Artistic robotic painting implies creating a picture on canvas according to brushstroke map preliminarily computed from source image. To make the look closer human artwork, image should be preprocessed render effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and coherence. We propose an algorithm for perspective amp...

Preprocessing of CNF formulas is an invaluable technique when attempting to solve large formulas, such as those that model industrial verification problems. Unfortunately, the best combination of preprocessing techniques, which involve variable elimination combined with subsumption, is incompatible with incremental satisfiability. The reason is that soundness is lost if a variable is eliminated...

We examine the efficiency of clustering a set of points, when the encompassing metric space may be preprocessed in advance. In computational problems of this genre, there is a first stage of preprocessing, whose input is a collection of points M ; the next stage receives as input a query set Q ⊂ M , and should report a clustering of Q according to some objective, such as 1-median, in which case...

Some computationally hard problems –e.g., deduction in logical knowledge bases– are such that part of an instance is known well before the rest of it, and remains the same for several subsequent instances of the problem. In these cases, it is useful to preprocess off-line this known part so as to simplify the remaining on-line problem. In this paper we investigate such a technique in the contex...

Most machine learning tools work with a single table where each row is an instance and each column is an attribute. Each cell of the table contains an attribute value for an instance. This representation prevents one important form of learning, which is, classification based on groups of correlated records, such as multiple exams of a single patient, internet customer preferences, weather forec...

We present a first theoretical analysis of the power of polynomial-time preprocessing for important combinatorial problems from various areas in AI. We consider problems from Constraint Satisfaction, Global Constraints, Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a complexity theoretic assumption, none of the considered problems can be reduced by polynomial-tim...

Standard image processing techniques are not applicable to radar images because of the coherent nature of the sensor. Therefore there is a need to develop preprocessing techniques for radar images which will then allow these standard methods to be applied. A random field model for radar image data is developed. This model de~­ cribes the image data as the result of a mult1plicative-convolved pr...

In this work we study preprocessing for tractable problems when part of the input is unknown or uncertain. This comes up naturally if, e.g., the load of some machines or the congestion of some roads is not known far enough in advance, or if we have to regularly solve a problem over instances that are largely similar, e.g., daily airport scheduling with few charter flights. Unlike robust optimiz...

Data preprocessing converts raw data and signals into data representation suitable for application through a sequence of operations. The objectives of data preprocessing include size reduction of the input space, smoother relationships, data normalization, noise reduction, and feature extraction. Several data preprocessing algorithms, such as data values averaging, input space reduction, and da...