Vision processes can recognize patterns {pattern recognition, vision} {shape perception}.
patterns
Patterns have objects, features, and spatial relations. Patterns can have points, lines, angles, waves, histograms, grids, and geometric figures. Objects have brightness, hue, saturation, size, position, and motion.
patterns: context
Pattern surroundings and/or background have brightness, hue, saturation, shape, size, position, and motion.
patterns: movement
Mind recognizes objects with translation-invariant features more easily if they are moving. People can recognize objects that they see moving behind a pinhole.
patterns: music
Mind recognizes music by rhythm or by intonation differences around main note. People can recognize rhythms and rhythmic groups. People can recognize melodies transformed from another melody. People most easily recognize same melody in another key. People easily recognize melodies that exchange high notes for low. People can recognize melodies in reverse. People sometimes recognize melodies with both reverse and exchange.
factors: attention
Pattern recognition depends on alertness and attention.
factors: memory
Recall easiness varies with attention amount, emotion amount, cue availability, and/or previous-occurrence frequency.
animals
Apes recognize objects using fast multisensory processes and slow single-sense processes. Apes do not transfer learning from one sense to another. Frogs can recognize prey and enemy categories [Lettvin et al., 1959]. Bees can recognize colors, except reds, and do circling and wagging dances, which show food-source angle, direction, distance, and amount.
machines
Machines can find, count, and measure picture object areas; classify object shapes; detect colors and textures; and analyze one image, two stereo images, or image sequences. Recognition algorithms have scale invariance.
process levels
Pattern-precognition processing has three levels. Processing depends on effective inputs and useful outputs {computational level, Marr}. Processing uses functions to go from input to output {algorithmic level, Marr}. Processing machinery performs algorithms {physical level, Marr} [Marr, 1982].
neuron pattern recognition
Neuron dendrite and cell-body synapses contribute different potentials to axon initial region. Input distributions represent patterns, such as geometric figures. Different input-potential combinations can trigger neuron impulse. As in statistical mechanics, because synapse number is high, one input-potential distribution has highest probability. Neurons detect that distribution and no other. Learning and memory change cell and affect distribution detected.
Consciousness>Consciousness>Sense>Vision>Pattern Recognition
1-Consciousness-Sense-Vision-Pattern Recognition
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Date Modified: 2022.0224