Module/Course Title: Digital Image Processing
Module course code KOMS120607	Student Workload 119 hours	Credits 3 / 4.5 ETCS	Semester 6	Frequency Even Semester	Duration 16
1	Type of course Field of Study Courses	Contact hours 40 hours of face-to-face (theoretical) class activity	Independent Study 48 hours of independent activity 48 hours of structured activities	Class Size 30
2	Prerequisites for participation (if applicable) -
3	Learning Outcomes Students can demonstrate systematic thinking in analyzing and designing intelligent system solutions Students can apply effective methods in developing intelligent systems Students can create and evaluate intelligent systems Students can explain the concept of digital images Students can explain the concept of spatial and frequency domain Students can construct and present digital images Students can implement digital image quality improvement Students can examine and process colored images Students can demonstrate morphological image processing, image segmentation, and image representation
4	Subject aims/Content This course provides knowledge of commonly used methods for interpreting digital images for image enhancement and restoration and performing operations on images. Digital image processing (DIP) involves modifying digital images using a computer. It is a branch of signals and systems with a strong emphasis on images. The primary goal of DIP is to create a computer system that can process images. A digital image is used as the system's input, which is processed by the system utilizing effective algorithms to produce an image. DIP also emphasizes comprehending how human eyesight functions. How does the brain decipher the images that the human eye sees so many different things? The materials discussed in this course include basic concepts of digital imagery, intensity transformation, spatial domain filters, frequency domain filters, image restoration and reconstruction, color processing, morphing, image segmentation, and image representation and description. Study Material Digital image. Image file formats (PNM, BMP, TIFF, GIF, PNG, JFIF, JP2) Blur, sharpness, contrast, saturation, resolution, aliasing, noise. Image enhancement. Image analysis, image reproduction, image reconstruction, image compression. Footage, sensor size, and image resolution. digital image generation model sampling on sensor and noise point processing dan spatial processing point processing dan spasial processing  Region growing, histogram-based segmentation, otsu method Filtering with Laplacian Kernel, filtering with Seobel and Prewitt kernels Materials until the 7th meeting Color: physics, color, systems, human visual, and color perception, color theory, stimuli, color fields. color feature: Color histogram and color moment Texture feature: GLCM Shape feature: chain code An overview of the classification method on the image, Euclidean Distance, Minimum Distance  k-means clustering  Support Vector Machine (SVM) - Image Quality Metrics: Subjective and objective ratings Computer Vision Performance Metrics: recall precision and F-measure
5	Teaching methods Lectures, questions and answers, and discussions.
6	Assesment Methods Attendance and Participation
7	This module/course is used in the following study programme/s as well Computer Science Study Programme
8	Responsibility for module/course Ketut Agus Seputra, S.ST.,M.T. NIDN : 0015089006
9	Other Information Gonzalez, R.C., Woods, R.E., 2007, "Digital Image Processing," 3rd edition, Pearson. Pratt, W.K., 2007, "Digital Image Processing," Fourth Edition, John Wiley & Sons. Sonka, M., Hlavac, V.,, Boyle, R. (1998). Image Processing: Analysis and Machine Vision. CL-Engineering. ISBN: 053495393X McAndrew, A. (2004). An introduction to digital image processing with MATLAB. pub-BROOKS-COLE:adr: BrooksslashCole. ISBN: 0-534-40011-6 Computer Vision and Image Processing, Adrian Low, Second Edition, B.S.Publications Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital Image Processing using MATLAB. Pearson Education.  Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods: Website: http://www.imageprocessingplace.com/  OpenCV Tutorials: Website: https://docs.opencv.org/master/d9/df8/tutorial_root.html Digital Image Processing Tutorial by TutorialsPoint: Website: https://www.tutorialspoint.com/dip/ Purwanta, I.P., Dewi, N.P. and Adi, C.K. (2020) ‘Backpropagation neural network for book classification using the image cover’, International Journal of Applied Sciences and Smart Technologies, 2(2), pp. 89–106. doi:10.24071/ijasst.v2i2.2653. Students are also expected to look for articles from good journals to learn about the latest developments in techniques and algorithms in digital image processing.