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PANAKOS ANDREAS

An Interactive An Interactive Tool for Color Tool for Color Segmentation.Segmentation.

What is color What is color segmentation?segmentation?

Why is used in Why is used in Kouretes Vision Kouretes Vision Module?Module?

– Color Segmentation methodsColor Segmentation methods• Unsupervised learningUnsupervised learning• Supervised learningSupervised learning

– Unsupervised learningUnsupervised learning• No given informationNo given information• Acquisition of temporal informationAcquisition of temporal information• ClusteringClustering• k-meansk-means

– Supervised learningSupervised learning• Training using formal given informationTraining using formal given information• classificationclassification• SVMs, NN , DT , BayesSVMs, NN , DT , Bayes

Threshold techniques :pixels within a certain range belong Threshold techniques :pixels within a certain range belong to the same class or object . Careful tuning of thresholds and to the same class or object . Careful tuning of thresholds and are extremely sensitive to light conditions.are extremely sensitive to light conditions.

Edge-based methods :pixel values change rapidly at the Edge-based methods :pixel values change rapidly at the edge between two regions. Edge detectors such as Sobel, edge between two regions. Edge detectors such as Sobel, Canny and Susan are typically used .This tends to make Canny and Susan are typically used .This tends to make these methods very computationally expensive.these methods very computationally expensive.

Region-based methods :pixels in the same region have Region-based methods :pixels in the same region have similar color value. the similarity depends on the selected similar color value. the similarity depends on the selected homogeneity criterion, usually based on some threshold homogeneity criterion, usually based on some threshold value. value.

Color recognition uses color to separate objects.Color recognition uses color to separate objects.

Apply a segmentation method based on color components .Apply a segmentation method based on color components .

The color recognition method is an efficient way to describe The color recognition method is an efficient way to describe color images. color images.

The colors contained in the image are reduced from the The colors contained in the image are reduced from the possible ones to eight: red, blue, green, cyan, orange , possible ones to eight: red, blue, green, cyan, orange , yellow, white, and black.yellow, white, and black.

The colors contained in the image are reduced from the The colors contained in the image are reduced from the possible ones to eight: red, blue, green, cyan, orange, possible ones to eight: red, blue, green, cyan, orange, yellow, white, and black.yellow, white, and black.

1: Orange: This is the color of the ball. 1: Orange: This is the color of the ball. 2: Yellow: This is the color of the goal defended by the red 2: Yellow: This is the color of the goal defended by the red

team. team. 3: SkyBlue: This is the color of the goal defended by the blue 3: SkyBlue: This is the color of the goal defended by the blue

team. 4: White: This is the color of field lines, robot bodies, team. 4: White: This is the color of field lines, robot bodies, and barrier walls.and barrier walls.

5: Green: This is the color of the field. 5: Green: This is the color of the field. 6: Red: This is the color of the red teams uniform. 6: Red: This is the color of the red teams uniform. 7: Blue: This is the color of the blue teams uniform. shades 7: Blue: This is the color of the blue teams uniform. shades

of blue and green.of blue and green. 8: NoColor: This is an artificial color class for characterizing 8: NoColor: This is an artificial color class for characterizing

any color not belonging to any of the above classes.any color not belonging to any of the above classes.

Classification ProcessClassification Process• Training stage(off-line)Training stage(off-line)• Classification stageClassification stage

Manually labeled data taken from several camera images.Manually labeled data taken from several camera images.

Large areas of the raw image taken can be selected using Large areas of the raw image taken can be selected using graphical interface and all pixels within each area are graphical interface and all pixels within each area are associated with the desired color class labelassociated with the desired color class label

Once a sufficient number of data collected start training, Once a sufficient number of data collected start training, learn a good set of parameters and classify each pixel of learn a good set of parameters and classify each pixel of the image using the learned classier. the image using the learned classier.

Data Selection Process Data Selection Process • Line Selection Line Selection • Rectangle Selection Rectangle Selection • Poly Selection Poly Selection • K-means Selection K-means Selection

Extraction and Training Processes Extraction and Training Processes • Features Locality Features Locality • Color Spaces Color Spaces • Classification Methods Classification Methods

Validation Process Validation Process • K-Fold Cross Validation K-Fold Cross Validation • Visualized ValidationVisualized Validation

Line Selection Line Selection • acquire data belonging in lines. General use for accessing in acquire data belonging in lines. General use for accessing in

difficult areas.difficult areas.

Rectangle Selection Rectangle Selection • General selection method in which, large scaled areas can be General selection method in which, large scaled areas can be

picked.picked.

Poly Selection Poly Selection • For complex objects Poly Selection can be asserted to extract For complex objects Poly Selection can be asserted to extract

the desired region following accurately the contour.the desired region following accurately the contour.

K-means Selection K-means Selection • Grouping using K-means Clustering Grouping using K-means Clustering • Easy selection of regions of interestEasy selection of regions of interest

Features Locality Features Locality

• Difficult lighting situations, create difficult classification Difficult lighting situations, create difficult classification circumstances.circumstances.

• Color locality can be used to reinforce the method.Color locality can be used to reinforce the method.

• Use also as input attributes the values of pixels in its Use also as input attributes the values of pixels in its immediate neighborhood.immediate neighborhood.

Color Spaces Color Spaces • RGBRGB• YUVYUV• HSIHSI

Classification Methods Classification Methods

• Naive Bayes ClassifierNaive Bayes Classifier

• Support Vector MachinesSupport Vector Machines

• Decision TreesDecision Trees

Binary Classification – Multiclass ClassifcationBinary Classification – Multiclass Classifcation

• Soft Color ClassesSoft Color Classes

Additional ‘soft’ color classes are used to allow additional Additional ‘soft’ color classes are used to allow additional information to be classified and to reduce the risk of false information to be classified and to reduce the risk of false positive classification.positive classification.

Soft Soft color color classification classification used to classify regions that belongs to used to classify regions that belongs to an object but is less saturated.an object but is less saturated.

This allows the decision about what object the soft color belongs This allows the decision about what object the soft color belongs to, to be delayed until the entire image is processed.to, to be delayed until the entire image is processed.

Using soft color allows for the classification of ‘noisy’ shades of Using soft color allows for the classification of ‘noisy’ shades of and allows valuable object size information to be kept. and allows valuable object size information to be kept.

Risk of false positives is reduced by allowing true color to be Risk of false positives is reduced by allowing true color to be classed as the shades of color that are highly saturated.classed as the shades of color that are highly saturated.

K-Fold Cross ValidationK-Fold Cross Validation

• Validate the resulting classifier estimating the efficiency.Validate the resulting classifier estimating the efficiency.

• Randomly break the dataset into k partitions. For each of k Randomly break the dataset into k partitions. For each of k partitions, use k-1 folds for training and the remaining one partitions, use k-1 folds for training and the remaining one for testing.for testing.

Visualized Validation Visualized Validation

• Use of learned a classifier to classify image offering a visual Use of learned a classifier to classify image offering a visual validation.validation.