The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired...
We present an automatic method for fitting multiple B-spline curves to unorganized planar points. The method works on point clouds which have complicated topological structures and a single curve is insufficient for fitting the shape. A divide-and-merge algorithm is developed for dividing the unorganized data points into several groups while each group represents a smooth curve. Each point group is...
An incremental clustering technique to partition 3D point clouds into planar regions is presented in this paper. The algorithm works in real-time on unknown and noisy data, without any initial assumption. An iterative cluster growing technique is proposed in order to correctly classify a flow of 3D points and to merge close regions. The computational efficiency of the approach is achieved by using...
Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each object with hundreds of associated parameters. Exploration of this very high-dimensional data space poses a huge challenge. Subspace clustering is one among several approaches which have been proposed for this purpose in recent...
In this paper we present a new subspace clustering algorithm TGSCA for large dataset with noise. Experiments show that TGSCA can discover clusters both on entire space and subspace; the computation complexity is proximate linear with object's number, space dimension, and clusters' dimension respectively; it is not sensitive to noise; it can find both disjoint clusters or overlap clusters; it can find...
This paper presents a method for capture planning in view based 3D recognition. Views are represented by their contours, encoded into curvature functions, which are reduced into compact feature vectors by Principal Component Analysis. These vectors are very resistant against transformations, so they can be assumed to be distributed over the surface of a sphere with the object in its center. After...
We propose a robust registration method for range images under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, they are promising to range image registration of objects lacking in discriminative geometric features under variable illumination. In our method, we use adaptive regions to model the local distribution of reflectance, which enables...
Using the intensity of the element in interest, standard FCM generates the membership values to all classes. When used for segmentation of images, this method is not capable of correcting the effects of noise. To overcome that problem, we propose a modification on the standard method. The voxels in the neighborhood are taken into account, forming the shape elements in addition to the intensity of...
This paper proposes a new robust 3-D object blind watermarking method using constraints in the spectral domain. Mesh watermarking in spectral domain has the property of spreading the information in unpredictable ways, thus increasing the security of the watermark. In the proposed method, firstly, the Laplacian matrix of the graphical object mesh is eigen-decomposed. The coefficients corresponding...
Active shape model (ASM) statistically represents a shape by a set of well-defined landmark points and models object variations using principal component analysis (PCA). However, the extracted shape contour modeled by PCA is still unsmooth when the shape has a large variation compared with the mean shape. In this paper, we propose a regularized ASM (R-ASM) model for shape alignment. During training...
Nuclear magnetic resonance spectroscopy is a technique for the analysis of complex biochemical materials. Thereby the identification of known sub-patterns is important. These measurements require an accurate preprocessing and analysis to meet clinical standards. Here we present a method for an appropriate sparse encoding of NMR spectral data combined with a fuzzy classification system allowing the...
Statistical shape modelling is a technique whereby the variation of shape across the population is modelled by principal component analysis (PCA) on a set of sample shape vectors. The number of principal modes retained in the model (PCA dimension) is often determined by simple rules, for example choosing those cover a percentage of total variance. We show that this rule is highly dependent on sample...
Periodicity is at the core of the recognition of many actions. This paper takes the following steps to detect and measure periodicity. 1) We establish a conceptual framework of classifying periodicity in 10 essential cases, the most important of which are flashing (of a traffic light), pulsing (of an anemone), swinging (of wings), spinning (of a swimmer), turning (of a conductor), shuttling (of a...
During IC photomask vision inspection, considering problem that fine image defectpsilas fineness, complex shape, extraction feature difficultly, and effect by noise easily, presented defect identification classification algorithm based on PCA (principal components analysis) and SVM (support vector machine). It resolved the problem that fine and complex defect was difficult to classify, by merits of...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.