Chapter 8. Basic Image Process The visual information can be recorded by a TV camera or a 2D array of CCD sensors. A digital image is formed.

Digital image formation  visual information recorded by a TV camera or a two dimensional array of CCD sensors  analog to digital (A/D) converter : digitizing the light intensity values.  black-white image: pixel matrix of digital grey level value  colour image: pixel matrix of R / G/ B -- optical information changed into electrical signals, finally turn out to be computer image files.

Color Terminology Hue Color wheel Primary, secondary, tertiary Chroma (saturation) Intensity or purity Distance from gray Value (lightness or brightness)

Many, many RGB/HSV/HSL Color Tools eyedropper

Perceptual Organization of Color Not “ RGB ” as in Colorimetry Lightness Hue Colorfulness

Original image Tint Shirt Only Overall Purple Tint Chromatic Adaptation

application of basic image processing in computer vision :  conditioning the image, either by highlighting its contrast or by filtering  devoted to eliminating non-relevant information from the scene  extracting characteristics, enable us to proceed to its classification, localization, manipulation and so on

1.Image Highlight improve the contrast carried out by an amplification of non-linear characteristic Blinking momentarily attracts attention A flying box leads attention Blinking momentarily attracts attention Motion elicits an orienting response

 very clear image: amplification of logarithmic characteristic  The dynamic range of an image can be compressed by replacing each pixel value with its logarithm.  Enhance the low intensity pixel values, but compress high intensity values into a relatively small pixel range. if an image contains some important high intensity information, applying the logarithmic operator might lead to loss of information. Logarithmic ImageOriginal Image

Log Transformation Large range – 0 to 1.5*10 6 Brightest pixels dominate Log transformation reduces the range to 0 to 6.2

 dark images: amplification of exponential characteristic  Like the logarithmic transform, they are used to change the dynamic range of an image.  In contrast to the logarithmic operator, they enhance high intensity pixel values.  Basis number is used depends on the desired degree of compression of the dynamic range.

the dual of the logarithmic transform The basis for the exponential operator should be the same as it was for the logarithmic transform. Applying exponential operator Applying logarithmic operator

Applying an exponential transform with the base Original ImageApplying an exponential transform with the base 1.01

 image containing areas with light and dark tones vary continuously the exponential-linear- logarithmic characteristic of the amplifier

2. Filtering and Background Extraction 1. Filtering image may contain a high proportion of noise filtering carried out  in the spatial domains: Each pixel is obtained from a weighted averaging with the values for luminosity of the pixels in its neighborhood  in the frequency domains: Fourier transform operator  in the temporal domain from a certain number of images: obtaining an intensity value for each point from the date for the corresponding points in previous images

 Spatial filter: Pixel gray value = a weighted averaging of its neighbors Example: a 3 x 3 operator: ABC DEF GHI

Functional diagram of a processor which carries out a spatial domains filter of an image making a serial/parallel conversion of the n  n pixels considered and using a weighter

Low pass filters Moving average of time series smoothes Average (up/down, left/right) smoothes out sudden changes in pixel values removes noise introduces blurring Classical 3x3 template Removes high frequency components Better filter, weights centre pixel more

Example of Low Pass Original Gaussian, sigma=3.0

Example of High Pass Laplacian Filter - 2nd derivative

More e.g. ’ s Horizontal SobelVertical Sobel 1st derivative

Temporal filter: frequency domain For random noise in continuous image Recursive filters: Non-recursive filters:

2. Background Extraction image with little contrast, difficultly distinguishing between the objects and the background  making a periodical subtraction between consecutive image.  result is of a sufficiently low value for all the pixel – background, stored it in memory  result stops giving null values in a high proportion of pixels – object appears  in real time, subtracting the image of object obtained from the digitizer from the image contained in memory.

Eliminating a large volume of non-relevant data.Help to distinguishing the objects from the background Periodical subtraction between consecutive image. Background region: Object appear region:  eliminate, black  Date remain in the memory

Functional diagram of a recursive algorithm to generate a reference image

Background Extraction +

3. Obtain Contour Elements Contour of object: -- Points separating the regions -- Important information -- relevant to their identification Advantages of getting contour: -- considerable reduction in the volume of data -- relative stability under fluctuation of light

The contours are composed of the points separating the different regions in an image, a sharp variation in the grey level appears in these points. Del operator The partial derivation of I(x,y) with respect to x P(x,y) contour point Operator G(x,y)= f(H x, H y ) – matrix type

characteristics to evaluate the degree of effectiveness of a contour extractor:  Sensitivity  Continuity of the contours obtained  Degree of contour thinning  Complexity-Calculation time

Edge coding & image compression Edge detection Edge decoding. Is it possible?

TV inpainting: applications in edge-based image decoding edge tube T No extra data are needed. Just inpaint!

edge tube T No extra data are needed. Just inpaint! “ Paulina, now you are ready to turn around …”

4. Contour Thinning and Feature Extraction 1. Contour Thinning  eliminate the redundant pixels from the gradient image until contours of single pixel thickness are obtained  applies an algorithm of elimination of the redundant points of a contour a horizontal sweep is made and the pixels selected to belong to the thinned contour are marked a vertical sweep is made and the redundant pixels are eliminated

Thinning of the contour

2. Feature Extraction  geometrical or local features of the objects may be used for the formation of these vectors.  measurement of the area : adding the number of pixels defining the object  measurement of the perimeter: weighted sum of the number of pixels of the contour  analysis of local features, such as vertices, holes or marks.

Processor for detecting holes in real time

Morphing

Sharpening beforeafter

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