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Semantic feature analysis in raster maps

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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.

- 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.

Scanning artifact which introduces noise

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

Closely grouped features make discerning features difficult.

- 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

- Law of Similarity
- Grouping of similar elements into whole features.
- Reinforced withhistogram models.

- Law of Proximity
- Spatial proximity of elementsgroups them together.
- Reinforced through TensorVoting System

- Law of Continuity
- Features with small gaps should be viewed as continuous.
- Idea of multiple layers offeatures that overlap.
- Reinforced by Tensor VotingSystem.

- Class Conditional Density Classifier
- Uses statistical meansand histogrammodels.
- μ = Histogram modelvector.
- Find class k with thesmallest δ is the classof x.

- 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.

- 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.

- Original Image with Features Estimated

- Original Image with Roads Extracted

Class condition classifier k-Nearest Neighbors Knowledge Based

- Overview

- 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.

- What is a tensor?
- Two vectors that are orthogonal to one another packed into a 2x2 matrix.

- Creating estimates of tensors from input tokens.
- Principal Component Analysis
- Canny edge detection
- Ball Voting

- 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.

- For each tensor in the sparse field

- Voting Fields
- A window size is calculated from
- Direction of each tensor in the field is calculated from
- Attenuation derived from

- Voting Fields (Attenuation)
- Red and yellow are higher votes, blue and turquoise lower.
- Shape related to continuation vs. proximity.

- 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.

- Extracting features from dense voting field.

Original Image Curve Map Junction Map

- Extracting features from curve map and junction map.
- Global Threshold and Thinning
- Local Threshold and Thinning
- Local Normal Maximum
- Knowledge Based Approach

- Global threshold on curve map.

Applied Threshold Thinned Image

- Local threshold on curve map.

Applied Threshold Thinned Image

- Local Normal Maximum
- Looks for maximum over the normal of the tensor at each point.

Applied Threshold Thinned Image

- 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%.

- Contributions from tensor voting were mixed.

- 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%

- Road Recall:

- 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.

- 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.

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

- Closely grouped and overlapping features.

- Developing other pre-processing and post-processing techniques.
- Learning algorithms
- Various local threshold algorithms
- Road following algorithms