Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356199 Image & Signal Recognition 1 3.0 3 ★ Hong-Chuong Tran GME
半導體學位學程
DME
機電外國學生專班 21 0 機電所.半導體碩班.機電博含外生班合開,限21人

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356199	Image & Signal Recognition	1	3.0	3	★	Hong-Chuong Tran	GME 半導體學位學程 DME 機電外國學生專班	21	0	機電所.半導體碩班.機電博含外生班合開,限21人

Course Plan：

Instructor Hong-Chuong Tran　Office Hours

Email hctran@ntut.edu.tw

Last Updated 2025-12-21 20:39:31

Course Objective 本課程介紹主題包括基本的影像處理算法，如直方圖均衡化；影像濾波與卷積；輪廓提取、霍夫變換，以及模式辨識與分類。 This course introduce basic image processing algorithm such as histogram equalization; Image Filtering and Convolution; Contour Extractions, Hough Transform , Pattern Recognition and Classification

Course Schedule Weeks 1-2: Introduction to Image and Signal Processing Overview of image and signal processing Basics of digital image representation Introduction to MATLAB or Python for image and signal processing Weeks 3-4: Image Enhancement Techniques Histogram equalization Spatial domain methods (smoothing, sharpening) Frequency domain methods (Fourier transform) Weeks 5-6: Image Filtering and Convolution Convolution operation Linear and nonlinear filtering Edge detection techniques Weeks 7-8: Image Transformations Image rotation, scaling, and translation Geometric transformations Introduction to wavelet transforms Weeks 9-10: Image Segmentation Thresholding methods Region-based segmentation Clustering algorithms for segmentation Weeks 11-12: Image Feature Extraction Feature extraction techniques Corner detection and Harris corner detector SIFT and SURF feature descriptors Weeks 13-14: Introduction to Signal Recognition Overview of signal recognition Signal processing techniques Feature extraction from signals Weeks 15-16: Pattern Recognition and Classification Introduction to pattern recognition Supervised and unsupervised learning Classification algorithms (e.g., SVM, k-NN) Weeks 17-18: Final Project and Review Application of image and signal recognition techniques in a final project Review of key concepts Preparation for final assessments

Evaluation and grading policy Homework 30% Midterm 30% Final Projects 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
"Image Processing, Analysis, and Machine Vision" by Milan Sonka, Vaclav Hlavac, and Roger Boyle

The access to curricular consultation If students would like to consult about the course, please contact me via mail email address: hctran@ntut.edu.tw

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG5：性別平等（Gender Equality）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG17：夥伴關係（Partnerships for the Goals）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）

Note
　

Instructor	Hong-Chuong Tran　Office Hours
Email	hctran@ntut.edu.tw
Last Updated	2025-12-21 20:39:31
Course Objective	本課程介紹主題包括基本的影像處理算法，如直方圖均衡化；影像濾波與卷積；輪廓提取、霍夫變換，以及模式辨識與分類。 This course introduce basic image processing algorithm such as histogram equalization; Image Filtering and Convolution; Contour Extractions, Hough Transform , Pattern Recognition and Classification
Course Schedule	Weeks 1-2: Introduction to Image and Signal Processing Overview of image and signal processing Basics of digital image representation Introduction to MATLAB or Python for image and signal processing Weeks 3-4: Image Enhancement Techniques Histogram equalization Spatial domain methods (smoothing, sharpening) Frequency domain methods (Fourier transform) Weeks 5-6: Image Filtering and Convolution Convolution operation Linear and nonlinear filtering Edge detection techniques Weeks 7-8: Image Transformations Image rotation, scaling, and translation Geometric transformations Introduction to wavelet transforms Weeks 9-10: Image Segmentation Thresholding methods Region-based segmentation Clustering algorithms for segmentation Weeks 11-12: Image Feature Extraction Feature extraction techniques Corner detection and Harris corner detector SIFT and SURF feature descriptors Weeks 13-14: Introduction to Signal Recognition Overview of signal recognition Signal processing techniques Feature extraction from signals Weeks 15-16: Pattern Recognition and Classification Introduction to pattern recognition Supervised and unsupervised learning Classification algorithms (e.g., SVM, k-NN) Weeks 17-18: Final Project and Review Application of image and signal recognition techniques in a final project Review of key concepts Preparation for final assessments
Evaluation and grading policy	Homework 30% Midterm 30% Final Projects 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes "Image Processing, Analysis, and Machine Vision" by Milan Sonka, Vaclav Hlavac, and Roger Boyle
The access to curricular consultation	If students would like to consult about the course, please contact me via mail email address: hctran@ntut.edu.tw
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG5：性別平等（Gender Equality） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG17：夥伴關係（Partnerships for the Goals）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
Note