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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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machine vision is not a subset of
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
Machine vision is
  • “…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
machine vision requires
Machine vision Requires
  • Mechanical handling
  • Lighting
  • Optics, including conventional imaging, lasers, diffractive optics, fiber optics, etc.
  • Sensors (Cameras)
  • Electronics (analog and digital)
  • Digital systems architecture
  • Software
illumination invariance
Illumination Invariance

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.

video signal generation
Video Signal Generation

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.
scan line from hypothetical image
Scan Line from Hypothetical Image
  • 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!
ideal low pass filter
Ideal Low-Pass Filter
  • 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)

linear high pass filtering
Linear High-Pass Filtering
  • 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.
illumination invariant processing
Illumination-Invariant Processing
  • 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
Background Illumination
  • 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.
homomorphic filtering
Homomorphic Filtering

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

homomorphic example
Homomorphic example

High-Pass Filtering Homomorphic Filtering

Original Image Log Transformed

linear filtering limitations
Linear Filtering Limitations
  • 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.
illumination invariant fir filter
Illumination Invariant FIR Filter

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

invariant filter output
Invariant Filter Output

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:

morphological background estimation
Morphological Background Estimation
  • 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.
slow and fast illumination changes
Slow and Fast Illumination Changes
  • 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.
short term compensation
Short-Term Compensation
  • 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.
alternative short term compensation
Alternative Short-Term Compensation
  • 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]

short termp compensation cont
Short Termp Compensation (cont.)
  • 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.

short termp compensation cont1
Short Termp Compensation (cont.)

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

quantization considerations
Quantization Considerations
  • 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.
accidental illumination invariance
Accidental Illumination Invariance
  • 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
image format considerations
Image Format Considerations
  • 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!