The Computer and the Mind - Revision history

Admin: /* Plans and Productions */

2017-05-09T23:05:15Z

Plans and Productions

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	==Plans and Productions==		===Plans and Productions===

	Breadth-first search: number of routes to explore grows exponentially, doubling at each step for even the simplest decision tree (two decisions).		Breadth-first search: number of routes to explore grows exponentially, doubling at each step for even the simplest decision tree (two decisions).
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	- p. 1161		- p. 1161
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			===Parallel Distributed Processing===

			etc...

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Admin: /* Components of Memory */

2017-05-09T23:04:19Z

Components of Memory

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 There is evidently a sensory memory for a visual image that persists for about a quarter of a second. This memory might be useful for reading a newspaper in a lightning storm...
-Similar effects have been demonstrated for hearing - you retain a brief "echo" of what someone has just said, which fades rapidly. Each sense musht ahve its own memory system whose contents are continuously replaced by new incoming information and cannot be rehearsed.
+Similar effects have been demonstrated for hearing - you retain a brief "echo" of what someone has just said, which fades rapidly. Each sense must have its own memory system whose contents are continuously replaced by new incoming information and cannot be rehearsed.
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Admin: /* Components of memory */

2017-05-09T22:51:33Z

Components of memory

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-===Components of memory===
+===Components of Memory===
 {{Quote|
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 Broadbent, "Perception and Communication." Oxford. 1958.
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Admin: /* Part 3: Learning, Memory, and Action */

2017-05-09T21:42:25Z

Part 3: Learning, Memory, and Action

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 Donald Broadbent
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Admin: /* References */

2017-05-07T00:55:04Z

References

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	Marroquin, J. L. "Human visual perception of structure." Master's thesis, Dept of EE and CS, MIT, 1976.		Marroquin, J. L. "Human visual perception of structure." Master's thesis, Dept of EE and CS, MIT, 1976.

			===Seeing the World in Depth===

			Stereopsis - fusion of disparate images so as to see the world in depth

			Problem of disparity: slight differences in images from eye to eye. Once size and direction of disparity is known, trigonometry can be used to find relative depth of point.

			Puzzling problem: how to match two images, if neither image has any particular location on the retina that is the same between the two images?

			Could use top-down image processing, identifying objects in the scene and matching up key points. But this requires two separate images being held in the head simultaneously, identification of objects and orientations of each independently, so seems unlikely.

			Alternative is bottom-up processing, matching the intensity values in a pair of gray-level arrays. However, this also presents a challenge, in that intensities can be different between eyes (e.g. sunglasses over one eye).

			If we want to write a program to do this, we have to ask: how does nature do it?

			Run experiments to better understand the mechanism:
			* First, see how the visual system responds when there is no high-level knowledge that can be exploited (in which case, we're isolating the bottom-up mechanism)
			* Second, modify input data so they are badly corrupted, so process is not entirely data-driven.

			====Bottom up Stereopsis====

			The first approach, bottom-up stereopsis, in which we remove high-level knowledge, has proved invaluable in understanding/studying stereopsis. Stereoscope - victorian invention, combines two photographs of the same location from slightly different perspectives. This consists of using a mirror to manipulate images and present the brain with a particular stereo image. These are the "Magic Eye" style images, in which there is no high level detail for the brain to latch onto. And yet the brain can still perceive shapes and see the Magic Eye shape "pop out".

			John Frisby and John Clatworthy - found that explicit high-level knowledge cannot speed up the process of perceiving stereopsis pictures.

			How does the brain do this? Several hypotheses:
			* First, one thing cannot be in two places at the same time - so there is a uniqueness constraint (on point can be matched with one and only one point)
			* Surfaces are typically opaque and smooth, so depth from observer varies continuously rather than undergoing sudden changes (continuity constraint)

			Imagine two eyes shooting out two rays. If each eye perceives the same point, that's 3^3 or 9 possible intersection points where that single point's location could be.
			* The first constraint - the uniqueness constraint - implies that the point can only be along one line of sight for each eye. So out of the nine total locations, really there are only three possible, at a given time.
			* The second constraint - the continuity constraint - implies the points must be roughly the same depth from the observer. This leads to other points being eliminated.
			* Constraints act in an exclusionary way - if X, then not Y or Z

