Computing the minimum distance between a point and a NURBS curve

A new method is presented for computing the minimum distance between a point and a NURBS curve. It utilizes a circular clipping technique to eliminate the curve parts outside a circle with the test point as its center point. The radius of the elimination circle becomes smaller and smaller during the subdivision process. A simple condition for terminating the subdivision process is provided, which leads to very few subdivision steps in the new method. Examples are shown to illustrate the efficiency and robustness of the new method.     

Modeling and classifying symmetries using a multiscale opponentcolor representation

A new class of multiscale symmetry features provides a useful high-level representation for color texture. These symmetry features are defined within and between the bands of a color image using complex moments computed from the output of a bank of orientation and scale selective filters. We show that these features not only represent symmetry information but are also invariant to rotation, scale, and illumination conditions. The features computed between color bands are motivated by opponent process mechanisms in human vision. Experimental results are provided to show the performance of this set of features for texture classification and retrieval     

Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms

This paper addresses the range image registration problem for views having low overlap and which may include substantial noise. The current state of the art in range image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: it requires prealignment of the range surfaces to a reasonable starting point; and it is not robust to outliers arising either from noise or low surface overlap. This paper proposes a new approach that avoids these problems. To that end, there are two key, novel contributions in this work: a new, hybrid genetic algorithm (GA) technique, including hill climbing and parallel-migration, combined with a new, robust evaluation metric based on surface interpenetration. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. Because they search in a space of transformations, GA are capable of registering surfaces even when there is low overlap between them and without need for prealignment. The novel GA search algorithm we present offers much faster convergence than prior GA methods, while the new robust evaluation metric ensures more precise alignments, even in the presence of significant noise, than mean squared error or other well-known robust cost functions. The paper presents thorough experimental results to show the improvements realized by these two contributions.     

Discrimination of similar characters using nonlinear normalization based on regional importance measure

Discrimination of confusing characters is very important in recognition of character sets containing a multitude of similar characters. Confusing characters have very similar shapes and are separated by only a small difference. For a successful discrimination, we need to focus on that difference. However, the small difference can be reduced or even lost during the feature extraction process. In such a case, further analysis after the feature extraction rarely succeeds. This paper proposes a discriminative nonlinear normalization algorithm to improve discrimination ability. The proposed method emphasizes the difference between confusing characters. It measures the importance of each region in the discrimination of confusing characters. Then, it resamples the image according to the regional importance measure. As a result, it expands important regions but shrinks less important regions. Since it emphasizes important regions in the preprocessing step, it does not suffer from the information loss during the feature extraction. In experiments, the proposed method successfully detected and expanded important regions. In handwritten Hangul recognition, the proposed method outperformed other two recently developed pair-wise discrimination methods. On SERI95a data set, it improved the recognition rate from 87.69 to 90.11 %, achieving a 19.66 % error reduction rate.     

Computing isophotos of surface of revolution and canal surface

Isophote of a surface consists of a loci of surface points whose normal vectors form a constant angle with a given fixed vector. It also serves as a silhouette curve when the constant angle is given as π/2. We present efficient and robust algorithms to compute isophotes of a surface of revolution and a canal surface. For the two kinds of surfaces, each point on the isophote is derived by a closed-form solution. To find each connected component in the isophote, we utilize the feature of surface normals. Both surfaces are decomposed into a set of circles, where the surface normal vectors at points on each circle construct a cone. The vectors which form a constant angle with given fixed vector construct another cone. We compute the parametric range of the connected component of the isophote by computing the parametric values of the surface which derive the tangential intersection of these two cones.     

Coupling of scales in a multiscale simulation using neural networks

Multiscale approaches require the coupling of models on different spatial scales. In this paper, a coupling using neural networks is proposed. Based on a set of mesoscale simulations of concrete a system of neural networks is trained to approximate the response. A macroscale constitutive model is obtained by homogenizing the mesoscale response. Special focus is put on the mesh sensitivity, since the mesoscale model includes softening and consequently the width of the localization zone compared to the dimension of the mesoscale model is an additional parameter in the model.     

Classification of honeybee pollen using a multiscale texture filtering scheme

People are interested in the composition of honeybee pollen due to its nutritional value and therapeutic benefits. Its palynological composition depends on the local flora surrounding the beehive, and its identification is currently done manually using optical microscopy. This procedure is tedious and expensive in systematic application and is unable to automatically separate pollen loads of different species of plants. We present an automatic methodology to discriminate pollen loads based on texture image classification. Texture features are generated using a multiscale filtering scheme. A statistical evaluation of the algorithm is provided and discussed.Received: 26 January 2003, Accepted: 2 March 2004, Published online: 13 July 2004 Correspondence to: P. Carrión     

Computing global visibility maps for regions on the boundaries of polyhedra using Minkowski sums

A global visibility map is a spherical image built to describe the complete set of global visible view directions for a surface. In this paper, we consider the computation of global visibility maps for regions on the boundary of a polyhedron. Both the self-occlusions introduced by a region and the global occlusions introduced by the rest of the surfaces on the boundary of the polyhedron are considered for computing a global visibility map. We show that the occluded view directions introduced between a pair of polyhedral surfaces can be computed from the spherical projection of the Minkowski sum of one surface and the reflection of the other. A suitable subset of the Minkowski sum, which shares the identical spherical projection with the complete Minkowski sum, is constructed to obtain the spherical images representing global occlusions. Our method has been successfully tested on many CAD models. It extends the previous methods for computing global visibility maps using convex decomposition, and it exhibits a better performance.     

Identification of complex shapes using a self organizing neuralsystem

We present a multilayer hierarchical neural system for automatic classification of complex contour patterns. The system consists of a neocognitron-like network structure combined with self-organizing maps to automatically determine feature classes. We present results showing that multilayer hierarchical networks are able to tolerate pattern distortion considerably better than standard neural network implementations.     

The area under the ROC curve as a measure of clustering quality

Data Mining and Knowledge Discovery - The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain....     

An easy measure of compactness for 2D and 3D shapes

An easy measure of compactness for 2D (two dimensional) and 3D (three dimensional) shapes composed of pixels and voxels, respectively, is presented. The work proposed here is based on the two previous works of the measure of discrete compactness [E. Bribiesca, Measuring 2-D shape compactness using the contact perimeter, Comput. Math. Appl. 33 (1997) 1–9; E. Bribiesca, A measure of compactness for 3D shapes, Comput. Math. Appl. 40 (2000) 1275–1284]. The measure of compactness proposed here improves and simplifies the previous measure of discrete compactness. Now, using this proposed measure of compactness, it is possible to compute measures for any kind of object including porous and fragmented objects. Also, the computation of the measures is very simple by means of the use of only one equation. The measure of compactness proposed here depends in large part on the sum of the contact perimeters of the side-connected pixels for 2D shapes or on the sum of the contact surface areas of the face-connected voxels for 3D shapes. Relations between the perimeter and the contact perimeter for 2D shapes and between the area of the surface enclosing the volume and the contact surface area, are presented.The measure presented here of compactness is invariant under translation, rotation, and scaling. In this work, the term of compactness does not refer to point-set topology, but is related to intrinsic properties of objects. Finally, in order to prove our measure of compactness, we calculate the measures of discrete compactness of different objects. Also, we present an important application for brain structures quantification by means of the use of the new proposed measure of discrete compactness.     

Identification of multiscale spatio-temporal dynamical systems using a wavelet multiresolution analysis

In this article, a new algorithm for the multiscale identification of spatio-temporal dynamical systems is derived. It is shown that the input and output observations can be represented in a multiscale manner based on a wavelet multiresolution analysis. The system dynamics at some specific scale of interest can then be identified using an orthogonal forward least-squares algorithm. This model can then be converted between different scales to produce predictions of the system outputs at different scales. The method can be applied to both multiscale and conventional spatio-temporal dynamical systems. For multiscale systems, the method can generate a parsimonious and effective model at a coarser scale while considering the effects from finer scales. Additionally, the proposed method can be used to improve the performance of the identification when the measurements are noisy. Numerical examples are provided to demonstrate the application of the proposed new approach.     

Panchromatic sharpening of remote sensing images using a multiscale Kalman filter

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.     

Finite element model updating of honeycomb sandwich plates using a response surface model and global optimization technique

Honeycomb sandwich plates are used widely in the aerospace industry. Building accurate finite element models of honeycomb sandwich plates is necessary for analyzing and optimizing the microvibration that occurs in spacecraft. This study investigated two types of finite element dynamic models of honeycomb plates: a sandwich shell model and a shell-volume-shell model. Two response surface model-based optimization methods and a particle swarm optimization method were compared for updating the finite element models. Finally, we validated the accuracy of the two optimized honeycomb sandwich plate finite element dynamic models by comparing the results obtained by the frequency response functions with experimental data.