Results 1 - 10 of 462

A general framework for object detection

by Constantine P. Papageorgiou, Michael Oren, Tomaso Poggio - Sixth International Conference on , 1998
"... This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of ..."
Abstract - Cited by 395 (21 self) - Add to MetaCart
previous approaches, this system learns from examples and does not rely on any a priori (handcrafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest

Example-based super-resolution

by William T. Freeman, Thouis R. Jones, Egon C. Pasztor - IEEE COMPUT. GRAPH. APPL , 2001
"... The Problem: Pixel representations for images do not have resolution independence. When we zoom into a bitmapped image, we get a blurred image. Figure 1 shows the problem for a teapot image, rich with real-world detail. We know the teapot’s features should remain sharp as we zoom in on them, yet sta ..."
Abstract - Cited by 349 (5 self) - Add to MetaCart
of pixel-based representations is an important task for image-based rendering. Our example-based super-resolution algorithm yields Fig. 1 (h, i). Previous Work: Researchers have long studied image interpolation, although only recently using machine learning or sampling approaches, which offer much power

Machine recognition of human activities: A survey

by Pavan Turaga, Rama Chellappa, V. S. Subrahmanian, Octavian Udrea , 2008
"... The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the a ..."
Abstract - Cited by 218 (0 self) - Add to MetaCart
The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition

Video Encoding and Transcoding Using Machine Learning

by Gerardo Fernandez Escribano, Rashid Jillani, Christopher Holder, Hari Kalva, Jose Luis, Martinez Martinez, Pedro Cuenca
"... Machine learning has been widely used in video analysis and search applications. In this paper, we describe a non-traditional use of machine learning in video processing – video encoding and transcoding. Video encoding and transcoding are computationally intensive processes and this complexity is in ..."
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Machine learning has been widely used in video analysis and search applications. In this paper, we describe a non-traditional use of machine learning in video processing – video encoding and transcoding. Video encoding and transcoding are computationally intensive processes and this complexity

An MPEG-2 to H.264 Video Transcoder in the Baseline Profile

by G. Fernández-escribano, H. Kalva, Senior Member, J. L. Martínez, Student Member, P. Cuenca, L. Orozco-barbosa, A. Garrido
"... Abstract—Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format and the distributi ..."
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Abstract—Based on our previous efforts, we introduce in this letter a high-efficient MPEG-2 to H.264 transcoder for the baseline profile in the spatial domain. Machine learning tools are used to exploit the correlation between the macroblock (MB) decision of the H.264 video format

IMPROVED MACHINE LEARNING TECHNIQUES FOR LOW COMPLEXITY MPEG-2 TO H.264 TRANSCODING USING OPTIMIZED CODECS

by Chris Holder, Tao Pin, Hari Kalva
"... This paper discusses techniques for efficiently implementing a Mpeg-2 to H.264 video transcoder. The transcoding results reported in the literature are based on a reference implementation and may not reflect the true performance gains obtained in real world systems. We have developed low complexity ..."
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and low-complexity H.264 MB encoding mode decisions. The results show that the proposed transcoder reduces the complexity by 50 % without a significant loss in PSNR. This performance improvement in production quality transcoders, and demonstrates the practicality of machine learning based video

Very low complexity mpeg-2 to h.264 transcoding using machine learning

by Gerardo Fernández , Hari Kalva , Pedro Cuenca , Luis Orozco Barbosa - in [MULTIMEDIA ’06: Proceedings of the 14th annual ACM international conference on Multimedia ], 931–940, ACM , 2006
"... ABSTRACT This paper presents a novel macroblock mode decision algorithm for inter-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast m ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
ABSTRACT This paper presents a novel macroblock mode decision algorithm for inter-frame prediction based on machine learning techniques to be used as part of a very low complexity MPEG-2 to H.264 video transcoder. Since coding mode decisions take up the most resources in video transcoding, a fast

Reduced Resolution MPEG-2 to H.264 Transcoder

by Hari Kalva, Gerardo Fernández-escribano, Kelly Kunzelmann
"... This paper describes complexity reduction in MPEG-2 to H.264 transcoding with resolution reduction. The methods developed are applicable to transcoding any DCT based video such as MPEG-2, MPEG-4, and H.263 to the recently standardized H.264 video at a reduced resolution. H.264 is being adopted by mo ..."
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allows conversion of MPEG-2 video to the H.264 video format at a reduced resolution with substantially less computing complexity. We use machine learning based approaches to significantly reduce the complexity of this transcoding. Experimental results show a reduction in transcoding time of about 67 % (a

Machine Learning for Video-Based Rendering

by Arno Schödl, Irfan Essa - In Advances in Neural Information Processing Systems , 2000
"... We pres[ t techniques for rendering and animation of realis4: ss4: by analyzing and training onsS7] videosoS7(1[(S This workextends the new paradigm for computer animation, video textures, whichus[ recorded video to generate novelanimations by replaying the videos amples in a new order. Here we conc ..."
Abstract - Cited by 15 (2 self) - Add to MetaCart
We pres[ t techniques for rendering and animation of realis4: ss4: by analyzing and training onsS7] videosoS7(1[(S This workextends the new paradigm for computer animation, video textures, whichus[ recorded video to generate novelanimations by replaying the videos amples in a new order. Here we

Real-time neuroevolution in the nero video game

by Kenneth O. Stanley, Bobby D. Bryant, Risto Miikkulainen - IEEE Transactions on Evolutionary Computation , 2005
"... In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This pap ..."
Abstract - Cited by 120 (37 self) - Add to MetaCart
’ teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive
Results 1 - 10 of 462