Source: New Machine Vision Originally from: Chengmai Technology Abstract: This article mainly introduces the five major technologies in computer vision, which are image classification, object detection, object tracking, semantic segmentation, and instance segmentation. Each technology is given a basic concept and corresponding typical methods, making it simple and easy to read. Computer vision is one … Read more
Reprinted from: Jishi Platform As one of the representative algorithms of deep learning, Convolutional Neural Networks (CNN) have achieved the best results in fields such as computer vision. In 1998, Yann LeCun proposed LeNet-5, applying the BP algorithm to train the neural network structure, forming the prototype of contemporary CNNs. In 2012, during the ImageNet … Read more
In today’s digital age, Convolutional Neural Networks (CNNs), as a subset of Deep Learning (DL), are widely used in various computer vision tasks such as image classification, object detection, and image segmentation. Many types of CNNs have been designed to meet specific needs and requirements, including one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) CNNs, as … Read more
Click on the above“Beginner Learning Vision“, select to add “Star” or “Top“ Essential content delivered promptly In this article, we will learn how to classify images of any size without using computationally intensive sliding windows. By modifying the ResNet-18 CNN framework, we will change the required input size from 224×224 to any size. First, we … Read more
Click on the above“Beginner Learning Vision”, select to add“Starred” or “Top” Heavy content delivered first Introduction The so-called “plugin” is something that can add value, is easy to implement, and truly plug-and-play. The “plugins” listed in this article can enhance the deformation capabilities of CNNs like translation, rotation, scale, etc., or multi-scale feature extraction, and … Read more
Click the above “Beginner Learning Vision“, choose to add “Starred” or “Pinned“ Heavyweight content delivered at the first time Convolutional Neural Networks (CNNs) have achieved unprecedented success in the field of computer vision, but we currently do not have a comprehensive understanding of the reasons for their remarkable effectiveness. In March 2018, Isma Hadji and … Read more
Click on the above “Beginner’s Visual Learning” and choose to add a Star or “Pin” Heavyweight content delivered at the first time Although this article does not propose new methods, it greatly helps us understand how CNN learns some appearance cues to address visual problems requiring geometric models. First, we know that a monocular camera … Read more
Click the above “Beginner’s Guide to Vision“, select to add “Starred” or “Pinned“ Important content delivered promptly This article is reprinted from: OpenCV Academy Traditional Object Recognition – Pattern Recognition Traditional pattern recognition neural network (NN) algorithms are based on gradient descent and learn to recognize and classify different target samples based on a large … Read more
Currently, there are applications based on Transformer in three major image problems:Classification (ViT), Detection (DETR) and Segmentation (SETR), all achieving good results. In the future, could Transformer possibly replace CNN? Will Transformer revolutionize the CV field just like its application in NLP? What might the research directions be? Please look forward to the next article … Read more
Abstract Artificial Intelligence (AI) has entered a new period of vigorous development. The driving forces behind this wave of AI are three major engines: Deep Learning (DL), Big Data, and Large-Scale Parallel Computing, with DL at the core. This article reviews the basic situation of the “revival of deep neural networks,” briefly introduces four commonly … Read more