post-Processing Finding the best window size
for local thresholding. Best parameter was found between 10 and
14.
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Experiments Best Post-Processing The best post-processing
method was found by using a nave pre-processing technique and
tensor voting. Knowledge Based Approach performed the best.
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Experiments Running the system on perfect data (ground truth as
inputs) produced higher results then any other method (as
expected). Thesholding had a considerably low intersection
precision due to artifacts produced in the process.
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Experiments Best combination found was k-Nearest Neighbors with
a Knowledge Based Approach. Note the best pre-processing method
Knowledge Based Classifier was not the best pre-processing method
when used in combinations due to the type of noise it produces.
With Text: 92% Road Recall, 95% Road Precision 82% Intersection
Recall, 80% Intersection Precision Without Text: 94% Road Recall,
95% Road Precision 83% Intersection Recall, 80% Intersection
Precision
Experiments Adjusting parameters dynamically Dynamically
adjusting the between 4 and 10 by looking at the amount of features
in a window did not produce much difference in the recall and
precision (less than 1%). Dynamically adjusting the c parameter in
tensor voting actually produced worse results because of
exaggerations in the curve map due to slight variations in the
tangents for each tensor.
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Future Work & Issues Tensor Voting and thinning tend to
bring together intersections too soon when the road intersection
angle was too low or the roads were too thick. The Hough transform
may possibly overcome this issue.
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Future Work & Issues Scanning noise will need to be removed
in order to produce high intersection recall and precision
results.
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Future Work & Issues Closely grouped and overlapping
features.
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Future Work & Issues Developing other pre-processing and
post-processing techniques. Learning algorithms Various local
threshold algorithms Road following algorithms
