1341 Probability (機率)
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Course Time: Mon 09:10-12:00
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Classroom: AT338
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Course Outlines:
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1. Experiments, Models and Probabilities;
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2. Sequential Experiments;
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3. Discrete Random Variables;
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4. Continuous Random Variables.
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5. Multiple Random Variables;
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6. Probability Models of Derived Random Variables;
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7. Conditional Probability Models;
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8. Random Vectors;
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9. Sums of Random Variables;
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10. The Sample Mean;
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11. Hypothesis Testing;
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12. Estimation of a Random Variable;
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13. Stochastic Processes.
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Textbook: Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers ", the 3rd Edition, by Roy D. Yates and David J. Goodman (John Wiley & Sons), 2015.
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Reference Books:
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Introduction to Statistical Pattern Recognition, by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.
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Introduction to Probability and Statistics: for Engineering and the Computing Sciences, by J. Susan Milton, Jesse C. Arnold, Liu Kwong Ip, the McGraw Hill Companies.
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R in ACTION: Data analysis and graphics with R, by Kabacoff, Robert I., Manning Publications, 2015.
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Lecture Notes: Probability0
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Grade:
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News:
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HW1: R Programming for Quiz 1.6.
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HW2: Problems: 1.1.2, 1.3.1 and 1.5.7.
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HW3: R Programming for Quiz 2.4.
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HW4: Problems: 2.1.10, 2.2.10 and 2.2.12.
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HW5: R Programming for Example 3.34.
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HW6: Problems: 3.2.10, 3.3.13 and 3.5.10.
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期中考時間：11/3 0930-1200 ，範圍：chapter1 ~ chapter3
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6653&7766 Digital Image Processing (影像處理)
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Course Time: Tue 13:10-16:00 / Mon 18:20-21:00
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Classroom: AT336 / AT338
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Course Outlines: This course covers fundamental concepts and methods in digital image processing and their applications. The course is outlined as:
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1 Introduction
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2 Digital Image Fundamentals
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3 Intensity Transformations and Spatial Filtering
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4 Filtering in the Frequency Domain
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5 Image Restoration and Reconstruction
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6 Wavelet and Other Image Transforms
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7 Color Image Processing
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8 Morphological Image Processing
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9 Image Segmentation
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10 Deep Learning
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11 Image Classification and Object Detection with Convolutional Neural Networks.
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Textbook: "Digital Image Processing", by R. C. Gonzalez and R. E. Woods, 4th Edition, Pearson (開發), 2017.
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Reference Books: "
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Digital Image Processing Using MATLAB", 2nd Edition, by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins,
McGraw-Hill, 2009. -
"Artificial Intelligence" by Leonardo Araujo dos Santos. 2018.
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"Hands-On Computer Vision with TensorFlow 2", by Benjamin Planche and Eliot Andres, Packt Publishing, 2019.
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Lecture Notes: Chapter0, 關於PPT授課著作權, Chapter1, Chapter2, Chapter3, Chapter5, Chapter6, Chapter9, Chapter10, Chapter12
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Grade:
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News:
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HW1: Please write a programming assignment that generates the artifacts caused by insufficient sampling and quantization during digital image creation—specifically Moiré patterns and false contours. You may use either grayscale or true-color images.
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10/7因到台北參加2025國家新創獎複審會議，課程暫停一次。
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HW2: Please write a programming assignment to implement logical AND operation and union operation between two images."
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HW3: Please write a programming assignment to implement perspective transformation using an 8-parameter spatial transformation model.
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作業繳交方式：內容為主要演算法與程式片斷，包含測試資料與結果，以及簡短的討論或結論等。請把書面報告WORD電子檔，程式Source code及相關測試資料先以Winzip或WinRAR壓縮，並以學號為其檔名，上傳到以下FTP網站：主機IP: 140.120.182.208，Port: 2025，碩博班：使用者名稱：DIP2025_student，密碼：DIP2025；碩專班：使用者名稱：DIP2025_student_1ABC，密碼：DIP2025_1ABC
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期末報告，每組報告一篇近五年SCIE期刊論文，報告時間為15分鐘(含QA)，分組方式上課時說明。
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HW4: Please write a programming assignment to implement image sharpening techniques based on the Laplacian operator and gradient-based masks. The gradient and the convolution operation should be implemented manually, including appropriate zero-padding. The use of built-in convolution APIs is not permitted.
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