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While at SRI International, Duda and Peter E. Hart were the authors of "Pattern Classification and Scene Analysis", originally published in 1973. This classic text is a widely cited reference, and the first edition was in print for over 25 years until being superseded by the second edition in 2000. [2] Duda is an IEEE Fellow and a AAAI Fellow. [3]
The Hough transform is a feature extraction technique used in image analysis, computer vision, pattern recognition, and digital image processing. [1] [2] The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent patterns.
The Ramer–Douglas–Peucker algorithm, also known as the Douglas–Peucker algorithm and iterative end-point fit algorithm, is an algorithm that decimates a curve composed of line segments to a similar curve with fewer points.
Suppose a pair (,) takes values in {,, …,}, where is the class label of an element whose features are given by .Assume that the conditional distribution of X, given that the label Y takes the value r is given by (=) =,, …, where "" means "is distributed as", and where denotes a probability distribution.
In a statistical-classification problem with two classes, a decision boundary or decision surface is a hypersurface that partitions the underlying vector space into two sets, one for each class. The classifier will classify all the points on one side of the decision boundary as belonging to one class and all those on the other side as belonging ...
Bloodstain pattern analysis (BPA) is a forensic discipline focused on analyzing bloodstains left at known, or suspected crime scenes through visual pattern recognition and physics-based assessments. This is done with the purpose of drawing inferences about the nature, timing and other details of the crime. [ 1 ]
The extension to cases where there are more than two classes is relatively straightforward. [2] [6] [7] Let be the number of classes.Then multi-class KFD involves projecting the data into a ()-dimensional space using () discriminant functions