Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space

This paper proposes a novel texture segmentation approach using independent-scale component-wise Riemannian-covariance Gaussian mixture model (ICRGMM) in Kullback-Leibler (KL) measure based multi-scale nonlinear structure tensor (MSNST) space. We use the independent-scale distribution and full-covariance structure to replace the covariant-scale distribution and 1D-variance structure used in our previous research. To construct the optimal full-covariance structure, we define the full-covariance on KL, Euclidean, log-Euclidean, and Riemannian gradient mappings, and compare their performances. The comparison experiments demonstrate that the Riemannian gradient mapping leads to its optimum properties over other choices when constructing the full-covariance. To estimate and update the statistical parameters more accurately, the component-wise expectation-maximization for mixtures (CEM²) algorithm is proposed instead of the originally used K-means algorithm. The superiority of the proposed ICRGMM has been demonstrated based on texture clustering and Graph Cuts based texture segmentation using a large number of synthesis texture images and real natural scene textured images, and further analyzed in terms of error ratio and modified F-measure, respectively.     

Gender discriminating models from facial surface normals

In this paper, we show how to use facial shape information to construct discriminating models for gender classification. We represent facial shapes using 2.5D fields of facial surface normals, and investigate three different methods to improve the gender discriminating capacity of the model constructed using the standard eigenspace method. The three methods are novel variants of principal geodesic analysis (PGA) namely (a) weighted PGA, (b) supervised weighted PGA, and (c) supervised PGA. Our starting point is to define a weight map over the facial surface that indicates the importance of different locations in discriminating gender. We show how to compute the relevant weights and how to incorporate the weights into the 2.5D model construction. We evaluate the performance of the alternative methods using facial surface normals extracted from 3D range images or recovered from brightness images. Experimental results demonstrate the effectiveness of our methods. Moreover, the classification accuracy, which is as high as 97%, demonstrates the effectiveness of using facial shape information for gender classification.     

Supervised relevance maps for increasing the distinctiveness of facial images

This paper shows how to improve holistic face analysis by assigning importance factors to different facial regions (termed as face relevance maps). We propose a novel supervised learning algorithm for generating face relevance maps to improve the discriminating capability of existing methods. We have successfully applied the developed technique to face identification based on the Eigenfaces and Fisherfaces methods, and also to gender classification based on principal geodesic analysis (PGA). We demonstrate how to iteratively learn the face relevance map using labelled data. Experimental results confirm the effectiveness of the developed approach.     

A parallel two-level method for simulating blood flows in branching arteries with the resistive boundary condition

Computer modeling of blood flows in the arteries is an important and very challenging problem. In order to understand, computationally, the sophisticated hemodynamics in the arteries, it is essential to couple the fluid flow and the elastic wall structure effectively and specify physiologically realistic boundary conditions. The computation is expensive and the parallel scalability of the solution algorithm is a key issue of the simulation. In this paper, we introduce and study a parallel two-level Newton–Krylov–Schwarz method for simulating blood flows in compliant branching arteries by using a fully coupled system of linear elasticity equation and incompressible Navier–Stokes equations with the resistive boundary condition. We first focus on the accuracy of the resistive boundary condition by comparing it with the standard pressure type boundary condition. We then show the parallel scalability results of the two-level approach obtained on a supercomputer with a large number of processors and on problems with millions of unknowns.     

Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

The approach of using primarily satellite observations to estimate ecosystem gross primary production (GPP) without resorting to interpolation of many surface observations has recently shown promising results. Previous work has shown that the remote sensing based greenness and radiation (GR) model can give accurate GPP estimates in crops. However, the feasibility of its application and the model calibration to other ecosystems remain unknown. With the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images and the surface based estimates of photosynthetically active radiation (PAR), we provide an analysis of the GR model for estimating monthly GPP using flux measurements at fifteen sites, representing a wide range of ecosystems with various canopy structures and climate characteristics. Results demonstrate that the GR model can provide better estimates of GPP than that of the temperature and greenness (TG) model for the overall data classified as non-forest (NF), deciduous forest (DF) and evergreen forest (EF) sites. Calibration of the GR model is also conducted and has shown reasonable results for all sites with a root mean square error of 47.18 g C/m²/month. Different coefficients acquired for the three plant functional types indicate that there are shifts of importance among various factors that determine the monthly vegetation GPP. The analysis firstly shows the potential use of the GR model in estimating GPP across biomes while it also points to the needs of further considerations in future operational applications.     

