Prerequisite – Pattern Recognition | Introduction
Phases in Pattern Recognition System
Approaches for Pattern Recognition Systems can be represented by different phases as Pattern Recognition Systems can be divided into components.
- Phase 1: Converts images or sounds or other inputs into signal data.
- Phase 2: Isolates the sensed objects from the background.
- Phase 3: Measures objects properties that are useful for classification.
- Phase 4: Assign the sensed object to category.
- Phase 5: Take other consideration to decide for appropriate action.
Problems solved by these Phases are as follows:
- Sensing: It deals with problem arises in the input such as itsbandwidth, resolution, sensitivity, distortion, signal-to-noise ratio, latency,etc.
- Segmentation and Grouping: Deepest problems in pattern recognition that deals with the problem of recognizing or grouping together the various parts of an object.
- Feature Extraction: It deals with the characterization of an object so that it can be recognized easily by measurements.Those objects whose values are very similar for the objects are consider to be in the same category, while whose values are very different for the objects are placed in different categories.
- Classification: It deals with assigning the object to their particular categories by using the feature vector provided by the feature extractor and determining the values of all of the features for a particular input.
- Post Processing: It deals with action decision making by using the output of the classifier.Action such as to minimum-error-rate classification that will minimize the total expected cost.
Activities for designing the Pattern Recognition Systems
There are various sequences of activities that are used for designing the Pattern Recognition Systems. These activities are as follows:
- Data Collection
- Feature Choice
- Model Choice
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