﻿ 视觉和图像考试助攻 CS5187代写 - CS代写, 考试助攻

# 视觉和图像考试助攻 CS5187代写

2022-10-20 11:48 星期四 所属： CS代写 浏览：48

## Time allowed : Two hours

1.This paper consists of 16 questions.

This is an open-book examination.

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### Section A (short questions)   视觉和图像考试助攻

Five marks for each question (5%)

Q1: Please describe two image processing methods that require the image interpolation.

Q2: Please explain how SIFT feature could achieve rotation invariance.

Q3: Assume we do not change the physical size of the virtual image plane, and we reduce the number of pixels to one fourth (1/4) in acquisition of the photos. In particular, the number of pixels in both horizontal and vertical directions are reduced by half. Please explain whether the camera intrinsic parameters that project a scene point in the camera coordinate system to a pixel in the image plane will be changed. Please give specific explanations.

Q5: Please show that the combination of scale and translation is still affine transformation.

Q7: Please explain how we can enhance the sharpness of an image.

Q8: What is the minimal number of pixels to measure the optical flow using the Lucas-Kanade equation.

Q9: Assume that in homogeneous coordinate (x, y, w), when w = 0, the point is infinitely far. Show that the affine transformation of an infinity point is still at infinity.

Q10: Regarding the structure from motion, assuming that the input includes 30 images, and 2000 scene points need to be reconstructed, please describe the unknown variables that need to be solved for structure from motion.

Q11: What is the output with the following inputs and functions:

1) ReLU with the input as 5;

2) Average pooling with inputs as 4, 6, 6, 8.

Q12: The following question is regarding the in-network upsampling. Given the max-pooling as follows, please use the corresponding position in the pooling layer to fill in the results of the table with max unpooling. After identifying the positions for unpooling, other positions are padding with zeros.

The max pooling process is as follows:

### Section B (long questions)   视觉和图像考试助攻

#### Q13:

(a) Please explain why the stereo image rectification is performed for the inference of the depth map. (2 marks)

(b) After image rectification, where are the epipoles? (2 marks)

#### Q14:

(a) Please compute the LBP for the central pixel p given the pixel value and 3×3 neighbouring pixel values. In particular, please create the 8-bit number b1b2b3b4b5b6b7b8, where bi = 0 if neighbouring i has value less than or equal to p’s value and 1 otherwise. The upper left has the index 1 (first), and the order is in clock-wise. (3 marks)

 120 50 60 100 100(p) 120 60 120 60

(b) When the neighbouring pixels are rotated as follows, please create the 8-bit number LBP again, following the rule in (a). (2 marks)

 100 120 50 60 100(p) 60 120 60 120

(c) The current LBP is not robust to rotation. Please design an algorithm based upon LBP, making the LBP robust to rotation variations. Please explain in detail how the algorithm works. (5 marks)

#### Q15:   视觉和图像考试助攻

(a) Please elaborate how the Lucas-Kanada improves the performance of the optical flow estimation when the small motion assumption is violated. (3 marks)

(b) Let S(x,y,t) , denote an image sequence, and assume that there is an affine change in intensities due to the illumination variations for neighbouring frames,

S(x+u,y+v,t+1) = α·S(x,y,t) +β

Herein, (u,v) is the motion vector. α and β, which depend on the location (x,y) are photometric parameters that change the pixel intensity.

First, based upon the first-order Taylor expansion of S(x+u,y+v,t+1), please show the linear system of the equations for the estimation of the unknown parameters (u,v,α,β). (5 marks)

Second, please indicate the minimal window size to estimate these parameters, based upon the assumption of spatial coherence. (2 marks)

#### Q16:    视觉和图像考试助攻

(a) In the alignment, assume that the transformation is only translation. Given three matches in I and I′,

(x1,y1) = (5, 10), (x’1,y’1) = (7, 12)

(x2,y2) = (5, 10), (x’2,y’2) = (8, 23)

(x3,y3) = (5, 10), (x’3,y’3) = (32, 26)

Please provide the translation vector (from I to I′) that will lead to the least square solution on the sum of square residuals. Herein, the residual is defined as the difference between the estimated position based on the derived translation vector and the true position for (x’1,y’1), (x’2,y’2), and (x’3,y’3). (4 marks)

(b) Assuming that the transformation is affine transformation, and we need at least 3 matches to obtain the parameters in affine transformation. For two images, we find that there are 1000 matches, within which 50% points are inliers. Assuming the number of iterations is 2 in RANSAC, please estimate the probability that we could obtain the correct transformation. Show the detailed steps. (6 marks)