计算机代写|EECS442 Computer Vision

MY-ASSIGNMENTEXPERT™可以为您提供umich.edu EECS442 Computer Vision计算机视觉课程的代写代考和辅导服务！

EECS442课程简介

EECS 442 is an advanced undergraduate-level computer vision class. Class topics include lowlevel vision, object recognition, motion, 3D reconstruction, basic signal processing, and deep learning. We’ll also touch on very recent advances, including image synthesis, self-supervised learning, and embodied perception.

Lectures:
Lectures will take place Monday and Wednesday, 3:00 – 4:30pm. Attendance will not be required, but it is highly encouraged. There are multiple ways to participate:

• In person in Dow 1013. Due to space limits, this is only available to students who have registered for the in-person version of the class.
• Live-streamed on Zoom. Please see here for the link. Please do not share it, so that we can avoid Zoom bombing.
• We’ll post lecture recordings online here.

Prerequisites 

Prerequisites:

This course puts a strong emphasis on mathematical methods. We’ll cover a wide range of techniques in a short amount of time. Background in linear algebra is required. For a refresher, please see here. This material should mostly look familiar to you.

This class will require a significant amount of programming. All programming will be completed in Python, using numerical libraries such as numpy, scipy, and PyTorch. The problem sets will be completed using Jupyter notebooks, generally using Google Colab. In some assignments, we’ll give you starter code; in others, we’ll ask you to write a large amount of code from scratch.

EECS442 Computer Vision HELP（EXAM HELP， ONLINE TUTOR）

Here’s my implementation of the convolve function in filters.py:

def convolve(img, kernel):
“””
Convolve an image with a kernel.
Use zero-padding to handle borders.
“””
kh, kw = kernel.shape
ph, pw = kh // 2, kw // 2 # padding height and width
padded_img = np.pad(img, ((ph, ph), (pw, pw)), mode=’constant’)

output = np.zeros_like(img)
for i in range(img.shape[0]):
    for j in range(img.shape[1]):
        output[i, j] = np.sum(padded_img[i:i+kh, j:j+kw] * np.flip(kernel))

return output

To ensure consistency with the expected output, I flipped the kernel as soon as I received it, since the problem prompt asks for convolution rather than cross-correlation. I used zero-padding with mode='constant' to handle the borders. I then looped over each pixel in the output image and computed the sum of the elementwise product between the kernel and the corresponding input patch, which is a 3×3 neighborhood of the input image centered at the pixel. Finally, I assigned the result to the corresponding pixel in the output image.

Here’s the code to apply a Gaussian filter with a $3 \times 3$ kernel and a standard deviation of $\sigma=0.572$ to the grace_hopper.png image:

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
from filters import gaussian_kernel

load image in grayscale

img = ndimage.imread(‘grace_hopper.png’, mode=’L’)

define Gaussian kernel

sigma = 0.572
kernel = gaussian_kernel(3, sigma)

apply Gaussian filter

output = ndimage.convolve(img, kernel, mode=’constant’)

plot input and output images

fig, axs = plt.subplots(1, 2)
axs0.imshow(img, cmap=’gray’)
axs0.set_title(‘Input Image’)
axs1.imshow(output, cmap=’gray’)
axs1.set_title(‘Gaussian Filtered Image’)
plt.show()

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