'Image content' diaporamas de présentation

Content-based Image Retrieval (CBIR)

Content-based Image Retrieval (CBIR). Searching a large database for images that match a query: What kinds of databases? What kinds of queries? What constitutes a match? How do we make such searches efficient?. Applications. Art Collections e.g. Fine Arts Museum of San Francisco

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???????? ??????. Fractal-based Image Database Retrieval. ???????. ?????????? http://image.nsysu.edu.tw/. ???. ????? ??????????? ?????? ?????? ????? ??????? ?????? ??????. ?????. ???????????????? ???????? ( a) ????????????? ( b) ??????????????? ??????????????

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Using HTML Metadata to Retrieve Relevant Images from the World Wide Web

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CADIP Research at UW-Milwaukee

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Seam Carving for Content-Aware Image Resizing

Seam Carving for Content-Aware Image Resizing. Shai Avidan Mitsubishi Electric Research Labs Ariel Shamir The Interdisciplinary Center & MERL. Introduction Background The Operator Discrete Image Resizing Multi-size Images Limitations Conclusions and Future Work. Introduction.

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Seam Carving for Content-Aware Image Resizing

Seam Carving for Content-Aware Image Resizing. Shai Avidan Mitsubishi Electric Research Labs Ariel Shamir The Interdisciplinary Center & MERL. Introduction Background The Operator Discrete Image Resizing Multi-size Images Limitations Conclusions and Future Work. Introduction.

568 views • 56 slides

Multimedia Information Retrieval

Multimedia Information Retrieval. It is of the highest importance, in the art of detection, to be able to recognize out of a number of facts which are incidental and which are vital ... -Sherlock Holmes Presented by Dipti Vaidya. Overview of the Presentation.

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Architectures: From Video Storage to Campus Rich Media Services

Architectures: From Video Storage to Campus Rich Media Services. Alan McCord and Dan Hague University of Michigan SURA/ViDe Conference March 26, 2003. Agenda. 0-10 minutes Introductions, Al setting the stage for Dan 11-30 minutes Dan describing his IP SANS solution 31-50 minutes

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Content Based Image Retrieval

Content Based Image Retrieval. Natalia Vassilieva HP Labs Russia. Tutorial outline. Lecture 1 Introduction Applications Lecture 2 Performance measurement Visual perception Color features Lecture 3 Texture features Shape features Fusion methods Lecture 4 Segmentation

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Agenda

Agenda. Why bother? What to consider Design specifics Other elements Samples. Why Bother?. Nearly everyone with a Social Strategy has a Twitter Account It is easy to do and helps polish your image Not everyone utilizes the branding opportunity

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COMP 9517 Computer Vision

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inf.ed.ac.uk

Institute for Language, Cognition, and Computation (ILCC). www.inf.ed.ac.uk. Broad and Deep Strength in Research. Natural language processing and computational linguistics (SMT, web mining, parsing ...) Spoken language processing Dialogue and multimodal interaction

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Photo from history

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Text Extraction from Image using Python

With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to and talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition OCR comes in. Here we process the image, where we apply various pre processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha "Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2501.pdf Paper URL: http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash

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SmartVision - A New Turning Point for Responsive Web Design

Fast image delivery and responsive web design has been an issue for most websites. At Instart Logic, the back-end infrastructure like Image Resizing and unique client-cloud architecture enables SmartVision. These optimizes the whole delivery pipeline around content being delivered, reduces load time and enhance user experience. Learn more about SmartVision technology at: http://www.instartlogic.com/technology/smartvision/

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Content Production | AuroIN

We deals with content production as well as content marketing.

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Video Analysis: Annotation technology for retrieval

Browsing and metadata clustering Sketch based query composition Image similarity search OQUEL, query description language Personal Medial Management ICON: Image Content-based Organisation and Navigation CVIeditor: Computer Vision-based Image editor

1.71k views • 1 slides