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Lecturer(s)
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Tunák Maroš, doc. Ing. Ph.D.
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Course content
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Lectures: 1. Introduction to image processing and analysis. Image sensing and acquisition, digital representation of images. Image sampling and quantization. Basic types of images. 2. Mathematical tools used in digital image processing. Basic relationships between pixels (neighborhood, connectivity, area, boundaries, distance measures). 3. Basic intensity transformation function. Histogram equalization. 4. Fundamentals of spatial filtering. Spatial correlation and convolution. Smoothing spatial linear filters. Smoothing spatial nelinear filters. 5. Sharpening spatial filters. Using the second derivative fo image sharpening - the Laplacian. Unsharp masking. Using the first order derivatives for image sharpeningg - the gradient. 6. Filtering in the frequency domain. 2D Fourier transform. Relationship between the filtration in frequency and spatial domain. Smoothing and sharpening in frequency domain. 7. Image segmentation. Thresolding. Segmentation - point, line and edge detection. Edge detectors. Hough transform. 8. Morphological image processing. Dilation. Erosion. Morphological opening and closing. Basic morphological operations for binary images. The hit or miss transformation. 9. Morphological algorithms (boundary extraction, hole filling, extraction of connected components, convex hull, thinning, thickening, skeletons, pruning). Basic morphological algorithms on gray level images. 10. Representation and description of objects or regions of interest in images (area, perimeter, centroid, orientation, equivalent diameter, eccentricity, bounding box, convex hull etc.) 11. Color image processing. Color models. Color transformation. Filtration of color images. Segmentation of color images. 12.-14. Using the tools of image processing in soving problems in textile and industrial engineering. Excercises: The exercises will be practiced lectured matter using software MatLab + Image Processing Toolbox, Computer Vision System Toolbox and ImageJ (freely available at https://imagej.nih.gov/ij/).
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Learning activities and teaching methods
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Lecture, Practicum
- Class attendance
- 56 hours per semester
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Learning outcomes
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The processing, analysis and evaluation of digital image data is today a very important in the field of industrial applications, thanks to its ability to perform fast non-invasive analysis and evaluation products and processes. In textile metrology, image processing has an irreplaceable role in understanding the content of images, knowing important features, and performing geometric and quantitative descriptions of objects of interest in the image. The aim of the course is to acquire basic knowledge and skills of students in the field of processing, analysis and evaluation of image data and their application in solving specific tasks in textile and industrial engineering.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral exam, Written exam
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Recommended literature
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GONZALEZ, R. C., WOODS, R. E. Digital Image Processing. 3rd Edition.. New Jersey: Prentice-Hall,, 2008. ISBN 978-0-13-168728-8.
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GONZALEZ, R. C., WOODS, R. E., EDDINS, S. L. Digital Image Processing using Matlab. 1st Edition.. New Jersey: Prentice-Hall, 2004. ISBN 0-13-008519-7.
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PETROU, M., SEVILLA, G. P. Image Processing, Dealing with Texture.. Chichester: John Wiley and Sons, 2006. ISBN 978-0-470-02628-1.
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SONKA, M., HLAVAC, V., BOYLE, R. Image Processing, Analysis, and Machine Vision.. Pacific Grove: Books/Cole Publishing Company, 1998. ISBN 0-534-95393-X.
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