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ACIVS
2005
Springer
14 years 2 months ago
Image De-Quantizing via Enforcing Sparseness in Overcomplete Representations
We describe a method for removing quantization artifacts (de-quantizing) in the image domain, by enforcing a high degree of sparseness in its representation with an overcomplete or...
Luis Mancera, Javier Portilla
AUSDM
2007
Springer
131views Data Mining» more  AUSDM 2007»
14 years 2 months ago
A Bottom-Up Projection Based Algorithm for Mining High Utility Itemsets
Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset ...
Alva Erwin, Raj P. Gopalan, N. R. Achuthan
SADM
2010
173views more  SADM 2010»
13 years 3 months ago
Data reduction in classification: A simulated annealing based projection method
This paper is concerned with classifying high dimensional data into one of two categories. In various settings, such as when dealing with fMRI and microarray data, the number of v...
Tian Siva Tian, Rand R. Wilcox, Gareth M. James
ICIP
2007
IEEE
14 years 10 months ago
Statistically Driven Sparse Image Approximation
Finding the sparsest approximation of an image as a sum of basis functions drawn from a redundant dictionary is an NPhard problem. In the case of a dictionary whose elements form ...
Rosa M. Figueras i Ventura, Eero P. Simoncelli
ICPR
2006
IEEE
14 years 9 months ago
Adaptive variational sinogram interpolation of sparsely sampled CT data
We present various kinds of variational PDE based methods to interpolate missing sinogram data for tomographic image reconstruction. Using the observed sinogram data we inpaint th...
Harald Köstler, Marcus Prümmer, Ulrich R...