An extended iterative format for the progressive-iteration approximation

Progressive-iteration approximation (PIA) is a new data fitting technique developed recently for blending curves and surfaces. Taking the given data points as the initial control points, PIA constructs a series of fitting curves (surfaces) by adjusting the control points iteratively, while the limit curve (surface) interpolates the data points. More importantly, progressive-iteration approximation has the local property, that is, the limit curve (surface) can interpolate a subset of data points by just adjusting a part of corresponding control points, and remaining others unchanged. However, the current PIA format requires that the number of the control points equals that of the data points, thus making the PIA technique inappropriate to fitting large scale data points. To overcome this drawback, in this paper, we develop an extended PIA (EPIA) format, which allows that the number of the control points is less than that of the given data points. Moreover, since the main computations of EPIA are independent, they can be performed in parallel efficiently, with storage requirement O(n), where n is the number of the control points. Therefore, due to its local property and parallel computing capability, the EPIA technique has great potential in large scale data fitting. Specifically, by the EPIA format, we develop an incremental data fitting algorithm in this paper. In addition, some examples are demonstrated in this paper, all implemented by the parallel computing toolbox of Matlab, and run on a PC with a four-core CPU.     

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R² greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R² greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.     

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of today's climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.     

Suitability evaluation of urban construction land based on geo-environmental factors of Hangzhou, China

Suitability evaluation of urban construction land based on geo-environmental factors is the process of determining the fitness of a given tract of land for construction. This process involves a consideration of the geomorphology, geology, engineering geology, geological hazards, and other geological factors and is the basis of urban construction land planning and management. With the support of Geographic Information Systems (GIS), grid analysis, and geo-spatial analysis techniques, four factor groups comprising nine separate subfactors of geo-environmental attributes were selected to be used in the evaluation of the suitability level for construction land in Hangzhou. This was based on K-means clustering and back-propagation (BP) neural network methods due to their advantages in fast computing, unique adaptive capacity, and self-organization. Simultaneously, the evaluation results based on K-means clustering and BP neural network were compared and analyzed, and the accuracy evaluation was set. The results showed that the geo-environmental suitability evaluation results of construction land based on K-means clustering and BP neural network were similar in terms of the distribution and scale of construction land suitability level. At the same time, the results of the two evaluation methods were consistent with the variability in suitability level, engineering geology, and hydrogeology of Hangzhou. The results also showed that the real advantage of the methods proposed in this paper lies in their capacity to streamline the mapping process and to ensure that the results are consistent throughout. The suitability level of the urban construction land based on the geo-environment in Hangzhou was divided into four construction sites: land for building super high-rise and high-rise buildings, land for building multistorey buildings, land for low-rise buildings, and nonbuilding land. The results of the suitability evaluation for each category will provide a scientific basis for decision-making in urban development in Hangzhou.     

A new paradigm for urban surveillance with vehicular sensor networks

We consider a new application paradigm of vehicular sensor networks (VSN). Currently, vehicles are equipped with forward facing cameras to assist forensic investigations of events by proactive image-capturing from streets and roads. Due to content redundancy and storage imbalance in this in-network distributed storage system, how to maximize its storage capacity becomes a nontrivial challenge. In other words, how to maximize the average lifetime of sensory data (i.e., images generated by cameras) in the network is a fundamental problem to be solved. This paper presents, VStore, a cooperative storage solution in vehicular sensor networks for mobile surveillance, which has been designed to support redundancy elimination and storage balancing throughout the network. Compared with existing works, we propose a novel storage architecture for urban surveillance and deal with challenges in a mobile scenario. Field testing was carried out with a trace-driven simulator, which utilized about 500 taxis in Shanghai. The testing results showed that VStore can largely prolong the average lifetime of sensory data by cooperative storage.