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This paper presents a novel 3D partial shape retrieval algorithm based on time-series analysis. Given a piece of a 3D shape, the proposed method encodes the shape descriptor given by the Heat Kernel Signature (HKS) as a time-series, where the time is considered an ordered sequence of vertices provided by the Fiedler vector. Finally, a similarity metric is created using a well-known tool in time-series...
Research on iris recognition have observed that iris texture has inherent radial correlation. However, currently, there lacks a deeper insight into iris textural correlation. Few research focus on a quantitative and comprehensive analysis on this correlation. In this paper, we perform a quantitative analysis on iris textural correlation. We employ steering kernels to model the textural correlation...
Many real-world graphs, such as those that arise from the web, biology and transportation, appear random and without a structure that can be exploited for performance on modern computer architectures. However, these graphs have a scale-free graph topology that can be leveraged for locality. Existing sparse data formats are not designed to take advantage of this structure. They focus primarily on reducing...
Heat Kernel Signature (HKS) is a powerful tool for shape analysis for its multi useful properties and has been successfully used in many correspondence tasks. However, the shape's feature detection is an empirical way since HKS depends on the time scale, which is not the intrinsic property of the shape. In order to eliminate the effects of time ambiguity, a novel HKS based feature extraction algorithm...
Quality of food and agricultural products is vital for farmers and consumers. Quality based classification of these products is being carried out manually in the industry which is tedious and expensive. Computer Vision systems can be used to automate the classification process. Automation can reduce the production cost and improve the overall quality. A computer vision system captures the image of...
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Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge-based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined...
This paper puts forward a new tracking algorithm based on Mean Shift algorithm and the Particle Filter algorithm. We combined the two algorithms efficiently based on the open structure system of both Mean Shift algorithm and the Particle Filter algorithm, and the model similar expression of establishment of the target, similarity measure and the selection of kernel function they have. The new algorithm...
To achieve the effective plant leaf classification using manifold learning, the local geometry structure of plant leaves is able to be preserved effectively and a discriminant manifold-based projection should be learned to capture the dominant structure features better. We firstly use Gabor filter to model the texture of plant leaf images as the samples. Then for the high-dimensional features, we...
Predictive maintenance task is of crucial role for any plant equipment supervision and scheduling of service activities. For this purpose it should be known what is current aging status of any equipment. Presented approach assumes that we know the nominal (starting) element curve and a damage one as well. It is also assumed that the aging course progresses according to some good practice aging Lorentz...
This work aims to improve the accuracy of the SVDD-based Intrusion Detection Systems. In this study we are interested by approaches using only one-class classification, namely the class of normal user sessions. Sessions are modeled by vectors of points in a finite features space. The goal of using the SVDD in anomaly detection is to find the hypersphere with a minimal volume that encloses the entire...
The field of medical image analysis has grown and these advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support. This paper presents a simple yet efficient image processing approach by proposing a new image feature detector...
The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC)...
Previously, a few bidirectional reflectance distribution function (BRDF) archetypes were distilled from the routine MODIS BRDF product for capturing the major variability of anisotropic reflectance of a large number of land surfaces from MODIS, based on the RossThick-LiSparseReciprocal (RTLSR) model. Since the routine RTLSR BRDF model tends to underestimate the hotspot effect, the resulting hotspot...
Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations...
Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error,...
An adaptive learning algorithm for Radial Basis Functions Neural Networks, RBFNNs, is provided. In recent years, RBFs have been subject to extensive areas of interests. But the setting up of RBFs in a network architecture can be time consuming, computationally deficient and unstable. Thus we have developed an efficient adaptive algorithm in a feedforward neural architecture in which the hidden neurons...
In the product life cycle, design reuse can save cost and improve existing products conveniently in most new product development. To retrieve similar models from big database, most search algorithms convert CAD model into a shape descriptor and compute the similarity two models according to a descriptor metric. This paper proposes a new 3D shape matching approach by matching the coordinates directly...
Shape-specific points are special data points invariant to translation, scaling, and rotation. The radius weighted mean (RWM) and the system center are two examples of shape-specific points. These points feature in contour registration, color quantization, and the detection of rotationally symmetric shape orientations. This study uses shape-specific points to cluster nonlinearly separable data into...
We investigate electromagnetic fields and emission thresholds of two-dimensional dielectric microcavities solving the Lasing Eigenvalue Problem (LEP). We propose a new convenient formulation of LEP as a nonlinear spectral problem for a Fredholm holo-morphic operator-valued function and solve LEP for microcavities of arbitrary shape with active regions. We reduce the original problem to the system...
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