IMAGE PROCESSING
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Image processing applications for baking process monitoring Computer vision and image processing offer the development and employment of non-invasive, contactless, non-destructive, and fast measurement methods with a wide range of applications within the baking industry. These include both the visualization (qualitative measurement) and the exploration of shape and
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As a subcategory of signal processing, image processing is used in satellite imaging, medical imaging, and industrial imaging for process and even product assessment. Image processing uses camera technologies to capture two- or three-dimensional data, followed by data transfer to a processing unit to extract product or process information. The usual captured light range is the visible (VIS) light range (400–800nm) because it is captured by both CMOS (complementary metal-oxidesemiconductor) and CCD (charge-coupled device) cameras, which can also include the initial nearinfrared (NIR) range (800–900nm). Silicon sensors can also detect the extended VIS/NIR range. The NIR range up to 1700nm is captured using InGaAs sensors, e.g., with shortwave infrared (SWIR) cameras. For the mid-infrared range (MIR, 2.5–25.0μm), used to capture thermograms, microbolometers are utilized. Microbolometers can also be used to capture ultraviolet and Xradiation. Other camera types for specific parts of the electromagnetic spectrum are available, and these are constantly being developed and adapted to match changing requirements. Image data capture occurs mainly in line scan mode, in which a dynamic scene is captured line
by line, or in area scan mode, where the whole scene is captured in one shot. For image processing, these single recorded lines are sequentially stitched together for image reconstruction. For example, with conveyer belts, typically line scan cameras are used to capture continuous webs of raw material or products. The obtained image data are available as twodimensional matrices with intensity values. For monochrome cameras, the form is n×m×1, and for RGB cameras, the form is n×m×3. The light or color intensity forms the third dimension for each pixel inside the layers in the n×m matrix. InGaAs-cameras and microbolometers capture n×m×1 monochrome data. By using filters for the separation into individual wavelengths, so-called spectral observations can be carried out. In the resulting spectral cube, the captured data is in the form n×m× ‘number of observed wavelengths/ regions’. The number of captured image data (sampling frequency) is specified by sampling criteria and indicated by frames per second (fps). Image data processing can be applied at different computational levels. First is the description of the image using statistical tools, e.g., the mean, minimum and maximum value of the color, contrast, or histogram analysis. The image data
I M AG E P RO C E SS I N G A P P L I C AT I O N S F O R BA K I N G P RO C E SS MO N I TO R I N G
surface properties (quantitative measurement).
