查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

112-2 331831 影像及訊號辨識 1 3.0 3 ★ 陳紅章機電所
製科所
機械自動化外生專班
機電博所
機電外國學生專班 24 0 機電所、製科所、機械自動化專班和機電博(含外)合開,

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
112-2	331831	影像及訊號辨識	1	3.0	3	★	陳紅章	機電所製科所機械自動化外生專班機電博所機電外國學生專班	24	0	機電所、製科所、機械自動化專班和機電博(含外)合開,

教學大綱與進度：

教師姓名陳紅章　教師諮商時間(Office Hours)

Email hctran@ntut.edu.tw

最後更新時間 2023-12-17 13:46:35

課程大綱本課程介紹當前針對訊號及影像常用之辨識演算法，包括支持向量機、支持向量描述、主成分分析、非線性主成分分析、線性鑑別分析、自回歸模型、共同空間圖形濾波。 Topics include support vector machine (SVM), support vector data description (SVDD), principal component analysis (PCA), nonlinear PCA, linear discriminant analysis (LDA), autoregressive model, and common spatial

課程進度 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

評量方式與標準 Homework 30% Midterm 30% Final Projects 40%

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
"Image Processing, Analysis, and Machine Vision" by Milan Sonka, Vaclav Hlavac, and Roger Boyle

課程諮詢管道 If students would like to consult about the course, please contact me via mail email address: hctran@ntut.edu.tw

課程對應SDGs指標

備註

教師姓名	陳紅章　教師諮商時間(Office Hours)
Email	hctran@ntut.edu.tw
最後更新時間	2023-12-17 13:46:35
課程大綱	本課程介紹當前針對訊號及影像常用之辨識演算法，包括支持向量機、支持向量描述、主成分分析、非線性主成分分析、線性鑑別分析、自回歸模型、共同空間圖形濾波。 Topics include support vector machine (SVM), support vector data description (SVDD), principal component analysis (PCA), nonlinear PCA, linear discriminant analysis (LDA), autoregressive model, and common spatial
課程進度	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
評量方式與標準	Homework 30% Midterm 30% Final Projects 40%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 "Image Processing, Analysis, and Machine Vision" by Milan Sonka, Vaclav Hlavac, and Roger Boyle
課程諮詢管道	If students would like to consult about the course, please contact me via mail email address: hctran@ntut.edu.tw
課程對應SDGs指標
備註