Introduction to Cognitive Science Lecture 2: Vision in Humans and Machines 1 Vision in Humans and Machines September 10, 2009

Introduction to Cognitive Science Lecture 2: Vision in Humans

and Machines

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Vision in Humans Vision in Humans and Machinesand Machines

September 10, 2009

Visible light is just a part of the Visible light is just a part of the electromagnetic spectrumelectromagnetic spectrum

September 10, 2009 Introduction to Cognitive Science Lecture 2: Vision in Humans and Machines

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Cross Section of the Human EyeCross Section of the Human Eye


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Anatomy of the Visual SystemAnatomy of the Visual System The EyesThe Eyes

Cornea:Cornea: Transparent outer covering of the eye that Transparent outer covering of the eye that

admits lightadmits light

Pupil:Pupil: Adjustable opening in the iris that regulates Adjustable opening in the iris that regulates

the amount of light that enters the eyethe amount of light that enters the eye

Iris:Iris: Pigmented ring of muscles situated behind Pigmented ring of muscles situated behind

the corneathe cornea


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Anatomy of the Visual SystemAnatomy of the Visual System PhotoreceptorsPhotoreceptors

Retina:Retina: The neural tissue and photoreceptive cellsThe neural tissue and photoreceptive cellslocated on the inner surface of the posteriorlocated on the inner surface of the posteriorportion of the eye.portion of the eye.

Rod:Rod: Photoreceptor cells of the retina, sensitive to Photoreceptor cells of the retina, sensitive to

light of low intensity.light of low intensity.

Cone:Cone: Photoreceptor cells of the retina; maximally Photoreceptor cells of the retina; maximally

sensitive to one of three different wavelengths sensitive to one of three different wavelengths of light and hence encodes color vision.of light and hence encodes color vision.

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Lens:Lens:Consists of a series of transparent, onion-like Consists of a series of transparent, onion-like

layers. Its shape can be changed by layers. Its shape can be changed by contraction of ciliary muscles.contraction of ciliary muscles.

Accommodation:Accommodation: Changes in the thickness of the lens, Changes in the thickness of the lens,

accomplished by the ciliary muscles, that accomplished by the ciliary muscles, that focus images of near or distant objects on the focus images of near or distant objects on the retinaretina

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Fovea:Fovea: Area of retina that mediates the most acute Area of retina that mediates the most acute

vision. Contains only color-sensitive cones.vision. Contains only color-sensitive cones.

Optic Disk:Optic Disk: Location on retina where fibers of ganglion Location on retina where fibers of ganglion

cells exit the eye. Responsible for the blind cells exit the eye. Responsible for the blind spot.spot.

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Coding of Visual Information in the RetinaCoding of Visual Information in the Retina

Coding of Light and DarkCoding of Light and Dark

Receptive field:Receptive field: That portion of the visual field in which the That portion of the visual field in which the

presentation of visual stimuli will produce an presentation of visual stimuli will produce an alteration in the firing rate of a particular alteration in the firing rate of a particular neuron.neuron.

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PhotoreceptorPhotoreceptor

BipolarBipolar

GanglionGanglion

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Major cell types of the retinaMajor cell types of the retina

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Receptive fieldsReceptive fields

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Color MixingColor Mixing

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Coding of Visual Information in the RetinaCoding of Visual Information in the Retina Photoreceptors: Trichromatic CodingPhotoreceptors: Trichromatic Coding

Peak Peak wavelengthwavelength sensitivities of the three cones: sensitivities of the three cones:Blue cone:Blue cone: Short-Short- Blue-violet (420 nm) Blue-violet (420 nm) Green cone:Green cone: Medium-Medium- Green (530 nm)Green (530 nm)Red Cone:Red Cone: Long-Long- Yellow-green (560nm)Yellow-green (560nm)

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Coding of Visual Information in the RetinaCoding of Visual Information in the Retina Retinal Ganglion Cells:Retinal Ganglion Cells:

Opponent-Process CodingOpponent-Process Coding

Negative afterimage:Negative afterimage: The image seen after a portion of the retina is exposed to an The image seen after a portion of the retina is exposed to an

intense visual stimulus; consists of colors complimentary to intense visual stimulus; consists of colors complimentary to those of the physical stimulus.those of the physical stimulus.

Complimentary colors:Complimentary colors: Colors that make white or gray when mixed together.Colors that make white or gray when mixed together.

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Analysis of Visual InformationAnalysis of Visual Information

Anatomy of the Striate cortexAnatomy of the Striate cortex

David Hubel and Torsten WieselDavid Hubel and Torsten Wiesel

1960’s at Harvard University1960’s at Harvard University

Discovered that neurons in the visual cortex did Discovered that neurons in the visual cortex did not simply respond to light; they selectively not simply respond to light; they selectively responded to specific features of the visual world.responded to specific features of the visual world.

