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November 2, 1998 1 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

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Page 1: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 1

MIDTERM GRADE DISTRIBUTION

100 2

90-99 7

80-89 10

70-79 4

60-69 4

50-59 6

40-49 1

Page 2: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 2

DILATION AND EROSION

EULER # = # CONNECTED COMPONENTS - # HOLES

CONNECTEDCOMPONENTS HOLES

EROSION INCREASES/ DECREASES DECREASES

DILATION DECREASES DECREASES/ INCREASES

Page 3: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 3

FINAL EXAMTues. Dec. 8 (Tentative)

FINAL PROJECTDue Wed. Dec. 2

Page 4: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 4

FINAL PROJECT(Pick one)

• Finding Medial Axis skeletons

• Object Recognition

• Texture Segmentation

• Simulate Shape From Reflectance

• Relaxation Labeling

Page 5: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 5

CLASS LECTURE TOPICS

• Mon. Nov. 2: Texture

• Tues. Nov. 3: Photometric Stereo

• Wed. Nov. 4: Shape From Shading

• Mon. Nov. 9: Motion and Optic Flow

• Tues./Wed. Nov. 10, 11: Color Science

Page 6: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 6

TEXTURE

REGULAR

STATISTICAL ISOTROPIC

STATISTICAL ANISOTROPIC

Page 7: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 7

Color Edges

• Color Edges in 2 or more bands may always have non-zero first order variation in all image directions at an edge point.

• Edge strengths arise from the 2 eigenvalues of a 2x2 symmetric matrix.

• The corresponding eigenvectors are the respective edge directions.

• Color Canny Edges arise from the first order variation of the largest eigenvalue along the direction of the respective eigenvector

Page 8: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 8

TOPIC REVIEW

• WEEK 1-2– Geometric Models– Illumination models– Basic Reflectance models– Basic Image Processing Transformations

• Discrete Convolution

• Gamma Correction

Page 9: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 9

TOPIC REVIEW

• WEEK 3– Image File formats– Image compression– Edge Detection

• First Order Edge Detection

• Second Order Edge Detection

• Mexican Hat Filter

• Canny Edge Detection

Page 10: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 10

TOPIC REVIEW

• WEEK 4-5– Iterative algorithms on images– Template Matching– Hough transform– Generalized Hough Transform– Grey Level Histrograms– Mathematical Morphology

Page 11: November 2, 19981 MIDTERM GRADE DISTRIBUTION 100 2 90-99 7 80-89 10 70-79 4 60-69 4 50-59 6 40-49 1

November 2, 1998 11

TOPIC REVIEW

• WEEK 6-7– Image Formation

• Perspective Projection

• Orthographic Projection

– Thin Lens Optics– Very Basic Radiometry– Stereo Imaging

• 2-D View

• 3-D View … Epipolar Geometry