| 課程大綱 Syllabus |
學生學習目標
Learning Objectives |
單元學習活動
Learning Activities |
學習成效評量
Evaluation |
備註
Notes |
序
No. | 單元主題
Unit topic |
內容綱要
Content summary |
| 1 | Introduction |
Computer Vision and Applications |
Understand why you need to learn computer vision. |
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| 2 | Camera Model and Image Representation |
1. 2D image from projection of a 3D scene
2. Imaging formation
3. Pin-hole camera
4. Video cameras
5. Lab: save images by OpenCV |
Fundamental image processing |
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| 3 | Filtering and Enhancing Images |
1. Gray level transformations
2. Histogram processing
3. Enhancement using arithmetic operations
4. Spatial filtering
5. Lab: image enhancement |
Learn how to call OpenCV for image preprocessing |
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| 4 | Binary Image Processing |
1. Pixels and neighborhood
2. Applying masks to images
3. Counting the object in an image
4. Connected components labeling
5. Binary image morphology
6. Region properties
7. Region adjacency graphs
8. Thresholding gray-scale images
9. Lab: Connected Component |
Feature extraction |
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| 5 | Case study - OCR |
1. Handwritten OCR systems
2. CIL - Greek Handwritten Character Database
3. Proposed OCR Methodology
4. Experimental Results
5. Experiments on Historical Documents
6. Lab: OCR |
Learn how to write code for OCR |
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| 6 | Image Segmentation |
1. Clustering
2. K-means
3. Region Growing
4. Lab: Segmentation |
Ideally, partition an image into regions corresponding to real world objects |
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| 7 | Case study - Multi-Focus Images Fusion |
1. Region-based image fusion methods
2. Multifocus images fusion by average
3. Intermediate fused image is segmented
4. Source images are segmented according to the segmenting result
5. Segmented regions of the source images are fused
6. Lab: Multi-Focus Images Fusion |
Multifocus image fusion using region segmentation and spatial frequency |
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| 8 | Case study –High Dynamic Range Image |
1. What is HDR?
2. Display HDR image by different exposure level
3. Tone mapping
4. Lab: HDRI Fusion |
Learn how to do a high dynamic range image. |
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| 9 | Mid-Term Exam |
Papaer Exam. |
Pass the exam. |
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| 10 | Case study - Motion from 2D Image Sequences |
1. 2-D motion vs. optical flow
2. Motion representation
3. Motion estimation criterion
4. Optimization methods
5. Gradient descent methods
6. Lab: Optical Flow |
Motion estimation |
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| 11 | Intelligent Video Surveillance |
1. Industrial need and trend
2. Aplication scenario and technology development
3. ITRI developed technologies and industrial applications |
Understand industrial trend and requirement |
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| 12 | Case study - Behavior Analysis |
1. Motion segmentation
2. Morphological operation
3. Object classification
4. Tracking
5. Lab: Tracking |
Learn how to do video surveillance |
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| 13 | Object Matching - SIFT |
1. Scale Space and Difference of Gaussian
2. Key point Localization
3. Orientation Assignment
4. Descriptor Building
5. Application
6. Lab: Object Recognition |
Learn how to do object matching using SIFT |
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| 14 | Pattern Recognition Concept |
1. Common model for classification
2. Precision vs. recall
3. Feature vector representation
4. Implementing the classifier
5. Structural techniques
6. The confusion matrix
7. Decision trees
8. Matching by relations
9. Lab: Neural Network |
Understand techniques of pattern recognition |
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| 15 | Face Detection and Recognition |
1. Face feature
2. Integral image
3. Ada-boost
4. Face detection
5. Lab: Face Detection |
Learn how to write programs to detect human faces. |
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| 16 | Gesture Recognition |
1. Image processing methods
2. Image pyramid
3. human face detection
4. digital focusing
5. Adaptive skin color detection
6. motion history image
7. Lab: Hand Gesture Recognition |
Learn how to detect hand gesture |
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| 17 | Special Topic |
Select a research topic |
Final project-Search published papers and design a workable computer vision system |
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| 18 | Term project |
Paper exam. or give a final report (oral and a ppt file) |
Pass |
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