Pattern Recognition | Phases and Activities
Prerequisite – Pattern Recognition | Introduction
Phases in Pattern Recognition System
Approaches for Pattern Recognition Systems can be represented by distinct phases, as Pattern Recognition Systems can be divided into the following components.
- Phase 1: Convert images or sounds or other inputs into signal data.
- Phase 2: Isolate the sensed objects from the background.
- Phase 3: Measure objects properties that are useful for classification.
- Phase 4: Assign the sensed object to a category.
- Phase 5: Take other considerations to decide on appropriate action.
Problems solved by these Phases are as follows:
- Sensing: It deals with problem arises in the input such as its bandwidth, 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 considered to be in the same category, while those whose values are quite 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 minimum-error-rate classification 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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