Now, there is one thing to keep in mind here. This process smoothens out the edges in an image as well.įigure 3. So, to blur an image, we average the neighboring pixel values using this low pass filter/kernel. In this regard, whenever there is an edge in an image, then there is a drastic change in the pixel value. Also, it is almost always likely that the neighboring pixels (pixels close to each other in the matrix) have similar values. And each pixel value gives the RGB color that defines an image. ![]() We know that an image consists of pixel values arranged in a matrix. ![]() To blur an image, we apply this kernel to the image that we have. Now, let’s suppose that we want to blur an image. This matrix can be used for blurring, sharpening, and even detecting edges in an image. What is a kernel and what is a convolution operation? Kernel in Image ProcessingĪ kernel, or convolution matrix, or mask is a matrix that consists of some numerical values. From this discussion, there may be two questions that arise in your mind. When applying the kernel over the image, we carry an operation called the convolution operation. This low-pass filter is also called a convolution matrix. While blurring an image, we apply a low pass filter or kernel over an image. Image Blurring using 2D Convolution Kernelįirst, we need to know what is a kernel and convolution operations in an image. While working with images in the computer vision field, we may need to blur images and videos sometimes. Starting from security to detection, there are many real-world applications. Different types of blurring techniques:Ĭomputer vision applications are increasing every day in almost all the fields we can think of.And the next tutorial is going to be edge detection using OpenCV and Python. After all, it is related to Computer Vision and Python. And this brought me to think about writing a tutorial on image and video blurring using OpenCV. ![]() A bit more research led to blurring and edge detection combined. While I tried that, I did not get the best results either. I did a bit of research and found out that edge detection might help. While working on the project, I found that training the neural network on the direct RGB images of sign language did not produce the best results. It is on my checklist to finish the project as soon as possible and get the tutorial out here. So, currently, I am working on a large project for ASL (American Sign Language) recognition using deep learning for computer vision. We will use different filters that are available in the OpenCV library to blur images, video streams, and webcam feeds. Image blurring in computer vision and machine learning is a very important concept. In this tutorial, you will learn image blurring and smoothing using OpenCV and Python.
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