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Stimuli in Stimuli in receptive receptive field of field of neuronneuron

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Cat V1 (striate Cat V1 (striate cortex)cortex)

Orientation Orientation preference preference mapmap

Ocular Ocular dominance dominance mapmap

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“Data Flow Diagram”of Visual Areas inMacaque Brain

Blue:motion perception pathway

Green:object recognition pathway


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Computer VisionComputer VisionA typical computer vision applications are complex and consist of different levels of processing, from the low-level pixel-by-pixel analysis to the high-level creation of scene descriptions.

Generally, computer vision systems consist of an image processing stage, followed by a scene analysis stage.

The following slide outlines the structure of a computer vision system.


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Computer VisionComputer Vision

A simple two-stage model of computer vision:

Image processing

Sceneanalysis

Bitmap image

Scene description

feedback (tuning)

Prepare image for scene analysis

Build an iconic model of the world


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Computer VisionComputer VisionThe image processing stage prepares the input image for the subsequent scene analysis.

Usually, image processing results in one or more new images that contain specific information on relevant features of the input image.

The information in the output images is arranged in the same way as in the input image. For example, in the upper left corner in the output images we find information about the upper left corner in the input image.


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Computer VisionComputer VisionThe scene analysis stage interprets the results from the image processing stage.

Its output completely depends on the problem that the computer vision system is supposed to solve.

For example, it could be the number of bacteria in a microscopic image, or the identity of a person whose retinal scan was input to the system.


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Digitizing Visual ScenesDigitizing Visual ScenesWith regard to spatial resolution, we will map the intensity in our image onto a two-dimensional finite array:

[0, 0] [0, 1] [0, 2] [0, 3]

[1, 0] [1, 1] [1, 2] [1, 3]

[2, 0] [2, 1] [2, 2] [2, 3]

y’

x’


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ThresholdingThresholding

Here, the right image is created from the left image by Here, the right image is created from the left image by thresholding, assuming that object pixels are darker thresholding, assuming that object pixels are darker than background pixels.than background pixels.

As you can see, the result is slightly imperfect (dark As you can see, the result is slightly imperfect (dark background pixels).background pixels).

September 4, 2007 Computer VisionLecture 1: Digital Images/Binary Image Processing

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Geometric PropertiesGeometric Properties


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Geometric PropertiesGeometric PropertiesWe could teach our program what the objects look We could teach our program what the objects look like at different sizes and orientations, and let the like at different sizes and orientations, and let the program search all possible positions in the input.program search all possible positions in the input.

However, that would be a very inefficient and However, that would be a very inefficient and inflexible approach.inflexible approach.

Instead, it is much simpler and more efficient to Instead, it is much simpler and more efficient to standardizestandardize the input before performing object the input before performing object recognition.recognition.

We can We can scalescale the input object to a given size, the input object to a given size, centercenter it in the image, and it in the image, and rotaterotate it towards a specific it towards a specific orientation.orientation.


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Noise ReductionNoise Reduction

Here, a size filter perfectly removes all noise in the Here, a size filter perfectly removes all noise in the input image.input image.


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Noise ReductionNoise Reduction

However, if our threshold is too high, “accidents” may However, if our threshold is too high, “accidents” may happen.happen.


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Edge DetectionEdge Detection

Calculating the magnitude of the brightness gradient Calculating the magnitude of the brightness gradient with a Sobel filter. Left: original image; right: filtered with a Sobel filter. Left: original image; right: filtered image.image.


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TextureTexture


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TextureTextureTexture Texture is an important cue for biological vision is an important cue for biological vision systems to estimate the boundaries of objects.systems to estimate the boundaries of objects.

Also, Also, texture gradienttexture gradient is used to estimate the is used to estimate the orientation of surfaces.orientation of surfaces.

For example, on a perfect lawn the grass texture is For example, on a perfect lawn the grass texture is the same everywhere.the same everywhere.

However, the further away we look, the finer this However, the further away we look, the finer this texture becomes – this change is called texture texture becomes – this change is called texture gradient.gradient.

For the same reasons, texture is also a useful feature For the same reasons, texture is also a useful feature for for computer vision systemscomputer vision systems..


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Texture GradientTexture Gradient


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TextureTextureThe most fundamental question is: How can we The most fundamental question is: How can we “measure”“measure” texture, i.e., how can we quantitatively texture, i.e., how can we quantitatively distinguish between different textures?distinguish between different textures?

Of course it is not enough to look at the intensity of Of course it is not enough to look at the intensity of individual individual pixels.pixels.

Since the repetitive local arrangement of intensity Since the repetitive local arrangement of intensity determines the texture, we have to analyze determines the texture, we have to analyze neighborhoods neighborhoods of pixels to measure texture of pixels to measure texture properties.properties.


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Stereo VisionStereo Vision

Geometry of binocular stereo visionGeometry of binocular stereo vision


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Statistical Pattern RecognitionStatistical Pattern Recognition


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Object RecognitionObject RecognitionThis algorithm learns to recognize 25 different chairs:

It is shown each chair from 25 different viewing angles.


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The AlgorithmThe Algorithm

Documents

Introduction to Cognitive Science Lecture 2: Vision in Humans and Machines 1 Vision in Humans and Machines September 10, 2009