			Program for random dot stereograms:
			* Constructing arrays of fragments/sequences
			* Use large number of processors, applying calculation to local values.
			* Relaxation - system starts at starting point, continues to compute, uses output from each cycle as input to next cycle, until it gradually relaxes into stable configuration of values
			* Relaxation program for stereograms: takes input rows from two stereograms, array of processors work out possible fusions.
			* Array is 3D, with each 2D slice corresponding to one horizontal row from two stereograms.
			* 3D volume represents all different depths of possible fusions of dots in white rows
			* The two constraints, uniqueness and continuity, are implemented in the program; exitation from neighbors (continuity constraint), inhibition from processors in same line of sight (uniqueness constraint), so these values will be summed and subtracted. If the combination of excitation and inhibition exceeds a threshold value, it is chosen as a possible solution and the program moves on to the next step with it.
			* Only local information (from neighbors) is required, meaning this scales well.

			====Real Stereopsis====

			Questions still remain: how does the brain match up visual points? Can't be based on illumination or light, since that does not interfere with the mechanism.

			Hypothesis: brain begins with zero-crossings, and specifically, zero-crossings starting with a very coarse filter (fewer zero crossings, so fewer edges to match). Edges going from dark-to-light or light-to-dark are matched up. Once areas are matched up, initial registration of coarse filtere images, and brain then repeats on zero-crossings with a smaller filter, with more details (but a better "initial guess").

			Physiologically more plausible than the computationally complex random dot method.

			Eric Grimson has applied this to aerial stereophotographs.

			Colin Blakemore reported cells in visual cortex that corresponded to discrepancies in the same line of sight, but discrepancies were not in zero-crossings. John Mayhew and John Frisby demonstrated in some cases it is nearby peaks in change of gradient. Robert Gregory found edges of objects do not always correspond to elements matched in stereopsis.

			Watt and Morgan advocated peaks-and-troughs approach, think zero-crossings do not play major role.

			James Gibson found gradients in texture can be source of information about depth and orientation of surfaces: textures consisting of similar shapes repeated on a surface. Gradient can indicate orientation of surface. There are computer programs that can interpret textured gradient images, but that does not mean the human mechanism for perceiving them is well-known.

			Other cues to depth: distant objects are hazier, bluer, higher in visual field; parallel edges converge with distance; motion of an object affords multiple views; visible boundaries of object against background are also cues to shape.

			Rotating cylinders of random dots: without the motion of two coaxial cylinders painted with random dots, there is no ability to identify a pattern. But when dots begin moving, or are projected on screen, becomes clear.

			Importance of rigidity: Gunnar Johansson ran experiments in pitch dark room, with lights attached to human limbs at the joints, and attached at the middle of the limbs. In the former case, people could perceive someone walking via the the lights alone. In latter case, people could not sense motion. Visual system assumes adjacent lights/points connected by rigid entities.

			====Contour and Shape====

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			I hold my hand between a light and a wall, manipulate it appropriately, and what you seeon the wall is the shadow of a rabbit. The phenomenon raises again the argument about the impossibility of vision: there are an infinite number of different three-dimensinal shapes that can give rise to the same two-dimensional shadow. how is it, then, that you see a rabbit?

			The same question arises not just with shadows, but with silhouettes and the visible contours of objects against their backgrounds... Once again, the issue is whether vision relies on a knowledge of such things as rabbits or uses low-level assumptions to work bottom up from the visual data.
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			====References====

			S. Ullman, "The Interpretation of Visual Motion." MIT Press, 1979.

			Marr, "Vision" (1982)

			Special issues of journals Artificial Intelligence (1981, Volume 17) and Cognition (184, Volume 18) are devoted to vision

			Koenderink (14) - advanced account of role of contour as cue to shape

			Brady (1983) - analysis of shape from standpoint of machine vision

			===Scenes, Shapes, and Images===

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			In what pattern does light reflect from objects on to a surface? This problem in optics is well-formed: it can be solved much as a set of equations can be solved. Vision is inverse optics. It has to establish what objects caused the patterns of light projected on to the retinae. This problem is not well-formed: it is almost impossible to solve, because there are too many unknowns...

