课程名称︰电脑视觉 课程性质︰资工系选修 课程教师：傅楸善 开课学院：电机资讯学院 开课系所︰资讯工程学系 考试日期（年月日）︰2024/10/22 考试时限（分钟）：180 分钟 试题 : 1. (62%) Please define the following terms and explain the content, purpose, and application of each term and give an illustrative example if possible. If possi- ble, define the term in mathematical equation. For example: thresholding: an image point operation that produces a binary image from a gray scale image. A binary-1 is produced on the output image whenever a pixel value on the input image is above a specified minimum threshold level. A binary-0 is produced otherwise. Alternatively, thresholding can produce a binary-1 on the output image whenever a pixel value on the input image is below a specified ma- ximum threshold level. A binary-0 is produced otherwise. (1) template matching (2) feature extraction (3) region centroid (4) region area (5) image segmentation (6) edge linking (7) corner finding (8) labeling (9) noise suppression (10) background normalization (11) 2D discrete Euclidean space (12) 4-connectivity (13) border pixel (14) region perimeter (15) octagon (16) GLCM: Gray Level Co-occurrence Matrix (17) image contrast (18) Bayes decision rule (19) economic gain matrix (20) statistical pattern recognition (21) conditional probability (22) fair game assumption (23) reserving judgment (24) maximin decision rule (25) dilation (26) erosion (27) opening (28) closing (29) mathematical morphology (30) operator extensive (31) idempotent 2. (8%) When binarizing image, we should find the best threshold of this image. Therefore, please list two ways (statistical methods) to find the best threshold of the image and explain advantages and disadvantages in detail. 3. (6%) In the Classical Connected Components Labeling Algorithm, we face a big problem - global equivalence table may be too large for memory, so what kind of methods can solve this problem? Please explain differences, advantages, and dis- advantages. 4. (6%) Please describe the method, steps, and results of YouAnamoly: Unified Anamoly Detection for Wafer Defect Inspection. 5. (6%) Please describe the method, steps, and results of Fast In-Bed Human Pose Estimation Using RGB-D (Red, Green, Blue -- Depth) Images. 6. (6%) Please describe the method, steps, and results of YuLPR: Moving Vehicle License Plate Recognition. 7. (6%) Please describe the method, steps, and results of Text to Image Genera- tion with Stable Diffusion. --

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