Ändra sökning
Avgränsa sökresultatet
1 - 5 av 5
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Abedan Kondori, Farid
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Yousefi, Shahrouz
    KTH Royal Institute of Technology, Department of Media Technology and Interaction Design.
    Kouma, Jean-Paul
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Liu, Li
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    KTH Royal Institute of Technology, Department of Media Technology and Interaction Design.
    Direct hand pose estimation for immersive gestural interaction2015Ingår i: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 66, s. 91-99Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a novel approach for performing intuitive gesture based interaction using depth data acquired by Kinect. The main challenge to enable immersive gestural interaction is dynamic gesture recognition. This problem can be formulated as a combination of two tasks; gesture recognition and gesture pose estimation. Incorporation of fast and robust pose estimation method would lessen the burden to a great extent. In this paper we propose a direct method for real-time hand pose estimation. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Extensive experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation On two different setups; desktop computing, and mobile platform. This reveals the system capability to accommodate different interaction procedures. In addition, a user study is conducted to evaluate learnability, user experience and interaction quality in 3D gestural interaction in comparison to 2D touchscreen interaction.

  • 2.
    Karlsson, Johannes
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Kouma, Jean-Paul
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Wark, Tim
    CSIRO.
    Corke, Peter
    CSIRO.
    Demonstration of Wyner-Ziv video compression in a wireless camera sensor network2009Ingår i: The 9th Scandinavian Workshop on Wireless Ad-hoc & Sensor Networks (ADHOC'09 ). 4-5 May 2009, Uppsala, Sweden., Uppsala, 2009Konferensbidrag (Refereegranskat)
    Abstract [en]

    Sending  video over wireless sensor networks is a challenging task. The encoding and transmission of video is very resource hungry and the sensor nodes have very limited resources in terms of communication bandwidth,memory, computation and  typically 5-10 times. In this paper we will present a practical implementation of a Wyner-Ziv video codec where the reversed asymmetry in complexity between encoder and decoder can be achieved. We will also present our sensor network platform used in this demonstration known as Fleck TM-3 as well as two different co-processor daughterboards for image processing. The different daughterboards are then compared in terms of speed and energy consumption.

  • 3.
    Kouma, Jean-Paul
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Large-scale face images retrieval: a distribution coding approach2009Ingår i: ICUMT 2009 - International Conference on Ultra Modern Telecommunications, 2009Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Great progress in face recognition technology has been made recently. Such advances will provide us the possibility to build a new generation of search engine: Face Google, searching from person photos. It is very challenging to find a person from a very large or extremely large database which might hold face images of millions or hundred millions of people. The indexing technology used in most commercial search engines like Google, is very efficient for text-based search, unfortunately, it is no longer useful for image search. A solution is to use partial information (signature) about all the face images for search. The retrieval speed is approximately proportional to the size of a signature image. In this paper we will study a totally new way to compress the signature images based on the observation that the face signature images and the query images are highly correlated if they are from the same individual. The face signature image can be greatly compressed (one or two orders of magnitude improvement) by use of knowledge of the query images. We can expect the new compression algorithm to speed up face search 10 to 100 times. The challenge is that query images are not available when we compress their signature image. Our approach is to transfer the face search problem into the so-called ”Wyner-Ziv Coding” problem, which could give the same compression efficiency even if the query images are not available until we decompress signature images. A practical compression scheme based on LDPC codes is developed to compress face signature images.

  • 4.
    Kouma, Jean-Paul
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Li, Haibo
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Large-scale face images retrieval: a transform coding approach2010Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Huge efforts have been devoted to face recognition technology and remarkable results, noticed. Such advances will provide us the possibility to build a new generation of search engine: persons photo fetching. It is a real computing challenge to find a person from a very large or extremely large database which might hold face images of millions or hundred millions of people. A candidate solution is to use partial information (signature) about all the face images for search, making the retrieval speed approximately proportional to the size of a signature image. In this paper we will investigate a totally new way to compress the signature images based on the observation that the face signature images and the query images are highly correlated if they are from the same individual. The face signature image can be greatly compressed (one or two orders of magnitude improvement) by use of knowledge of the query images. We can expect the new compression algorithm to speed up face search 10 to 100 times. The challenge is that query images are not available when we compress their signature image. Our approach is to transfer the face search problem into the so-called ”Wyner-Ziv Coding” problem, which could give the same compression efficiency even if the query images are not available until we decompress signature images. A practical compression scheme based on LDPC codes is developed to compress and retrieve face signature images.

  • 5.
    Kouma, Jean-Paul
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Söderström, Ulrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Wyner-Ziv Video Coding using Hadamard Transform and Deep Learning2016Ingår i: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 7, nr 7, s. 582-589Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predictive schemes are current standards of video coding. Unfortunately they do not apply well for lightweight devices such as mobile phones. The high encoding complexity is the bottleneck of the Quality of Experience (QoE) of a video conversation between mobile phones. A considerable amount of research has been conducted towards tackling that bottleneck. Most of the schemes use the so-called Wyner-Ziv Video Coding Paradigm, with results still not comparable to those of predictive coding. This paper shows a novel approach for Wyner-Ziv video compression. It is based on the Reinforcement Learning and Hadamard Transform. Our Scheme shows very promising results.

1 - 5 av 5
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf