boundary surfaces are presented. Independence of shading and classification calculations insures an undistorted visualization of 3-D shape. Non-binary classification operators insure that small or poorly defined features are not IosL The resulting colors and opacities am composited from back to front along

, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding. A

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Abstract. The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm (SOAP: Selection of Attributes by Projection) has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. The performance of SOAP is analyzed in two ways: percentage of reduction and classification. SOAP has been compared to CFS [4] and ReliefF [6]. The results are generated by C4.5 before and after the application of the algorithms. 1

We present an end-to-end feature description pipeline which uses a novel interest point detector and Rotation-Invariant Fast Feature (RIFF) descriptors. The proposed RIFF algorithm is 15 × faster than SURF 1 while producing large-scale retrieval results that are comparable to SIFT. 2 Such high

The thesis deals with the fast feature matching for simultaneous localization and mapping. A brief description of local features invariant to scale, rotation, translation and affine transformations, their detectors and descriptors are included. In general, real–time response for matching is crucial

can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. Results: We present a semi-automatic procedure for deriving a novel hierarchical

Dimensionalitycurse and dimensionality reduction are two issues that have retained high interest for data mining, machine learning, multimedia indexing, and clustering. We present a fast, scalable algorithm to quickly select the most important attributes (dimensions) for a given set of n

The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well