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This paper proposes the design of antipodal Vivaldi antennas using the kernel regression method. The kernel regression is applied for training a cost function model to predict the next sample with improved cost values, and the information of the predicted sample is employed to re-train the model. This process is repeated until the cost value converges to our design goal. The shapes of the tapered...
This paper presents a novel framework for brain tumor diagnosis and its grade classification based on higher order statistical texture features namely kurtosis and skewness along with selected morphological features. These features were extracted from segmented tumorous T2-weighted brain MR images, in order to distinguish high grade (HG) tumor from low grade (LG) tumor. Tumor classification is carried...
The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple...
The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center...
Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose...
This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
The recently proposed clustering algorithm based on density peaks is reported to generate very good clustering results. This algorithm is simple and efficient, and can be used to generate clusters of arbitrary shapes. However, the performance of this algorithm relies on the selection of the kernel in local density calculation. The original density peak based algorithm uses the cutoff kernel and Gaussian...
We present a novel method for training (evolving) fully convolutional neural networks (CNNs) for deformable object manipulation. Instead of using a weight update rule, we evolve an ensemble of compositional pattern generating networks (CPPNs) by means of a genetic algorithm (GA). These ensembles generate the convolutional kernels that comprise the CNN. This allows the GA to search for fit kernels...
For many intensive computing tasks, simultaneous data access into multi-dimensional data arrays is highly restricted by its data mapping strategy and memory port constraint. As such, to increase memory accessing bandwidth, innovative memory partitioning and mapping algorithms have been proposed to simultaneously access multiple memory blocks through physically distributing data elements in the same...
Graph kernels are powerful tools for structural analysis in computer vision. Unfortunately, most existing state-of-the-art graph kernels ignore the locational or structural correspondence information between graphs, based on the visual background. This drawback influences the performance of existing kernels for computer vision based classification problems, e.g., classification of shapes, point clouds...
We present a novel approach to the computation of dense correspondence maps between shapes in a non-rigid setting. The problem is defined in terms of functional correspondences. We deal with the non-injectivity of the solution of the functional map framework due to the under-determinedness of the original problem. Key to our approach is the injectivity constraint plugged directly into the problem...
Categorical description of leaf shapes is of paramount importance in agriculture and plant sciences. Traditionally, these descriptions have been based on categorical systems proposed by domain experts. Despite the importance of these visual descriptive systems, these approaches may be limited by the representation of unknown shapes as expected in exploratory domains. In this work, we propose a novel...
Support vector clustering (SVC) is a versatile clustering technique that is able to identify clusters of arbitrary shapes by exploiting the kernel trick. However, one hurdle that restricts the application of SVC lies in its sensitivity to the kernel parameter and the trade-off parameter. Although many extensions of SVC have been developed, to the best of our knowledge, there is still no algorithm...
Statistical shape models have become a widely used tool in computer vision and medical image analysis where they are of considerable interest when studying shape variations in anatomical shapes. The objective of this article is to build a 3D statistical shape modeling for a given data; the implemented process goes through those basic steps, first collect the given data then apply the alignment algorithm...
Area integral invariant (AII) is a functional obtained by performing integral operations on the closed planar contour of a shape via the convolution with disc kernels. This shape descriptor is insensitive to noise and robust with respect to occlusions. AII intrinsically introduces the notion of scale using the size of kernel radius. However how to select an optimal scale remains unresolved. In this...
With the increasing availability of multi-view nonnegative data in practical applications, multi-view learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, previous works are either difficult to generate meaningful clustering results in terms of views with heterogeneous quality, or sensitive to noise. To address these problems, we propose a co-regularized...
Analysis of near-infrared images has a possibility to simply find vein disease. If super-resolution (SR) techniques improve the quality of near-infrared images with a low signal-to-noise ratio, they could detect abnormal veins at an early stage. Deep convolutional neural networks (DCNNs) as a SR technique were applied to downgraded images, and the effectiveness was investigated. The DCNNs with the...
Segmentation of cell nuclei is an important step towards automatic analysis of microscopic images. This paper presents an automated technique for nuclear segmentation in skin histopathological images. The proposed technique first detects nuclear seeds using a bank of generalized Laplacian of Gaussian (gLoG) kernels. Based on the detected nuclear seeds, a multi-scale radial line scanning (mRLS) method...
A technique is presented to algorithmically evaluate prompt gamma neutron activation spectra, which were produced through excitation of specific material samples. The excitation is done with a neutron generator that provides a switchable, artificial form of neutron radiation. To evaluate the spectra, a extension to prior peak based analysis methods is proposed that dynamically incorporates the detector...
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