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)

COMPUTER VISION (CV) [ELECTIVE], Semester 8, B.E. Computer Science (CS), May 2011.

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

B.E. Computer Science (CS), May 2011.

(3 Hours)

[Total Mark: 100]

1. (a) Define Vanishing point and Vanishing line. ----- (5 Marks)

(b) What is significance of shape no. in region analysis? ---- (5 Marks)

(c) What is zero crossing edge detectors. ----- (5 Marks)

(d) Explain Least square method of curve fitting algorithm. ---- (5 Marks)

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

(b) Explain Run length implementation of algorithm with suitable example. ---- (10 Marks)

3. (a) Consider elongated region whose external points are given by ----- (10 Marks)

(r1, c1) = (24, 137)
(r2, c2) = (24, 163)
(r3, c3) = (30, 181)
(r4, c4) = (32, 181)
(r5, c5) = (39, 155)
(r6, c6) = (39,145)
(r7, c7) = (32, 119)
(r8, c8) = (30, 119)

Determine M1, M2, M3, M4, ϕ1, ϕ2, ϕ3, ϕ4. If this region is considered to be rectangle, what would be Computed orientation, length and width of rectangle?

(b) Develop the algorithm for automatically calculating threshold value by minimizing within group variance. --- (10 Marks)

4. (a) Explain the following region growing algorithm with suitable example. ---- (10 Marks)

(i) Centroid linkage

(ii) Hybrid linkage

(iii) Single linkage

(b) What is signature segmentation? Obtain the horizontal, vertical and diagonal signature of the binary image segment shown below: ---- (10 Marks)

			1	1	1	1	1
		1	1	1
			1	1	1
				1	1	1				1	1
1										1	1
1	1									1	1
	1
			1	1	1	1
			1	1	1	1

5. (a) Explain border tracking algorithm with suitable example. ---- (10 Marks)

(b) Using Hough transform show that the points (0,1), (1,4) and (-1,-2) are collinear and hence find the equation of line. ---- (10 Marks)

6. (a) What is knowledge based vision? Explain different forms of knowledge representation used in Computer vision. ---- (10 Marks)

(b) What are control strategies? Discuss the two major form of control Hierarchical and Hierarchical. ---- (10 Marks)

7. (a) Explain Information Integration ----- (5 Marks)

(b) What is facet model recognition ------ (5 Marks)

(c) Explain view class matching. ----- (5 Marks)

(d) Explain Intensity matching for one dimensional signal. ---- (5 Marks)