Answer
See the explanation
Work Step by Step
1. Pattern Matching and Recognition
Template Matching: Involves comparing observed data (e.g., a symbol or image) to a set of predefined templates to find the best match.
Error-Correcting Codes: Use known codewords to detect and correct errors in received data by comparing the received sequence to valid patterns.
Similarity: Both rely on comparing input to a known set of patterns to determine correctness or identity.
2. Tolerance to Noise or Distortion
Template Matching: Can handle imperfect input (e.g., blurry or distorted characters) by matching to the closest template.
Error-Correcting Codes: Designed to recover original data even if some bits are corrupted during transmission.
Similarity: Both systems are built to handle imperfect or noisy data and still produce accurate results.
3. Distance Metrics
Template Matching: Often uses geometric or statistical measures (like Euclidean distance or correlation) to evaluate similarity.
Error-Correcting Codes: Use Hamming distance to measure how many bits differ between codewords.
Similarity: Both use a form of distance measurement to evaluate how close an input is to a valid reference.
4. Redundancy and Discrimination
Template Matching: Templates are designed to be distinct enough to avoid confusion between similar inputs.
Error-Correcting Codes: Codewords are spaced apart to ensure that even with errors, the correct one can be identified.
Similarity: Both rely on well-separated reference patterns to ensure reliable identification or correction.
