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PATTERN RECOGNITION Talal A. Alsubaie SFDA
OUTLINES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WHAT IS A PATTERN? ,[object Object]
WHAT IS  A  PATTERN  CLASS ? ,[object Object],[object Object],[object Object]
WHAT IS PATTERN RECOGNITION? ,[object Object],[object Object],[object Object]
HUMAN PERCEPTION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
EXAMPLES OF APPLICATIONS
THE STATISTICAL WAY
GRID BY GRID COMPARISON Grid by Grid Comparison A A B
GRID BY GRID COMPARISON 0  0  1  0 0  0  1  0 0  1  1  1 1  0  0  1 1  0  0  1 No of Mismatch= 3 A A B 0  1  1  0 0  1  1  0 0  1  1  0 1  0  0  1 1  0  0  1
GRID BY GRID COMPARISON Grid by Grid Comparison A A B
GRID BY GRID COMPARISON 0  0  1  0 0  0  1  0 0  1  1  1 1  0  0  1 1  0  0  1 1  1  1  0 0  1  0  1 0  1  1  1 0  1  0  1 1  1  1  0 No of Mismatch= 9 A A B
PROBLEM WITH GRID BY GRID COMPARISON ,[object Object],Solution  Artificial Intelligence A-Z a-z 0-9 */-+1@#
HUMAN AND MACHINE PERCEPTION ,[object Object],[object Object],[object Object]
PATTERN RECOGNITION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
LEARNING ,[object Object],[object Object],[object Object]
[object Object],[object Object],CLASSIFICATION VS. CLUSTERING Category “A” Category “B” Clustering (Unsupervised Classification) Classification (Supervised Classification)
PATTERN RECOGNITION PROCESS (CONT.) Post- processing Classification Feature   Extraction Segmentation Sensing input Decision
PATTERN RECOGNITION PROCESS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
CASE STUDY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Salmon Sea-bass
CASE STUDY (CONT.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
CASE STUDY (CONT.) Classification Feature   Extraction Pre-processing “ Sea Bass” “ Salmon”
CASE STUDY (CONT.) ,[object Object],[object Object],[object Object],[object Object]
HOW TO SEPARATE  SEA BASS FROM SALMON? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HOW TO SEPARATE  SEA BASS FROM SALMON? ,[object Object],[object Object],[object Object]
FEATURE SELECTION “ Good” features “ Bad” features
DECISION BOUNDARY
DECISION BOUNDARY (CONT.) More complex model result more complex boundary
DECISION BOUNDARY (CONT.) Different criteria lead to different decision boundaries
DECISION BOUNDARY (CONT.) ,[object Object],[object Object]
DECISION BOUNDARY (CONT.) ,[object Object],[object Object],[object Object],[object Object]
CASE STUDY (CONT.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
THE DESIGN CYCLE
DEMO
DEMO ,[object Object],[object Object]
DEMO (CONT.) ,[object Object]
VIDEO DEMO
Q & A
THANK YOU

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Pattern Recognition