2D Convolution Explained: Fundamental Operation in Computer Vision

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Welcome to '2D Convolution in Computer Vision'! This computer vision tutorial aims to demystify one of the most crucial and foundational operations in the field of computer vision - the 2D convolution.

In the realm of computer vision, convolution is a mathematical operation that is used to process and transform images, making it a key component in image filtering and detection tasks. This operation revolves around the concept of a filter or kernel, typically a 3x3 or 5x5 matrix, which slides over the input image and manipulates it for further processing.

In this video, we will delve into an illustrative example where a 6x6 input is convolved with a 3x3 kernel. We'll break down the process step-by-step, making it easy for beginners to follow along and gain a solid understanding of how 2D convolution works.

Whether you're a beginner eager to learn the basics, an intermediate looking to solidify your knowledge, or an advanced learner interested in revisiting the fundamentals, this video will serve as a comprehensive guide to understanding 2D convolution in computer vision.

⭐️ Time Stamps:⭐️
00:00-00:10: Introduction
00:10-02:30: Convolution Operation
02:30-03:11: Experimenting with Kernels
03:11-03:54: CNNs
03:54-04:49: Example
04:49-05:06: Outro

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Комментарии
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Great video. I have a small nitpick: animation at 0:05 is showing a cross correlation not convolution. For convolution, you flip the kernel over each axis before performing the dot product

DillanWilson-qb
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Should you not apply the filter mask values mirrored in the dot product? Unmirrored the operation is a correlation. Especially if the filter is an asymmetric filter such as the sobel is.

HuCEcpvrLab
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Hi I have a question, Is it right to flip the kernel and then do the convolution? Thank you

matteo
Автор

I have one confusion, I have been searching for this but couldn't find any concrete answer. Is it correct to have the x-derivative and y-derivatives having dimensions smaller than image matrix? shouldn't these two have the same dimensions as well. Any one could guide me please.

usmankhawar