Robust Non-Rigid Registration with Reweighted Position and Transformation Sparsity

Non-rigid registration is challenging because it is ill-posed with high degrees of freedom and is thus sensitive to noise and outliers. We propose a robust non-rigid registration method using reweighted sparsities on position and transformation to estimate the deformations between 3-D shapes. We formulate the energy function with position and transformation sparsity on both the data term and the smoothness term, and define the smoothness constraint using local rigidity. The double sparsity based non-rigid registration model is enhanced with a reweighting scheme, and solved by transferring the model into four alternately-optimized subproblems which have exact solutions and guaranteed convergence. Experimental results on both public datasets and real scanned datasets show that our method outperforms the state-of-the-art methods and is more robust to noise and outliers than conventional non-rigid registration methods.Comment: IEEE Transactions on Visualization and Computer Graphic

Global alignment of deformable objects captured by a single RGB-D camera

We present a novel global registration method for deformable objects captured using a single RGB-D camera. Our algorithm allows objects to undergo large non-rigid deformations, and achieves high quality results without constraining the actor's pose or camera motion. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed model. To attack large scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than the state-of-the-art methods

Global 3D non-rigid registration of deformable objects using a single RGB-D camera

We present a novel global non-rigid registration method for dynamic 3D objects. Our method allows objects to undergo large non-rigid deformations, and achieves high quality results even with substantial pose change or camera motion between views. In addition, our method does not require a template prior and uses less raw data than tracking based methods since only a sparse set of scans is needed. We compute the deformations of all the scans simultaneously by optimizing a global alignment problem to avoid the well-known loop closure problem, and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed shapes, especially near open boundaries of scans. To cope with large-scale problems, we design a coarse-to-fine multi-resolution scheme, which also avoids the optimization being trapped into local minima. The proposed method is evaluated on public datasets and real datasets captured by an RGB-D sensor. Experimental results demonstrate that the proposed method obtains better results than several state-of-the-art methods

SHREC'20: Shape correspondence with non-isometric deformations

Estimating correspondence between two shapes continues to be a challenging problem in geometry processing. Most current methods assume deformation to be near-isometric, however this is often not the case. For this paper, a collection of shapes of different animals has been curated, where parts of the animals (e.g., mouths, tails & ears) correspond yet are naturally non-isometric. Ground-truth correspondences were established by asking three specialists to independently label corresponding points on each of the models with respect to a previously labelled reference model. We employ an algorithmic strategy to select a single point for each correspondence that is representative of the proposed labels. A novel technique that characterises the sparsity and distribution of correspondences is employed to measure the performance of ten shape correspondence methods

Potentiality Evaluation for Revegetation of abandoned lands from coal mining activities based on Support Vector Machine

Activities of excavating coal exscind natural vegetation and deposit stone on the natural land that modified the natural land contribute to waste farmlands. Additionally, pollution of environment, losses of human life, human settlements and the infrastructure are also rising, which certainly demands urgent attention. Revegetation had been considered as a kind of cost-effective means. A reasonable potentiality for Revegetation of abandoned lands from coal mining activities is benefit for planning of Revegetation. In this paper, 34 instances were investigated, and seven attributes such as slope angle, elevation, topographic wetness index, lineaments, geological formations, soil types, condition of traffic, correlative with abandoned lands were recorded. A potentiality evaluation method based on support vector machine was proposed and was tested on those data. The purpose of SVM proposed in this paper is to construct a model that suggests target value of data instances in the testing set using only the given attributes. We randomly select 22 data instances to construct the SVM model. Testing is made by rest data. The results show SVM the good performance of potentiality evaluation with RBF kernel. it manages to achieve 95% success on the training set and 75% success on the testing set. Experiments performed also show that the performance of this method is mostly superior to that of artificial neural networks and SVM can be employed as an efficient method for evaluating the revegetation potentiality of abandoned lands from coal mining activities.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlIFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2163, Kobe, JapanSymposium 3, Session 8 : Intelligent Information Technology and Applications Computational Intelligence (2

AcdS gene of Bacillus cereus enhances salt tolerance of seedlings in tobacco (Nicotiana tabacum L.)

AbstractPrevious studies show that 1-aminocyclopropane-1-carboxylate (ACC) deaminase can facilitate the growth and stress tolerance of hosts by reducing ethylene levels. In this study, the acdS gene encoding ACC deaminase from Bacillus cereus (HK012) was cloned and transformed into tobacco (Nicotiana tabacum L.) by the leaf disc method using Agrobacterium. Molecular detection and physiological analysis of the transgenic tobacco plants were performed. Our results showed the acdS gene was integrated into the tobacco genome and fluorescence microscopy showed that the fusion protein was located on the cell membrane of tobacco root. Compared with control, the transgenic plants showed increases in plant height, root length, dry weight, fresh weight and chlorophyll content; and significant increases in the concentration of proline of 55.15% and 42.7% under salt stress conditions (150 mmol L−1 and 300 mmol L−1 NaCl, respectively). The superoxide dismutase, peroxidase, catalase and ACC deaminase activities of transgenic tobacco were higher than those of control tobacco at 150 and 300 mmol L−1 salt concentrations. Transgenic tobacco seedlings expressing the acdS gene of B. cereus HK012 showed higher salt tolerance than the control plants. The obtained results suggest that the acdS gene of B. cereus can be used to promote salt tolerance in glycophytes by using biotechnology strategies

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness

In this study, a hyperspectral imaging system of 866.4–1701.0 nm, combined with a variety of spectral processing methods were adopted to identify shrimp freshness. To gain the optimal model combination, three preprocessing methods (Savitzky-Golay first derivative (SG1), multivariate scatter correction (MSC), and standard normal variate (SNV)), three characteristic wavelength extraction algorithms (random frog algorithm (RFA), uninformative variables elimination (UVE), and competitive adaptive reweighted sampling (CARS)), and four discriminant models (partial least squares discrimination analysis (PLS-DA), least squares support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM)) were employed for experimental study. First of all, due to the full wavelength modeling analysis, three preprocessing methods were utilized to preprocess the original spectral data. The analysis showed that the spectral data processed by the SNV method had the best performance among the four discriminant models. Secondly, due to the characteristic wavelength modeling analysis, three characteristic wavelength extraction algorithms were utilized to extract the characteristic wavelength of the SNV-processed spectral data. It was found that the CARS algorithm achieved the best performance among the three characteristic wavelength extraction algorithms, and the combining adoption of the ELM model and different characteristic wavelength extraction algorithms obtained the best results. Therefore, the model based on SNV-CARS-ELM obtained the best performance and was elected as the optimal model. Lastly, for accurately and explicitly displaying the refrigeration days of shrimps, the original hyperspectral images of shrimps were substituted into the SNV-CARS-ELM model, thus obtaining the general classification accuracy of 97.92%, and the object-wise method was used to visualize the classification results. As a result, the method proposed in this study can effectively detect the freshness of shrimps