KBV Recognition Toolkit Tasks
TASK |
TYPE |
DESCRIPTION |
AutoCode |
Utility |
Automatically codes features to discrete form. The user can select the maximum number of discrete states permitted. |
Bayes_Cls |
Classifier |
Multi-variate normal Bayesian classifier for normally distributed measurement data which can produce optimal results when its assumptions are met. Prior class weights can be accepted from the reference file, can be set equal, or can be adjusted by the user to favor specific classes. |
Bayes_Trn |
Training |
This trainer sets up all of the reference data required by Bayes_Cls. This includes generation of class centroids, covariance, and base weights. |
Boxes |
Extract |
Finds related opposite and adjacent corners for the extraction of rectangular objects. |
Centroid |
Utility |
Given data and labels, this routine computes class centroids in the feature space. |
Corners |
Extract |
Finds corners under user-specified tolerances. It uses basic line Token input. |
CorrEval |
Evaluation |
Correlation-based feature evaluation. This Task can assist in evaluation of the utility of measurement data and reduce redundancy in a set of features. Rank orders features. |
DIT_Cls |
Classifier |
The Discriminant Information Tree (DIT) classifier provides a capability to classify data which is discrete, coded, or symbolic. It can function without any class centroid separability under complex data distributions. Continuous measurement data can be coded for use with this classifier using the AutoCode or Encode Tasks. |
DIT_Trn |
Training |
This trainer uncovers the optimal discriminant information tree and probability structures for DIT_Cls. |
Encode |
Utility |
Encodes measurement data using specified partitions. The partitions can be from AutoCode or user-generated. |
FindROI |
Extract |
This routine detects regions of interest (ROI) in an image using a K-nearest-neighbor algorithm. It can be used for cueing or as the first stage in a multi-stage classifier. |
InfoEval |
Evaluation |
This tool determines, on a feature-by-feature basis, the best features for class separation. It can be used to evaluate symbolic features, discrete features, and coded measurement data. It provides a measure of the discriminant information in each feature, and automatically rank-orders a provided feature set from most powerful to least powerful. |
KNN_Cls |
Classifier |
K-Nearest-Neighbor classifier for utilization on measurement data with complex distributions. This classifier can operate when classes are less easily separable. |
KNN_Trn |
Training |
Generates a reference prototype file for the K-Nearest-Neighbor Classifiers of KNN_Cls and FindROI. |
LinkEval |
Evaluation |
Finds tree linkage structure which maximizes second-order discriminant information. Can assist in feature discrimination or redundancy evaluation. |
MeanAsgn |
Utility |
This routine assigns data to closest centroid. Permits rapid evaluation of class separability of user-selected or modified centroid values. Can be used with Centroid Task or PFSClust Task to develop alternative classifiers. |
MinD_Cls |
Classifier |
This Minimum Distance Classifier provides an easy-to-use classification capability, with minimal training data requirements and computational complexity. It assigns samples based on Euclidean distance from class centroids in the feature space. |
MinD_Trn |
Training |
Sets up class centroids and weights for the minimum distance classifier. Easy use and result interpretation for measurement data from well separated classes. |
MRegres |
Evaluation |
This Multiple Regression analysis technique provides the optimal linear combination of features to estimate an outcome. It can be used in feature evaluation and redundancy analysis. |
Normal |
Utility |
Generates normalized features by extracting the mean and standardizing variances to 1. Eliminates measurement unit scaling effects in data. |
PairEval |
Evaluation |
Computes pairwise discriminant information. It provides a measure of how well each pair of features can be used to predict the correct class label. It can help identify redundant features and in selection of discriminative feature sets, or used with LinkEval. |
Parallels |
Extract |
Finds parallel line pairs under user-specified control of separation distance ranges and degree of overlap. Uses line Token input. |
PFSClust |
Clustering |
Pseudo F Statistic (PFS) Clustering finds the natural classes in a set of data. This unsupervised classification algorithm can be used totally hands-off to find natural separations in the feature space. It can guide in feature selection and class definition for a later classifier implementation. |
WinFeats |
Extract |
Produces window-based features useful for detection and classification. It supports FindROI in detection of regions of interest. Window size and overlap are user-selected. |
XternTks |
Utility |
This utility permits external text file data in observation matrix form (possibly from a spreadsheet or other source) to be converted to KBV Tokens. Each row of the input matrix holds the all feature values for one sample, and is converted to a Token. |