Feature Preserving Parameterization for Quadrilateral Mesh Generation Based on Ricci Flow and Cross Field

We propose a newmethod to generate surface quadrilateralmesh by calculating a globally defined parameterization with feature constraints.In the field of quadrilateral generation with features,the cross field methods are wellknown because of their superior performance in feature preservation.The methods based on metrics are popular due to their sound theoretical basis,especially the Ricci flow algorithm.The cross field methods’major part,the Poisson equation,is challenging to solve in three dimensions directly.When it comes to cases with a large number of elements,the computational costs are expensive while the methods based on metrics are on the contrary.In addition,an appropriate initial value plays a positive role in the solution of the Poisson equation,and this initial value can be obtained from the Ricci flow algorithm.So we combine the methods based on metric with the cross field methods.We use the discrete dynamic Ricci flow algorithm to generate an initial value for the Poisson equation,which speeds up the solution of the equation and ensures the convergence of the computation.Numerical experiments show that our method is effective in generating a quadrilateral mesh for models with features,and the quality of the quadrilateral mesh is reliable.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications.Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension.The classification accuracy of hyperspectral images(HSI)increases significantly by employing both spatial and spectral features.For this work,the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared(VNIR)range of 400 to 1000 nm wavelength within 180 spectral bands.The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel.The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system.In this study,a unique pixel-based approach was designed to improve the crops'classification accuracy by using the edge-preserving features(EPF)and principal component analysis(PCA)in conjunction.The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI.In the second step,this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information.The resultant feature space(PCA-EPF)demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost.The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF.The classification performance evaluation was measured in terms of individual class accuracy,overall accuracy,average accuracy,and Cohen kappa factor.The proposed scheme achieved greater than 90%results for all the performance evaluation metrics.The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range.The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.

Feature-preserving color pencil drawings from photographs

Color pencil drawing is well-loved due to its rich expressiveness.This paper proposes an approach for generating feature-preserving color pencil drawings from photographs.To mimic the tonal style of color pencil drawings,which are much lighter and have relatively lower saturation than photographs,we devise a lightness enhancement mapping and a saturation reduction mapping.The lightness mapping is a monotonically decreasing derivative function,which not only increases lightness but also preserves input photograph features.Color saturation is usually related to lightness,so we suppress the saturation dependent on lightness to yield a harmonious tone.Finally,two extremum operators are provided to generate a foreground-aware outline map in which the colors of the generated contours and the foreground object are consistent.Comprehensive experiments show that color pencil drawings generated by our method surpass existing methods in tone capture and feature preservation.

Towards uniform point distribution in feature-preserving point cloud filtering

While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.

Feature Preserving Mesh Simplification Using Feature Sensitive Metric

We present a new method for feature preserving mesh simplification based on feature sensitive （FS） metric. Previous quadric error based approach is extended to a high-dimensional FS space so as to measure the geometric distance together with normal deviation. As the normal direction of a surface point is uniquely determined by the position in Euclidian space, we employ a two-step linear optimization scheme to efficiently derive the constrained optimal target point. We demonstrate that our algorithm can preserve features more precisely under the global geometric properties, and can naturally retain more triangular patches on the feature regions without special feature detection procedure during the simplification process. Taking the advantage of the blow-up phenomenon in FS space, we design an error weight that can produce more suitable results. We also show that Hausdorff distance is markedly reduced during FS simplification.

Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections

Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.

Feature Preserving Milli-Scaling of Large Format Visualizations

Ultra-scale data analysis has created many new challenges for visualization. For example, in climate research with two-dimensional time-varying data, scientists find it crucial to study the hidden temporal relationships from a set of large scale images, whose resolutions are much higher than that of general computer monitors. When scientists can only visualize a small portion （〈 1/1000） of a time step at one time, it is extremely challenging to analyze the temporal features from multiple time steps. As this problem cannot be simply solved with interaction or display technologies, this paper presents a milli-scaling approach by designing downscaling algorithms with significant ratios. Our approach can produce readable-sized images of multiple ultra-scale visualizations, while preserving important data features and temporal relationships. Using the climate visualization as the testing application, we demonstrate that our approach provides a new tool for users to effectively make sense of multiple, arge-format visualizations

Laplacian-based 3D mesh simplification with feature preservation

We propose a novel Laplacian-based algorithm that simplifies triangle surface meshes and can provide different preservation ratios of geometric features.Our efficient and fast algorithm uses a 3D mesh model as input and initially detects geometric features by using a Laplacian-based shape descriptor(L-descriptor).The algorithm further performs an optimized clustering approach that combines a Laplacian operator with K-means clustering algorithm to perform vertex classification.Moreover,we introduce a Laplacian weighted cost function based on L-descriptor to perform feature weighting and error statistics comparison,which are further used to change the deletion order of the model elements and preserve the saliency features.Our algorithm can provide different preservation ratios of geometric features and may be extended to handle arbitrary mesh topologies.Our experiments on a variety of 3D surface meshes demonstrate the advantages of our algorithm in terms of improving accuracy and applicability,and preserving saliency geometric features.