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# Machine vision is not a subset of: - PowerPoint PPT Presentation

Machine vision is not a subset of:. Computer Science Image Processing Pattern Recognition Artificial Intelligence (whatever this is!) However, tools and concepts from these areas are often applied to vision applications. Machine vision is.

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## PowerPoint Slideshow about ' Machine vision is not a subset of:' - yosefu

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Presentation Transcript

• Computer Science

• Image Processing

• Pattern Recognition

• Artificial Intelligence (whatever this is!)

However, tools and concepts from these areas are often applied to vision applications.

• “…the use of devices for optical, non-contact sensing to receive and interpret an image of a real scene automatically, in order to obtain information and or control machines or processes."Automated Vision Association, 1985

• Mechanical handling

• Lighting

• Optics, including conventional imaging, lasers, diffractive optics, fiber optics, etc.

• Sensors (Cameras)

• Electronics (analog and digital)

• Digital systems architecture

• Software

A Simple 1-D Model For Illumination

Incident light reaching a linear sensor can be expressed as:

I[n]=I0[n]s[n]

In which I[n] is the light reaching the nth pixel, L[n] is the background illumination and s[n] is the value of reflection or transmission of the object being observed which can range between 0 and 1.

The sensor converts the light into an electrical signal v[n]

v[n] = b[n]s[n]

in which b[n] is proportional to I0[n].

• In many vision applications, b[n] varies slowly as a function of distance because background illumination does not change rapidly.

• Small objects have 50% contrast with background

• Background illumination variations can be caused by optics and lighting

• The objective here is to find the dark features!

• Filtering provides an estimate of b[n].

• Subtracting the original image from the low-pass filtered image provides the lower curve:

yhp[n] = b[n]s[n] ‑ b[n] = b[n](s[n] - 1)

• Results on previous slide show that the resulting high-pass filtering provides a significant improvement in the detection of dark features.

• A single threshold can be selected which will detect all of the features but the sensitivity varies because of the illumination level.

• Differentiating between features with different contrast values would be difficult, however.

• Transforming image pixel values logarithmically, however, removes the effects of varying illumination.

• A logarithmic image is sometimes referred to as a density image using medical imaging concepts.

• A single threshold will detect all features equally well regardless of light level.

• Background illumination for the following discussion refers to the source or sources that illuminate a given scene.

• The illumination may be either from the front or back but not from both at the same time.

• Background illumination may be measured directly in some vision applications but has to be estimated in others.

• If the background can be measured directly or estimated, illumination-invariant techniques can be employed so that a feature in a scene may be extracted regardless of the illumination level at the feature’s location.

Inverse

Nonlinear

Transform

• One classical image enhancement technique is based on combining linear filtering with a nonlinear point transform of the gray-scale values of the input image.

• If a logarithmic transform is used, the result is illumination invariant.

• The inverse operation is not useful when the results will be thresholded.

Nonlinear

Transform

Output

Image

High-Pass

Filter

Input

Image

High-Pass Filtering Homomorphic Filtering

Original Image Log Transformed

• In the hypothetical example, an assumption was made that a linear filter could be obtained that would filter out the dark features.

• In actuality, this turns out to be difficult to accomplish and implementation requires complicated (and therefor computationally intensive) algorithms and may very well not provide a good estimate of the background illumination.

• As will be shown, however, it is still possible to obtain illumination-invariant results.

Consider a FIR filter that will remove the DC component over

a neighborhood

Assuming that all pixels have gray-scale value , then

From this, it can be inferred that

Now, since v[n] = s[n]b[n]

For slowly varying illumination, b[k] is assumed to be

the constant  over the filter extent so y[n] does not

depend on illumination:

• For an image containing dark regions smaller than some given structuring element, a gray-scale closing operation can be used to estimate the background.

• With a good estimate of the background, an illumination invariant output is still obtained.

• Illumination changes can occur over a variety of time spans.

• Slow variations are often associated with filament evaporation in incandescent lamps and similar aging effects.

• Slow variations can, in some cases, be corrected by viewing a diffuse uniform reflector periodically.

• Rapid variations are often associated with voltage fluctuations and ripple due to sinusoidal driving voltages.

• Regulated DC sources can be used for the illumination sources but is quite expensive for high-power applications.

• In some applications, cameras can be scanned synchronously with the power line although AC power regulation may still be required, i.e. Sola transformers.

• This approach, however, is unsuitable for high-speed matrix and line-scan camera applications.

• Suppose that the effective illumination reaching the sensor is a function of both time and spatial position:

I[j,n] =I0[j,n]s[j,n]

• Since illumination sources are usually driven from a common power source, the light output of each illumination source will vary temporally in exactly the same manner so that I0[j,n] can be decomposed into the product:

I0[j,n] = It[j]Is[n]

• The voltage output of the sensor becomes

v[j,n] = It[j]b[n]s[j,n]

• The density representation becomes

d[j,n] = log(It[j]b[n]s[j,n]) = log(It[j]) + log(b[n]s[j,n])

• Let a[j] be the average value of the N density-image pixels in a reference region which never changes for the jth sample:

logIt[j] is constant for all pixels in region R since it only varies as a function of the sample number.

A constant kR can be defined as

A normalized image in which compensation for the short-term variations are provided follows:

dn[j,n] = logIt[j] + log(b[n]s[j,n] - a[j]

= logIt[j] + log(b[n]s[j,n] - log(It[j]) - kR

=log(b[n]s[j,n]) - kR

the amount of processing required is relatively small.

The region R need only be large enough to minimize errors due to camera signal noise.

The image normalization operation simply requires that a constant value be added to each pixel

• Effects of quantization error when logarithmic transformation is performed before (nearly horizontal line) after (approximately triangular waveform).

• The lower and higher ramps represent background and foreground data.

• Scaled gamma correction with an exponent of .45 (top curve) and logarithmic transformation function (bottom curve) are very similar except for low gray levels.

• Enabling gamma correction can provide a good approximation to a logarithmic transformation.

• The SNR of typical video cameras probably does not justify a better approximation

• GIF: The original Compuserve format!

• JPEG: Very good compression available.

• BMP: The Windows standard.

• PCX: The original PC paintbrush format

• TIFF: The almost universal standard !

• PGM: Portable Gray Map: No Endian Problems!

• Irfan View reads all of the above and many more!