In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse ...

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement ...

Banknote recognition plays a crucial role in various applications, including automated teller machines (ATMs), currency counting machines, and retail transactions. This project aims to provide a ...

This project implements basic machine learning algorithms for image recognition on the MNIST dataset in C++. Currently, it almosts includes a k-Nearest Neighbors (k-NN) classifier. The code is ...

Mathematicians have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine ...

Image processing is used in a variety of use cases today, including visualisation, pattern recognition, segmentation, image information extraction, classification, and many others. Image processing ...

But neural network-based image recognition algorithms are still far from perfect, and according to a pair of recent papers these algorithms can be tricked pretty easily.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. ... such as image classification and speech recognition, ...

Some common applications of machine learning include image recognition, natural language processing, fraud detection, and recommendation systems.” Screenshot from ChatGPT, April 2023 BARD ...

A new machine-learning technique has significantly reduced noise in images taken by scanning transmission electron microscopy (STEM). Developed by researchers in Ireland, the algorithm could make it ...