Deep CNN based pixel-wise semantic segmentation model with >80% mIOU (mean Intersection Over Union). Trained on cityscapes dataset, which can be effectively implemented in self driving vehicle systems ...

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

aerial-segmentation-> Learning Aerial Image Segmentation from Online Maps. IterativeSegmentation-> Recurrent Neural Networks to Correct Satellite Image Classification Maps. Detectron2 FPN + PointRend ...

Before the rise of deep learning, traditional machine learning techniques, such as model-based methods (e.g., active shape and appearance models) and atlas-based methods had been shown to achieve good ...

Keywords: greenhouse image analysis, image segmentation, deep learning, U-net, quantitative plant phenotyping. Citation: Narisetti N, Henke M, Neumann K, Stolzenburg F, Altmann T and Gladilin E (2022) ...

A new technical paper titled “A Universal AI-Powered Segmentation Model for PCBA and Semiconductor” was published by researchers at Nordson Corporation. “This paper introduces a novel universal deep ...

I hope you have got a fair and understanding of image segmentation using the UNet model. Now you can try implementing image segmentation on different problems using -Net or by exploring other models ...

Colorectal cancer (CRC) is the third most common cancer in humans with a rising incidence and a high mortality rate. Image segmentation of histopathological slices based on deep learning models can ...