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500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300. Each image segmented by five different subjects on average. 500 Segmented images Contour detection and hierarchical image segmentation 2011 [15] University of California, Berkeley: Microsoft Common Objects in Context ...
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
This article discusses a set of tactics useful in software testing.It is intended as a comprehensive list of tactical approaches to software quality assurance (more widely colloquially known as quality assurance (traditionally called by the acronym "QA")) and general application of the test method (usually just called "testing" or sometimes "developer testing").
Visual testing, therefore, requires the recording of the entire test process – capturing everything that occurs on the test system in video format. Output videos are supplemented by real-time tester input via picture-in-a-picture webcam and audio commentary from microphones. Visual testing provides a number of advantages.
An image retrieval system is a computer system used for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, title or descriptions to the images so that retrieval can be performed over the annotation words.
Metadata Working Group was formed by a consortium of companies in 2006 (according to their web page) or 2007 (as stated in their own press release). Version 2.0 of the specification was released in November 2010, [8] giving recommendations concerning the use of Exif, IPTC and XMP metadata in images.
With seamless integration across smartphones, tablets, laptops, and desktops, Capsyl Cloud provides a consistent, hassle-free user experience. Capsyl Cloud goes beyond storage—it leverages AI-powered tools to enhance how users organize, relive, and optimize their digital memories. Advanced AI capabilities include:
API testing is a type of software testing that involves testing application programming interfaces (APIs) directly and as part of integration testing to determine if they meet expectations for functionality, reliability, performance, and security. [1] Since APIs lack a GUI, API testing is performed at the message layer. [2]
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