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Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5] The U-Net architecture has also been employed in diffusion models for iterative image denoising. [6] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.
Medical image segmentation is made difficult by low contrast, noise, and other imaging ambiguities. Although there are many computer vision techniques for image segmentation, some have been adapted specifically for medical image computing. Below is a sampling of techniques within this field; the implementation relies on the expertise that ...
CellProfiler interfaces with the high-performance scientific libraries NumPy and SciPy for many mathematical operations, the Open Microscopy Environment [11] Consortium’s Bio-Formats library for reading more than 100 image file formats, ImageJ for use of plugins and macros, and ilastik for pixel-based classification. [12]
Originally developed by Intel, CVAT is designed for use by a professional data annotation team, with a user interface optimized for computer vision annotation tasks. [2] CVAT supports the primary tasks of supervised machine learning: object detection, image classification, and image segmentation. CVAT allows users to annotate data for each of ...
Aerial Classification, Object Detection, Instance Segmentation 2019 [147] [148] Syed Waqas Zamir, Aditya Arora, Akshita Gupta, Salman Khan, Guolei Sun, Fahad Shahbaz Khan, Fan Zhu, Ling Shao, Gui-Song Xia, Xiang Bai Aerial Image Segmentation Dataset 80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to ...
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
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