This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained reg...
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This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained regions that will be used as a reference for the construction of a distance metric. In this learning process, the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the IHC pattern distributions. The results of the proposed method showed a high correlation to a pathologist's manual analysis, which was used as a golden standard, presenting a more robust discrimination between stained and non-stained areas with little bias.
Accelerometers integrated in modern smartphones pave the way to intuitively use gestures for collaboratively controlling interactive applications. Using and holding smartphones has become natural and ensures user acce...
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Accelerometers integrated in modern smartphones pave the way to intuitively use gestures for collaboratively controlling interactive applications. Using and holding smartphones has become natural and ensures user acceptance as well as intuitive handling. We focus on using accelerators in several smartphones at the same time to interactively control a medical imaging solution. To this end, we introduce a framework to collect, modify, and distribute acceleration sensor data from multiple smartphones and integrate it with a medical imaging system which results in an environment suitable for e.g. doctors reviewing and explaining diagnostic findings. We performed some experiments to evaluate the usability of this approach and present an ongoing research in adapting the smartphone interface to physical simulation applications.
In this paper we present a new cross-platform approach for video game delivery in wired and wireless local networks. The developed 3D streaming and video streaming approaches enable users to access video games on set ...
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Building a dense and accurate environment model out of range image data faces problems like sensor noise, extensive memory consumption or computation time. We present an approach which reconstructs 3D environments usi...
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Building a dense and accurate environment model out of range image data faces problems like sensor noise, extensive memory consumption or computation time. We present an approach which reconstructs 3D environments using a probabilistic occupancy grid in real-time. Operating on depth image pyramids speeds up computation time, whereas a weighted interpolation scheme between neighboring pyramid layers boosts accuracy. In our experiments we compare our method with a state-of-the-art mapping procedure. Our results demonstrate that we achieve better results. Finally, we present its viability by mapping a large indoor environment.
Motivated by the success of free-parts based representations in face recognition, we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent visual ...
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作者:
Lucey, SimonLucey, PatrickAdvanced Multimedia Processing Laboratory
Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Speech
Audio Image and Video Research Laboratory Queensland University of Technology GPO Box 2424 Brisbane4001 Australia
Motivated by the success of free-parts based representations in face recognition [1] we have attempted to address some of the problems associated with applying such a philosophy to the task of speaker-independent auto...
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Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clut...
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Object identification from local information has recently been investigated with respect to its potential for robust recognition, e.g., in case of partial object occlusions, scale variation, noise, and background clutter in detection tasks. This work contributes to this research by a thorough analysis of the discriminative power of local appearance patterns and by proposing to exploit local information content for object representation and recognition. In a first processing stage, we localize discriminative regions in the object views from a posterior entropy measure, and then derive object models from selected discriminative local patterns. Object recognition is then applied to test patterns with associated low entropy using an efficient voting process. The method is evaluated by various degrees of partial occlusion and Gaussian image noise, resulting in highly robust recognition even in the presence of severe occlusion effects.
The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach call...
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In this work, we present a device for cell manipulation and separation using travelling wave dielectophoretic (twDEP) force. The device consists of microchamber and 16 parallel electrode array controlled by four-phase...
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