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●2019 3rd International Symposium on Intelligent Unmanned Systems and Artificial Intelligence(SIUSAI 2019)-- Ei Compendex & Scopus—Call for papers
|August 26-28, 2019|Vancouver, Canada|Website: http://www.siusai.org/
●Venue: Τhe University of British Columbia,Vancouver, Canada
The University of British Columbia (UBC) is a public research university with campuses in Vancouver and Kelowna, British Columbia. Established in 1908, UBC is British Columbia's oldest university. The university is ranked among the top 20 public universities worldwide and among the top three in Canada.
●SIUSAI 2019 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Intelligent Unmanned Systems and Artificial Intelligence can be shared and examined. The conference is an ideal platform for keeping up with advances and changes to a consistently morphing field.
●Publication and Indexing
All accepted papers will be published in the digital conference proceedings- International Conference Proceedings Series by ACM which will send to be indexed by all major citation databases such as Ei Compendex, Scopus, Google Scholar, Cambridge Scientific Abstracts (CSA), Inspec, SCImago Journal & Country Rank (SJR), EBSCO, CrossRef, Thomson Reuters (WoS), etc.
A selection of papers will be recommended to be published in journals.
●Program Preview/ Program at a glance
August 26, 2019: Registration + Icebreaker Reception
August 27, 2019: Opening Ceremony+ KN Speech+ Technical Sessions
August 28, 2019: Technical Sessions+ Half day tour/Lab tours
●Paper Submission
1. PDF version submit via CMT: https://cmt3.research.microsoft.com/SIUSAI2019
2. Submit Via email directly to: siusai@iased.org
●CONTACT US
Ms. Amy L. P. Yeung
Email: siusai@iased.org
Website: http://www.siusai.org/
Source : Event Website
Fast compressed domain image retrieval
Speakers - ICVISP 2018
Content-based image retrieval (CBIR) has been an active research area for many years. However, almost all images are stored in compressed form, the vast majority of CBIR algorithms operate in the (uncompressed) pixel domain. This not only leads to a computational overhead for feature calculation, it can also be shown that image compression affects retrieval accuracy, especially at extreme compression rates.
In my talk, I will discuss efficient and effective CBIR techniques that operate directly in the compressed domain. In particular, I will focus on JPEG compressed images since most images are stored in this format. Our compressed domain retrieval techniques eliminate the need of full decompression for feature extraction while matching common pixel domain methods in terms of retrieval performance. Last not least I will show how retrieval can be achieved without any decompression at all, by exploiting adapted information stored in the header of JPEG files.
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