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Semantic feature analysis in raster maps. Trevor Linton, University of Utah. Acknowledgements. Thomas Henderson Ross Whitaker Tolga Tasdizen The support of IAVO Research, Inc. through contract FA9550-08-C-005. Field of Study. Geographical Information Systems

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Semantic feature analysis in raster maps

Semantic feature analysis in raster maps

Trevor Linton, University of Utah


  • Thomas Henderson

  • Ross Whitaker

  • Tolga Tasdizen

  • The support of IAVO Research, Inc. through contract FA9550-08-C-005.

Field of study
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.


  • Degradation and scanning produces noise.

  • Overlapping features cause gaps.

  • Metadata has the same texture as features.

  • Closely grouped features makes discerning between features difficult.

Problems noisy data
Problems – Noisy Data

Scanning artifact which introduces noise

Problems overlapping features
Problems – Overlapping Features

Metadata and Features overlap with similar textures. Gaps in data.

Problems closely grouped features
Problems – Closely Grouped Features

Closely grouped features make discerning features difficult.

Thesis goals
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.


  • Gestalt Principles

    • Organizes perception, useful for extracting features.

    • Law of Similarity

    • Law of Proximity

    • Law of Continuity

Approach gestalt principles
Approach – Gestalt Principles

  • Law of Similarity

    • Grouping of similar elements into whole features.

    • Reinforced withhistogram models.

Approach gestalt principles1
Approach – Gestalt Principles

  • Law of Proximity

    • Spatial proximity of elementsgroups them together.

    • Reinforced through TensorVoting System

Approach gestalt principles2
Approach – Gestalt Principles

  • Law of Continuity

    • Features with small gaps should be viewed as continuous.

    • Idea of multiple layers offeatures that overlap.

    • Reinforced by Tensor VotingSystem.

Pre processing

  • Class Conditional Density Classifier

    • Uses statistical meansand histogrammodels.

    • μ = Histogram modelvector.

    • Find class k with thesmallest δ is the classof x.

Pre processing1

  • 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.

Pre processing2

  • 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.

Pre processing3

  • Original Image with Features Estimated

Pre processing4

  • Original Image with Roads Extracted

Class condition classifier k-Nearest Neighbors Knowledge Based

Tensor voting system1
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.

Tensor voting system2
Tensor Voting System

  • What is a tensor?

    • Two vectors that are orthogonal to one another packed into a 2x2 matrix.

Tensor voting system3
Tensor Voting System

  • Creating estimates of tensors from input tokens.

    • Principal Component Analysis

    • Canny edge detection

    • Ball Voting

Tensor voting system4
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.

Tensor voting system5
Tensor Voting System

  • Voting Fields

    • A window size is calculated from

    • Direction of each tensor in the field is calculated from

    • Attenuation derived from

Tensor voting system6
Tensor Voting System

  • Voting Fields (Attenuation)

    • Red and yellow are higher votes, blue and turquoise lower.

    • Shape related to continuation vs. proximity.

Tensor voting system7
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 λ1 and λ2 are small then the curve or junction has a small amount of confidence in existing or being relevant.

Tensor voting system8
Tensor Voting System

  • Extracting features from dense voting field.

Original Image Curve Map Junction Map

Post processing

  • Extracting features from curve map and junction map.

    • Global Threshold and Thinning

    • Local Threshold and Thinning

    • Local Normal Maximum

    • Knowledge Based Approach

Post processing1

  • Global threshold on curve map.

Applied Threshold Thinned Image

Post processing2

  • Local threshold on curve map.

Applied Threshold Thinned Image

Post processing3

  • Local Normal Maximum

    • Looks for maximum over the normal of the tensor at each point.

Applied Threshold Thinned Image

Post processing4

  • Knowledge Based Approach

    • Uses knowledge of types of artifacts of the local threshold to clean and prep the image.

Original Image Knowledge Based Approach


  • 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.


  • 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.


  • 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.


  • 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


  • 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


  • Tensor Voting System

    • Results from test show the best value for σis between 10 and 16 with little difference in performance.


  • 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%.


  • Best Post-Processing

    • Finding the best window size for local thresholding.

    • Best parameter was found between 10 and 14.


  • Best Post-Processing

    • The best post-processing method was found by using a naïve pre-processing technique and tensor voting.

    • Knowledge Based Approach performed the best.


  • 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.


  • 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


  • Confidence Intervals (95% CI, 100 samples)

    • Road Recall:

      • Mean: 93.61% CI [ 92.47% , 94.75% ] ± 0.14%

    • Road Precision:

      • Mean: 95.23% CI [ 94.13% , 96.33% ] ± 0.10%

    • Intersection Recall:

      • Mean: 82.22% CI [ 78.91% , 85.51% ] ± 3.29%

    • Intersection Precision:

      • Mean: 80.1% CI [ 76.31% , 82.99% ] ± 2.89%


  • Adjusting parameters dynamically

    • Dynamically adjusting the σ 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.

Future work issues
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.

Future work issues1
Future Work & Issues

  • Scanning noise will need to be removed in order to produce high intersection recall and precision results.

Future work issues2
Future Work & Issues

  • Closely grouped and overlapping features.

Future work issues3
Future Work & Issues

  • Developing other pre-processing and post-processing techniques.

    • Learning algorithms

    • Various local threshold algorithms

    • Road following algorithms