COMPUTER VISION (CV) [ELECTIVE], Semester 8, B.E. Computer Science (CS), May 2013.

COMPUTER VISION (CV) [ELECTIVE], Semester 8,

B.E. Computer Science (CS), May 2013.

Con. 8527-13

GS-3577

(3 Hours)

[Total Mark: 100]

N.B. (1) Question No 1 is compulsory.

(2) Attempt any four questions out of remaining six questions.

(3) Assume suitable data whenever necessary and justify the same.

(4) Figures to the right indicate full marks.

1. (a) Give all the steps involved in recognition methodology and briefly explain each. ---- (10 Marks)

(b) Explain ‘opening’ and ‘closing’ with example. ---- (10 Marks)

2. (a) Explain Hough transform with example. Mention all its merits and demerits. ---- (10 Marks)

(b) What is knowledge based vision? Explain different of knowledge representation used in computer vision. ---- (10 Marks)

3. (a) Explain Border tracking algorithm with suitable example. ---- (10 Marks)

(b) Explain inverse perspective projection. ---- (10 Marks)

4. (a) Explain intensity matching of 1 dimensional signals. ---- (10 Marks)

(b) Explain back – tracking algorithm with suitable example. ---- (10 Marks)

5. (a) Apply ‘iterative’ and ‘classical’ connected component labelling algorithms on following image: ---- (10 Marks)

0	0	0	0	0	0	0	1	1	0
0	1	1	0	0	0	1	1	1	0
0	1	1	1	0	1	1	1	1	0
0	0	1	1	0	0	0	1	1	0

(b) Explain boundary descriptors. --- (10 Marks)

6. (a) Explain ‘thinning’ and ‘thickening’ with the help of examples. ---- (10 Marks)

(b) Explain mixed spatial gray – level moments. ---- (10 Marks)

7. Write short note on: ---

(a) External points. ---- (5 Marks)

(b) Principal component analysis. --- (5 Marks)

(c) View class matching. ---- (5 Marks)

(d) Global V/S local features. --- (5 Marks)