Homework 1 Computer Vision (Term 3 2022-23) The purpose of this homework is to write an image filtering function to apply on input images. Image filtering (or convolution) is a fundamental image processing tool to modify the image with some smoothing or sharpening affect. You will be writing your own function to implement image filtering from scratch. More specifically, you will implement filter( ) function should conform to the following: (1) support grayscale images, (2) support arbitrarily shaped filters where both dimensions are odd (e.g., 3 × 3 filters, 5 × 5 filters), (3) pad the input image with the same pixels as in the outer row and columns, and (4) return a filtered image which is the same resolution as the input image. You should read a color image and then convert it to grayscale. Then define different types of smoothing and sharpening filters such as box, sobel, etc. Before you apply the filter on the image matrix, apply padding operation on the image so that after filtering, the output filtered image resolution remains the same. (Please refer to the end the basic image processing notebook file that you used for first two labs to see how you can pad an image) Then you should use nested loops (two for loops for row and column) for filtering operation by matrix multiplication and addition (using image window and filter). Once filtering is completed, display the filtered image. Please use any image for experiment. Submission You should submit (using Blackboard link) the source file which includes the code (Jupiter notebook) and a report containing different filters and corresponding filtered images. Deadline: May 11th, Thursday, End of the day.

Here is information on image filtering in spatial and frequency domains.

Image filtering in the spatial domain involves applying a filter mask to an image in the time domain to obtain a filtered image. The filter mask or kernel is a small matrix used to modify the pixel values in the image. Common types of filters include the Box filter, Gaussian filter, and Sobel filter.

To apply image filtering in the spatial domain, one can follow the steps mentioned in the prompt, such as converting the image to grayscale, defining a filter, padding the image, and using nested loops to apply the filter.

Both spatial and frequency domain filtering can be used for various image processing tasks such as noise reduction, edge detection, and image enhancement.

Learn more about frequency domain at:

brainly.com/question/14680642

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