			When the mind solves a seemingly impossible task, it must have a secret weapon, and, as I remarked,that secret weapon is knowledge. Knowledge comes in two main varieties that are used in two main ways, top down or bottom up.

			One sort of knowledge arises from evolution, and its wisdom is built into the process of the nervous system. This knowledge is not really knowledge at all...

			The other sort of knowledge accrues during the lifetime of an individual... In fact, you are not always aware either of using such knowledge or of its nature.
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			==Part 3: Learning, Memory, and Action==

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			We can now see why the problem of central control in brains is linked with that of long-term memory. It is obvious that no animal, let alone man, carries out a simple and predictable sequence of operations on the inputs which stimulate it. We must suppose therefore that the control exercised over the brain's operations is one which varies not only with the nature of the input but also with the results of past operations.

			Donald Broadbent
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Admin: /* References */

2017-05-07T00:11:37Z

References

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	====References====		====References====

	Harmon, L. D. ~~'''~~The recognition of faces.~~'''~~ Scientific American, November 1973, p 75.		Harmon, L. D. "The recognition of faces." Scientific American, November 1973, p 75.

	Mayhew Frisby 1984 (technical account of computer vision)		Mayhew Frisby 1984 (technical account of computer vision)

Admin: /* Flags */

2017-05-07T00:11:20Z

Flags

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	{{ReadingFlag}}		{{ReadingFlag}}

	[[Category:~~Machine Learning~~]]		[[Category:ML]]
	[[Category:~~Neural Networks~~]]		[[Category:NN]]

Admin: /* Part Two: Vision */

2017-05-07T00:11:00Z

Part Two: Vision

Admin: /* Locating Gradients in Intensity */

2017-05-07T00:09:48Z

Locating Gradients in Intensity

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	* Vision stage 3: the primal sketch		* Vision stage 3: the primal sketch

	====Locating Gradients in Intensity====		===Locating Gradients in Intensity===

	Gray-level array has certain amounts of noise - random fluctuations. How to differentiate between small scale changes and large scale changes?		Gray-level array has certain amounts of noise - random fluctuations. How to differentiate between small scale changes and large scale changes?

Admin: /* Third State of Vision: Primal Sketch */

2017-05-07T00:09:36Z

Third State of Vision: Primal Sketch

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	Usually, focusing on the primal sketch and ignoring details will lead to a loss of information. However, you can also gain information. Example: checkerboard image of Lincoln (image by Leon D. Harmon).		Usually, focusing on the primal sketch and ignoring details will lead to a loss of information. However, you can also gain information. Example: checkerboard image of Lincoln (image by Leon D. Harmon).

			===Cost of Visual Processing===

			Major challenges of visual processing with onboard computers: computers have far fewer interconnections (electronic nerves) than biological systems, so slower bus speeds and bandwidth. Crucial to work fast enough for the task at hand - e.g., self-driving car can't take two seconds to process an image.

			Major computational cost is filtering out gray-level array. For a 1000 x 1000 array, need to apply filter to every pixel, for every filter size. Specialty hardware can help, but still has significant costs.

			Workarounds include limiting vision of the world to a primal sketch - much like the housefly, which does not need to perform 3D extrapolation from 2D images, everything boils down to a set of algorithms. Landing algorithm: if visual field expands at high speed, fly turns feet toward center of expanding plane, and stops flying when its feet hit the surface. Mate tracking: find small black patch moving against a background. Left and right wing power governed by patch position in visual field and by angular velocity, so fly keeps the target centered in its visual field and flies toward it.

			For a fly, vision is, in fact, impossible. But the mechanism is tuned to work for specific scenarios with limited information. Thus there are many tasks a fly cannot accomplish.

			===References===

			Harmon, L. D. '''The recognition of faces.''' Scientific American, November 1973, p 75.

			Mayhew Frisby 1984 (technical account of computer vision)

			Watt 1988 (advanced monograph on the initial stages of human vision)

			Marroquin, J. L. "Human visual perception of structure." Master's thesis, Dept of EE and CS, MIT, 1976.

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