Brain has mechanisms to recognize patterns {pattern recognition, methods} {pattern recognition, mechanisms}.
mechanism: association
The first and main pattern-recognition mechanism is association (associative learning). Complex recognition uses multiple associations.
mechanism: feature recognition
Object or event classification involves high-level feature recognition, not direct object or event identification. Brain extracts features and feeds forward to make hypotheses and classifications. For example, people can recognize meaningful facial expressions and other complex perceptions in simple drawings that have key features [Carr and England, 1995].
mechanism: symbol recognition
To recognize letters, on all four sides, check for point, line, corner, convex curve, W or M shape, or S or squiggle shape. 6^4 = 1296 combinations are available. Letters, numbers, and symbols add to less than 130, so symbol recognition is robust [Pao and Ernst, 1982].
mechanism: templates
Templates have non-accidental and signal properties that define object classes. Categories have rules or criteria. Vision uses structural descriptions to recognize patterns. Brains compare input patterns to template using constraint satisfaction on rules or criteria and then selecting best-fitting match, by score. If input activates one representation strongly and inhibits others, representation sends feedback to visual buffer, which then augments input image and modifies or completes input image by altering size, location, or orientation. If representation and image then match even better, mind recognizes object. If not, mind inhibits or ranks that representation and activates next representation.
mechanism: viewpoint
Vision can reconstruct how object appears from any viewpoint using a minimum of two, and a maximum of six, different-viewpoint images. Vision calculates object positions and motions from three views of four non-coplanar points. To recognize objects, vision interpolates between stored representations. Mind recognizes symmetric objects better than asymmetric objects from new viewpoints. Recognition fails for unusual viewpoints.
importance: frequency
For recognition, frequency is more important than recency.
importance: orientation
Recognition processing ignores left-right orientation.
importance: parts
For recognition, parts are more important for nearby objects.
importance: recency
For recognition, frequency is more important than recency.
importance: size
Recognition processing ignores size.
importance: spatial organization
For recognition, spatial organization and overall pattern are more important than parts.
method: averaging
Averaging removes noise by emphasizing low frequencies and minimizing high frequencies.
method: basis functions
HBF or RBF basis functions can separate scene into multiple dimensions.
method: cluster analysis
Pattern recognition can place classes or subsets in clusters in abstract space.
method: feature deconvolution
Cerebral cortex can separate feature from feature mixture.
method: differentiation
Differentiation subtracts second derivative from intensity and emphasizes high frequencies.
method: generalization
Vision generalizes patterns by eliminating one dimension, using one subpattern, or including outer domains.
method: index number
Patterns can have algorithm-generated unique, unambiguous, and meaningful index numbers. Running reverse algorithm generates pattern from index number. Similar patterns have similar index numbers. Patterns differing by subpattern have index numbers that differ only by ratio or difference. Index numbers have information about shape, parts, and relations, not about size, distance, orientation, incident brightness, incident light color, and viewing angle.
Index numbers can be power series. Term coefficients are weights. Term sums are typically unique numbers. For patterns with many points, index number is large, because information is high.
Patterns have a unique point, like gravity center. Pattern points have unique distances from unique point. Power-series terms are for pattern points. Term sums are typically unique numbers that depend only on coordinates internal to pattern. Patterns differing by subpattern differ by ratio or difference.
method: lines
Pattern recognition uses shortest line, extends line, or links lines.
method: intensity
Pattern recognition uses gray-level changes, not colors. Motion detection uses gray-level and pattern changes.
method: invariance
Features can remain invariant as images deform or move. Holding all variables, except one, constant can find the derivative with respect to the non-constant variable, and so calculate partial differentials to measure changes/differences and find invariants.
method: line orientation
Secondary visual cortex neurons can detect line orientation, have large receptive fields, and have variable topographic mapping.
method: linking
Vision can connect pieces in sequence and fill gaps.
method: optimization
Vision can use dynamic programming to optimize parameters.
method: orientation
Vision accurately knows surface tilt and slant, directly, by tilt angle itself, not by angle function [Bhalla and Proffitt, 1999] [Proffitt et al., 1995].
method: probability
Brain uses statistics to assign probability to patterns recognized.
method: registers
Brain-register network can store pattern information, and brain-register network series can store processes and pattern changes.
method: search
Matching can use heuristic search to find feature or path. Low-resolution search over whole image looks for matches to feature templates.
method: separation into parts
Vision can separate scene into additive parts, by boundaries, rather than using basis functions.
method: sketching
Vision uses contrast for boundary making.
Consciousness>Consciousness>Sense>Vision>Pattern Recognition>Methods
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Date Modified: 2022.0224