Cloud Computing framework for Computer Vision Research:An Introduction
Yu Zhou

TL;DR
This paper introduces a cloud computing framework tailored for computer vision research, especially in medical image analysis, emphasizing security, usability, scalability, and resource management to facilitate scientific and clinical applications.
Contribution
It proposes a novel cloud-based framework that enables secure, scalable, and user-friendly access to medical image analysis algorithms without exposing proprietary code.
Findings
Enhanced security through algorithm hiding in the cloud
Improved usability for medical end users
Scalable infrastructure for large-scale image processing
Abstract
Cloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits can be obtained from cloud in order to help medical image analysis users (including scientists, clinicians, and research institutes). As security and privacy of algorithms are important for most of algorithms inventors, these algorithms can be hidden in a cloud to allow the users to use the algorithms as a package without any access to see/change their inside. In another word, in the user part, users send their images to the cloud and configure the algorithm via an interface. In the cloud part, the algorithms are applied to this image and the results are returned back to the user. My proposal has two parts: (1) investigate the potential of cloud…
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Taxonomy
TopicsRobotics and Automated Systems · Brain Tumor Detection and Classification · IoT and Edge/Fog Computing
