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# 机器人视觉代写 Robot Vision代写 考试代写 计算机视觉代写

468

## A29401 Robot Vision

No Calculator permitted in this examination

Time allowed: 1 hr 30 min

### 1.

(a) Describe a pinhole camera.  [3%]

(b) Describe the role of lenses in cameras.  [3%]

(c) What is a chromatic aberration? What causes it?  [2%]

(d) What is vignetting? What does it cause?  [2%]

### 2. 机器人视觉代写

(a) Describe “kernel separability” and its importance for image filtering. [5%]

(b) For the kernel given below determine if it is separable. Justify your answer. [5%]

-1 -2 -1

2 4 2

3 6 3

### 3.

(a) In pseudocode, describe the Hough transform for detecting circles with fixed radii.  [7%]

(b) Demonstrate the algorithm on a pair of points A=(3,4) and B=(5,4) for the values of radii r=1 and r=2. Map the two points into the parametric space. What can we infer about the points A and B from the analysis of the parametric space?  [8%]

### 4. 机器人视觉代写

(a) In pseudocode, describe RANSAC for a general case of searching for the best model in the set of data.  [10%]

(b) Explain the role of RANSAC parameters.  [5%]

### 5.

(a) Describe the process of detecting interest points with a DoG (Difference of Gaussians) pyramid (as proposed by Lowe).  [10%]

(b) How do we achieve in-image-plane rotational invariance of a SIFT descriptor?  [5%]

### 6. 机器人视觉代写

(a) Given the sketch of a two-camera system (Ol, Or denote the left centre and the right centre of the cameras, respectively), and a 3D point P, draw and explain the terms epipols, epipolar plane, and epipolar lines that correspond to the point.  [10%]

(b) A mobile robot is equipped with two cameras (assume a pinhole model). The optical axes of the cameras are parallel and lie on a horizontal plane. The baseline between the cameras is 12 cm. The disparity of a point T in space is 16 mm. When the robot moves 20 cm in the viewing direction, the disparity increases to 30 mm. Calculate the focal length. [10%]

### 7.

(a) Describe the steps involved in object learning and recognition using PCA. [10%]

(b) Describe object learning and categorization using “bag of features”. [5%]

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