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There is an increased need for coupling machine descriptions to various fusion physics codes. We present a computer aided design (CAD) service library that interfaces geometrical data requested by fusion physics codes in completely programmatic way for use in scientific workflow engines. Fusion codes can request CAD geometrical data at different levels of details (LOD) and control major assembly parameters...
We present a novel local shape descriptor by means of General Adaptive Neighborhoods (GANs) based on the properties of the heat diffusion process on a Riemannian manifold. The GAN is a spatial region, surrounding the feature point and fitting its local shape structure, which is isometric. Our signature, called the Heat Propagation Contours (HPCs), is obtained by analysing the well-known heat kernel...
We propose a novel univariate time series decomposition algorithm to partition temporal sequences into homogeneous segments. Unlike most existing temporal segmentation approaches, which generally build statistical models of temporal observations and then detect change points using inference or hypothesis testing techniques, our algorithm requires no domain knowledge, is insensitive to the choice of...
A method for spotting specific words in sign language video is proposed. In classes and talks given using Japanese Sign Language, words that do not have a defined sign, such as the names of people, objects, and places, are represented by sets of multiple characters from the Japanese finger alphabet. The difficulty of recognizing these words has created strong demand for the ability to spot specific...
The present work proposes a review and comparison of different Nonlocal Means (NLM) methods in the task of digital image filtering. Some different alternatives to change the classical exponential kernel function used in NLM methods are explored. Moreover, some approaches that change the geometry of the neighborhood and use dimensionality reduction of the neighborhood or patches onto principal component...
Reconstructing missing areas of arbitrary shape and size is particularly important in error-prone communication as well as in applications where motion compensation is conducted such as multi-image super-resolution or framerate up-conversion. To that end, frequency selective extrapolation is an effective image reconstruction technique. This approach was originally designed for block losses and has...
Support Vector Machine (SVM) is a powerful classifier used widely in textual and web classification. It tries to find an hyperplane that separates positive and negative data, maximizes the margin. SVM is a classifier that is based on a kernel whose choice is very critical. We propose in this paper an implicit links based Gaussian kernel that uses an implicit links based distance. This kernel helps...
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we introduce Geodesic Convolutional Neural Networks (GCNN), a generalization of the convolutional neural networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar...
This paper introduces a novel method to conserve the shape of smoke simulation based on fast Fourier transform. Through the advection step of simulating Navier-Stokes equation, semi-Lagrange method loses the high frequency part of fluid, since the interpolation method is equal to low-pass filter, which causes the shape of fluids variable in different resolution. The method consists of the dissipation...
Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) [1]. However, in this paper, the main focuses on classification that we propose a concise and provably factorial method...
Alzheimer disease is a chronic neurodegenerative disease that usually starts slowly and gets worse over time. The diagnosis of Alzheimer's disease is often made very late. Several years pass after the start of the first manifestations before the diagnosis is made. According to many researchers, roll back 5 years to the start of the disease would reduce the frequency of 50%. In this work, we propose...
Clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density...
Lost image areas with different size and arbitrary shape can occur in many scenarios such as error-prone communication, depth-based image rendering or motion compensated wavelet lifting. The goal of image reconstruction is to restore these lost image areas as close to the original as possible. Frequency selective extrapolation is a block-based method for efficiently reconstructing lost areas in images...
In image segmentation, the shape knowledge of the object may be used to guide the segmentation process. From a training set of representative shapes, a statistical model can be constructed and used to constrain the segmentation results. The shape space is usually constructed with tools such such as principal component analysis (PCA). However the main assumption of PCA that shapes lie a linear space...
The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification...
Different contacts between objects afford different interactions between them. For example, while contacts below an object can provide support, contacts on opposing sides can be used for pinching. Hence, a robot can learn to predict which interactions are currently afforded based on the set of contacts. However, representing sets of contacts is not trivial, as the number of contacts is not fixed nor...
Breast cancer has caused more and more attention in recent years since the mortality rate is increasing and age of onset is trend to be younger than before. Using computer vision technology for automatic classifying benign and masses malignant ones could assist doctors in diagnosing condition. However, the margins and shapes of masses are various and which are very similar with surrounding tissues,...
Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region...
In this paper, we propose a descriptor for Brachiopods classification by using a combination between curvature and Fourier descriptors. The curvature properties provide an apparently powerful cue to the underlying structure of the curve and captures completely the structure of planar curve. In addition, it is stable and complete. Fourier descriptors are powerful features for the recognition of two-dimensional...
Kernel graph cuts is one of the most efficient methods for image segmentation. However, kernel graph cuts for medical image segmentation without prior information is inefficient, especial for MRI tumor image segmentation. This paper presents a kernel graph cuts algorithm with deformable priors, which can successfully seize clinical MIR image features. The proposed networks for graph cuts are tailored...
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