Artificial Intelligence Chapter 6 Robot Vision Biointelligence Lab School of Computer Sci. & Eng. Seoul National University

Artificial IntelligenceArtificial IntelligenceChapter 6Chapter 6

Robot VisionRobot Vision

Biointelligence Lab

School of Computer Sci. & Eng.

Seoul National University

(c) 2000-2002 SNU CSE Biointelligence Lab

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IntroductionIntroduction Computer vision

Endowing machines with the means to “see” Create an image of a scene and extract features

Very difficult problem for machines Several different scenes can produce identical images. Images can be noisy . Cannot directly ‘invert’ the image to reconstruct the scene.

Figure 6.1 The Many-to-One Nature of the Imaging Process


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Steering an AutomobileSteering an Automobile

ALVINN system [Pomerleau 1991,1993] Uses Artificial Neural Network

Used 30*32 TV image as input (960 input node) 5 Hidden node 30 output node

Training regime: modified “on-the-fly” A human driver drives the car, and his actual steering angles ar

e taken as correct labels for the corresponding inputs. Shifted and rotated images were also used for training.

ALVINN has driven for 120 consecutive kilometers at speeds up to 100km/h.


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Steering an Automobile-ALVINN Steering an Automobile-ALVINN networknetwork

Figure 6.2 The ALVINN Network


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Two stages of Robot Vision (1/3)Two stages of Robot Vision (1/3) Finding out objects in the scene

Looking for “edges” in the image Edge:a part of the image across which the image intensity or some other

property of the image changes abruptly.

Attempting to segment the image into regions. Region:a part of the image in which the image intensity or some other property

of the image changes only gradually.

Figure 6.3 Scene Discontinuities


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Two stages of Robot Vision (2/3)Two stages of Robot Vision (2/3)

Image processing stage Transform the original image

into one that is more amendable to the scene analysis stage.

Involves various filtering operations that help reduce noise, accentuate edges, and find regions.

Scene analysis stage Attempt to create an iconic or a

feature-based description of the original scene, providing the task-specific information.

Figure 6.4 The Two Stages of Robot Vision


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Two stages of Robot Vision (3/3)Two stages of Robot Vision (3/3)

Scene analysis stage produces task-specific information. If only the disposition of the blocks

is important, appropriate iconic model can be (C B A FLOOR)

If it is important to determine whether there is another block on top of the block labeled C, adequate description will include the value of a feature, CLEAR_C.

Figure 6.5 A Robot in a Room with Toy Blocks


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Averaging (1/4)Averaging (1/4)

Original image can be represented as an m*n array of numbers. The numbers represent the light intensities at corresponding points in the image.

Certain irregularities in the image can be smoothed by an averaging operation.

Averaging operation involves sliding an averaging widow all over the image array.


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Smoothing operation thickens broad lines and eliminates thin lines and small details.

The averaging window is centered at each pixel, and the weighted sum of all the pixel numbers within the averaging window is computed. This sum then replaces the original value at that pixel.

Figure 6.6 Elements of the Averaging Operation


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Common function used for smoothing is a Gaussian of two dimensions.

Convolving an image with a Gaussian is equivalent to finding the solution to a diffusion equation when the initial condition is given by the image intensity field.


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Figure 6.7 The Gaussian Smoothing Function

Figure 6.8 Image Smoothing with a Gaussian Filter


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Edge enhancement (1/2)Edge enhancement (1/2)

Edge: any boundary between parts of the image with markedly different values of some property.

Edges are often related to important object properties.

Edges in the image occur at places where the second derivative of the image intensity is zero.


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Edge enhancement (2/2)Edge enhancement (2/2)

Figure 6.9 Edge Enhancement

Figure 6.10 Taking Derivatives of Image Intensity


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Combining Edge Enhancement with Combining Edge Enhancement with Averaging (1/2)Averaging (1/2) Edge enhancement alone would tend to emphasize

noise elements along with enhancing edges. To be less sensitive to noise, both operations are

needed. (First averaging and then edge enhancing) We can convolve the one-dimensional image with

the second derivative of a Gaussian curve to combine both operation.


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Combining Edge Enhancement with Combining Edge Enhancement with Averaging (2/2)Averaging (2/2) Laplacian is second-derivate-type operation that enhances edges of an

y orientation. Laplacian of the two-dimensional Gaussian function looks like an upsi

de-down hat, often called a sombrero function. Entire averaging/edge-finding operation can be achieved by convolvin

g the image with the sombrero function (called Laplacian filtering)

Figure 6.11 The Sombrero Function Used in Laplacian Filtering

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Artificial Intelligence Chapter 6 Robot Vision Biointelligence Lab School of Computer Sci. & Eng. Seoul National University