Editor’s Recommendation This article mainly introduces several works on Difference Convolution led by the University of Oulu and its applications in the fields of images and videos. Author丨Fisher Yuzi @ Zhihu Link丨https://zhuanlan.zhihu.com/p/392986663 Related works have been accepted by top journals and conferences such as TPAMI, TIP, CVPR’20, ICCV’21 (Oral), IJCAI’21, and have won two international … Read more
The Canny filter is undoubtedly the most famous and widely used edge detection filter. I will explain step-by-step how to implement the Canny filter for contour detection. Since the Canny filter is a multi-stage filter, it is rarely integrated into deep learning models. Therefore, I will describe the different parts while implementing it using PyTorch. … Read more
Click on the “Beginner Learning Vision” above, and select the “Starred” public account Essential Knowledge Delivered Immediately This article is reprinted from: New Machine Vision This article takes Dalsa Sherlock software as an example to explore the image processing methods for smooth and blurred images in visual inspection. 1. Observing the gray distribution to describe … Read more
Source: ZHUAN ZHI This article is approximately 1000 words long and takes about 5 minutes to read. This article presents the Edge-Aware Transformer (EAFormer) for more accurate text segmentation, especially at the text edges. Scene text segmentation aims to crop text from scene images, often used to assist generative models in editing or removing text. … Read more
Modern efficient convolutional neural networks (CNN) always use depthwise separable convolutions (DSC) and neural architecture search (NAS) to reduce the number of parameters and computational complexity. However, they overlook some inherent features of the network. Inspired by visual feature maps and N×N (N>1) convolution kernels, this paper introduces several guidelines to further improve parameter efficiency … Read more
Click the "Beginner's Visual Learning" above, select "Star" or "Top" Heavy content delivered to you first Researchers proposed the concept of CNN (Convolutional Neural Networks) while studying image processing algorithms. Traditional fully connected networks are a black box – they take all inputs and pass each value through a dense network, then to a hot … Read more