IEEE Trans Image Process - Evaluating combinational illumination estimation methods on real-world images.

Tópicos

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Resumo

Illumination estimation is an important component of color constancy and automatic white balancing. A number of methods of combining illumination estimates obtained from multiple subordinate illumination estimation methods now appear in the literature. These combinational methods aim to provide better illumination estimates by fusing the information embedded in the subordinate solutions. The existing combinational methods are surveyed and analyzed here with the goals of determining: 1) the effectiveness of fusing illumination estimates from multiple subordinate methods; 2) the best method of combination; 3) the underlying factors that affect the performance of a combinational method; and 4) the effectiveness of combination for illumination estimation in multiple-illuminant scenes. The various combinational methods are categorized in terms of whether or not they require supervised training and whether or not they rely on high-level scene content cues (e.g., indoor versus outdoor). Extensive tests and enhanced analyzes using three data sets of real-world images are conducted. For consistency in testing, the images were labeled according to their high-level features (3D stages, indoor/outdoor) and this label data is made available on-line. The tests reveal that the trained combinational methods (direct combination by support vector regression in particular) clearly outperform both the non-combinational methods and those combinational methods based on scene content cues.

Resumo Limpo

illumin estim import compon color constanc automat white balanc number method combin illumin estim obtain multipl subordin illumin estim method now appear literatur combin method aim provid better illumin estim fuse inform embed subordin solut exist combin method survey analyz goal determin effect fuse illumin estim multipl subordin method best method combin under factor affect perform combin method effect combin illumin estim multipleillumin scene various combin method categor term whether requir supervis train whether reli highlevel scene content cue eg indoor versus outdoor extens test enhanc analyz use three data set realworld imag conduct consist test imag label accord highlevel featur d stage indooroutdoor label data made avail onlin test reveal train combin method direct combin support vector regress particular clear outperform noncombin method combin method base scene content cue

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