The Experts below are selected from a list of 2163 Experts worldwide ranked by ideXlab platform
Harrison Leach - One of the best experts on this subject based on the ideXlab platform.
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[Poster] Contextually panned and zoomed augmented reality interactions using COTS heads up displays
2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2014Co-Authors: Alex Hill, Harrison LeachAbstract:Consumer of the shelf heads up displays with onboard cameras and processing power have recently become available. Evaluations of a Naive Implementation of video-see-through augmented reality suggest that their small display and off-axis camera presents usability problems. We panned and zoomed a composited video feed on the Google Glass device to center the augmented reality context within the display and to give the appearance of a fixed distance to the content. We pilot tested both the panned and zoomed display against a Naive Implementation and found that users preferred the view-stabilized version.
Pavel Zemcik - One of the best experts on this subject based on the ideXlab platform.
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Wavelet Lifting on Application Specific Vector Processor
2020Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its efficient Implementation is becoming increasingly more important. This work presents a general approach of discrete wavelet transform scheme vectorisation evaluated on an FPGAbased Application-Specific Vector Processor (ASVP). This unit can be classified as SIMD computer in Flynn’s taxonomy. The presented approach is compared with two other non-vectorised approaches. Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 2:6 compared to Naive Implementation.
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minimum memory vectorisation of wavelet lifting
Advanced Concepts for Intelligent Vision Systems, 2013Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its effective Implementation is becoming increasingly more important. This work presents a novel approach to discrete wavelet transform through a new computational scheme of wavelet lifting. The presented approach is compared with two other. The results are obtained on a general purpose processor with 4-fold SIMD instruction set (such as Intel x86-64 processors). Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 3× compared to Naive Implementation.
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ACIVS - Minimum Memory Vectorisation of Wavelet Lifting
Advanced Concepts for Intelligent Vision Systems, 2013Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its effective Implementation is becoming increasingly more important. This work presents a novel approach to discrete wavelet transform through a new computational scheme of wavelet lifting. The presented approach is compared with two other. The results are obtained on a general purpose processor with 4-fold SIMD instruction set (such as Intel x86-64 processors). Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 3× compared to Naive Implementation.
Alex Hill - One of the best experts on this subject based on the ideXlab platform.
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ISMAR - [Poster] Contextually panned and zoomed augmented reality interactions using COTS heads up displays
2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2014Co-Authors: Alex Hill, Harrison D. LeachAbstract:Consumer of the shelf heads up displays with onboard cameras and processing power have recently become available. Evaluations of a Naive Implementation of video-see-through augmented reality suggest that their small display and off-axis camera presents usability problems. We panned and zoomed a composited video feed on the Google Glass device to center the augmented reality context within the display and to give the appearance of a fixed distance to the content. We pilot tested both the panned and zoomed display against a Naive Implementation and found that users preferred the view-stabilized version.
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[Poster] Contextually panned and zoomed augmented reality interactions using COTS heads up displays
2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2014Co-Authors: Alex Hill, Harrison LeachAbstract:Consumer of the shelf heads up displays with onboard cameras and processing power have recently become available. Evaluations of a Naive Implementation of video-see-through augmented reality suggest that their small display and off-axis camera presents usability problems. We panned and zoomed a composited video feed on the Google Glass device to center the augmented reality context within the display and to give the appearance of a fixed distance to the content. We pilot tested both the panned and zoomed display against a Naive Implementation and found that users preferred the view-stabilized version.
David Barina - One of the best experts on this subject based on the ideXlab platform.
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Wavelet Lifting on Application Specific Vector Processor
2020Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its efficient Implementation is becoming increasingly more important. This work presents a general approach of discrete wavelet transform scheme vectorisation evaluated on an FPGAbased Application-Specific Vector Processor (ASVP). This unit can be classified as SIMD computer in Flynn’s taxonomy. The presented approach is compared with two other non-vectorised approaches. Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 2:6 compared to Naive Implementation.
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minimum memory vectorisation of wavelet lifting
Advanced Concepts for Intelligent Vision Systems, 2013Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its effective Implementation is becoming increasingly more important. This work presents a novel approach to discrete wavelet transform through a new computational scheme of wavelet lifting. The presented approach is compared with two other. The results are obtained on a general purpose processor with 4-fold SIMD instruction set (such as Intel x86-64 processors). Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 3× compared to Naive Implementation.
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ACIVS - Minimum Memory Vectorisation of Wavelet Lifting
Advanced Concepts for Intelligent Vision Systems, 2013Co-Authors: David Barina, Pavel ZemcikAbstract:With the start of the widespread use of discrete wavelet transform the need for its effective Implementation is becoming increasingly more important. This work presents a novel approach to discrete wavelet transform through a new computational scheme of wavelet lifting. The presented approach is compared with two other. The results are obtained on a general purpose processor with 4-fold SIMD instruction set (such as Intel x86-64 processors). Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 3× compared to Naive Implementation.
C. Faloutsos - One of the best experts on this subject based on the ideXlab platform.
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AutoLag: automatic discovery of lag correlations in stream data
21st International Conference on Data Engineering (ICDE'05), 2005Co-Authors: Y. Sakurai, S. Papadimitriou, C. FaloutsosAbstract:We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the Naive Implementation, with at most 1% relative error.
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ICDE - AutoLag: automatic discovery of lag correlations in stream data
21st International Conference on Data Engineering (ICDE'05), 2005Co-Authors: Y. Sakurai, S. Papadimitriou, C. FaloutsosAbstract:We have introduced the problem of automatic lag correlation detection on streaming data and proposed AutoLag to address this problem by using careful approximations and smoothing. Our experiments on real and realistic data show that AutoLag works as expected, estimating the unknown lags with excellent accuracy and significant speed-up. In our experiments on real and realistic data, AutoLag was up to about 42,000 times faster than the Naive Implementation, with at most 1% relative error.
