b牄楔杮畨湥倠汤-PatternRecognition.pptVIP

Pattern Recognition Why? To provide machines with perception cognition capabilities so that they could interact independently with their environments. Pattern Recognition a natural ability of human based on some description of an object, such description is termed Pattern. Patterns and Pattern Classes Almost anything within the reach of our five senses can be chosen as a pattern: Sensory patterns: speech, odors, tastes Spatial patterns: characters, fingerprints, pictures Temporal patterns: waveforms, electrocardiograms, movies Conceptual recognition for abstract items (We will limit ourselves to deal with only physical objects/ events, but NOT abstract entities, say, concepts.) A pattern class is a group of patterns with certain common characteristics. Pattern Recognition Pattern Recognition is the science to assign an object/event of interest to one of several pre-specified categories/classes based on certain measurements or observations. Measurements are usually problem dependent. E.g. weight or height for basketball players/jockeys color for apples/oranges Feature vectors represent measurements as coordinates of points in a vector space (feature space). Pattern Recognition Systems Statistical Pattern Recognition Taps into the vast and thorough knowledge of statistics to provide a formal treatment of PR. Observations are assumed to be generated by a state of nature data can be described by a statistical model model by a set of probability functions Strength: many powerful mathematical “tools” from the theory of probability and statistics. Shortcoming: it is usually impossible to design (statistically) error-free systems. Example: OCR Major Steps Raw Features: Example Feature Extraction: OCR Example Feature Extraction Objectives: To remove irrelevant information and extract distinctive, representative information of the objects. discriminative invariant data compression = dimension reduction It is not easy! Data Modeling To build stat