@conference {19290,
	title = {Loop transformations for interface-based hierarchies IN SDF graphs},
	booktitle = {2010 21st IEEE International Conference on Application-specific Systems Architectures and Processors (ASAP)},
	year = {2010},
	month = {2010},
	pages = {341 - 344},
	abstract = {Data-flow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of data-flow, termed synchronous data-flow (SDF), offers strong compile-time predictability properties, but has limited expressive power. A new type of hierarchy (Interface-based SDF) has been proposed allowing more expressivity while maintaining its predictability. One of the main problems with this hierarchical SDF model is the lack of trade-off between parallelism and network clustering. This paper presents a systematic method for applying an important class of loop transformation techniques in the context of interface-based SDF semantics. The resulting approach provides novel capabilities for integrating parallelism extraction properties of the targeted loop transformations with the useful modeling, analysis, and code reuse properties provided by SDF.},
	keywords = {Application software, code generation, Computer architecture, Computer interfaces, Data-Flow programming, Digital signal processing, Loop parallelization, PARALLEL PROCESSING, Power engineering computing, Power system modeling, Processor scheduling, Programming profession, scheduling, SDF graph, system recovery},
	author = {Piat, J. and Bhattacharyya, Shuvra S. and Raulet, M.}
}
@conference {14208,
	title = {Combining powerful local and global statistics for texture description},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009},
	year = {2009},
	month = {2009/06/20/25},
	pages = {573 - 580},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A texture descriptor is proposed, which combines local highly discriminative features with the global statistics of fractal geometry to achieve high descriptive power, but also invariance to geometric and illumination transformations. As local measurements SIFT features are estimated densely at multiple window sizes and discretized. On each of the discretized measurements the fractal dimension is computed to obtain the so-called multifractal spectrum, which is invariant to geometric transformations and illumination changes. Finally to achieve robustness to scale changes, a multi-scale representation of the multifractal spectrum is developed using a framelet system, that is, a redundant tight wavelet frame system. Experiments on classification demonstrate that the descriptor outperforms existing methods on the UIUC as well as the UMD high-resolution dataset.},
	keywords = {Computer science, discretized measurements, fractal geometry, Fractals, geometric transformations, global statistics, Histograms, illumination transformations, image classification, image resolution, Image texture, lighting, local measurements SIFT features, local statistics, MATHEMATICS, multifractal spectrum, multiscale representation, Power engineering and energy, Power engineering computing, Robustness, Solids, Statistics, texture description, UMD high-resolution dataset, wavelet frame system, Wavelet transforms},
	isbn = {978-1-4244-3992-8},
	doi = {10.1109/CVPR.2009.5206741},
	author = {Yong Xu and Si-Bin Huang and Hui Ji and Ferm{\"u}ller, Cornelia}
}
