Image Segmentation

Deep Learning-Based Natural And Medical Image Segmentation: Network Structure Design

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Deep Learning-Based Natural And Medical Image Segmentation: Network Structure Design

Source: Zhihu, Jishi Platform, Deep Learning Enthusiasts Author: Li Muqing @ Zhihu https://zhuanlan.zhihu.com/p/104854615 This article is about 5100 words long and is recommended for a 10-minute read. This article first introduces some classic semantic segmentation networks and their innovations, and then discusses some applications of network structure design in the field of medical image segmentation. … Read more

Essential Knowledge Summary for Digital Image Processing

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Click the "Visual Learning for Beginners" above, select "Star" or "Top" Heavyweight content delivered first time Basic Knowledge1. Digital Image:A digital image, also known as a digital photo or pixel image, is a two-dimensional image represented by a finite number of digital pixel values. A digital image is obtained by digitizing an analog image and … Read more

Understanding Fast-SCNN and Implementation Using TensorFlow 2.0

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Understanding Fast-SCNN and Implementation Using TensorFlow 2.0

Click the above “Beginner’s Guide to Vision”, select“Star” or “Pin” Heavy content delivered at the first time Author: Kshitiz Rimal Compiler: ronghuaiyang Introduction An explanation of the Fast-SCNN image segmentation method and code analysis of its implementation. Fast Segmentation Convolutional Neural Network (Fast-SCNN) is a real-time semantic segmentation model designed for high-resolution image data, suitable … Read more

Understanding Object Detection in Computer Vision

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Understanding Object Detection in Computer Vision

Click the "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavyweight content delivered to you first Editor’s Recommendation Object Detection is a popular direction in the field of Computer Vision, widely used in autonomous driving, industrial inspection, video surveillance, and aerospace, among others. Its basic process involves locating the target of interest in … Read more

How Is Computer Vision Evolving? A Comprehensive Review of Segmentation Large Models (SAM/SegGPT/SEEM)

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How Is Computer Vision Evolving? A Comprehensive Review of Segmentation Large Models (SAM/SegGPT/SEEM)

Source｜Heart of Autonomous Driving Image segmentation in computer vision is an important subfield that aims to assign each pixel in an image to different categories or objects. This technology is widely used in various applications such as image recognition, scene understanding, and medical image processing, demonstrating significant practical value. Previously, there were roughly two methods … Read more

Introduction to TensorFlow Image Segmentation Loss Functions

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In previous articles, we have introduced two types of image segmentation loss functions. Today, we will share commonly used multi-class image segmentation loss functions such as multi-class cross-entropy, weighted multi-class cross-entropy, multi-class Dice coefficient, multi-class Focal Loss, etc., and provide code to reproduce the above loss functions in TensorFlow. 1. Cross Entropy The cross-entropy loss … Read more

Introduction to Image Segmentation Loss Functions in TensorFlow

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Commonly used image segmentation loss functions include binary cross-entropy, dice coefficient, Tversky, and Focal Loss. Today, I will reproduce the above loss functions in TensorFlow and compare the results. 1. Cross Entropy The cross-entropy loss function compares the predicted class values with the target values on a pixel-by-pixel basis, and then averages the values over … Read more

Multimodal Visual Structure Learning

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Multimodal Visual Structure Learning

Author / Li Xi 0 Introduction This article organizes previous research on multimodal visual structure learning from a new perspective, focusing on the characteristics and applications of spherical panoramic images. Spherical images are mostly related to fisheye or 360° panoramic views, containing a wealth of structural knowledge, primarily aimed at applications such as autonomous driving, … Read more

40 Classic Papers on Convolutional Neural Networks

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40 Classic Papers on Convolutional Neural Networks

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

Using Image Segmentation for Defect Detection: A Practical Guide

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Using Image Segmentation for Defect Detection: A Practical Guide

Click the "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavy content delivered at the first time Author丨Vinithavn Source丨AI Park 1. Introduction What is Object Detection? Given an image, we humans can identify objects within it. For example, we can detect if there are cars, trees, people, etc., in the image. If we … Read more