基于主动形状模型和K近邻算法的人脸脸型分类

针对人脸描述性脸型特征分类问题,提出一种新的基于主动形状模型和K近邻算法的脸型分类方法。根据主动形状模型方法定位得到的测试样本人脸边缘轮廓点,经归一化后以其围成区域面积作为人脸脸型特征。采用K近邻算法和面型指数实现测试图像的脸型分类。实验结果表明,该方法对人脸姿态变化有一定的鲁棒性,分类结果准确度高且脸型的分类符合人主观描述性判断。

Monitoring of Growth Parameters of Sweet Corn Using CGMD302 Spectrometer

[Objective] This study aimed to clarify the quantitative relationships between growth parameters of sweet corn at different growth stages and canopy spectral indices,thus providing references for rapid monitoring of growth parameters of sweet corn.[Method] Based on field experiments of Zhengtian 68 under different potassium application levels,canopy spectral reflectance was collected using CGMD302 spectrometer at jointing stage,big bell mouth stage and tasseling stage,respectively;plant height,stem diameter,and leaf area index（LAI） were measured,to investigate the relationships between various growth parameters and spectral indices.[Result] Normalized difference vegetation index（NDVI） was positively correlated with stem diameter and LAI;ratio vegetation index（RVI） was negatively correlated with stem diameter and LAI.The single-stage monitoring models established based on NDVI and RVI could retrieve effectively stem diameter and LAI of sweet corn with the prediction accuracy of higher than 0.9,root mean square error（RMSE） of less than 10%,average relative error（RE） of less than 5%.[Conclusion] This study provided a technical basis for rapid monitoring of growth parameters of sweet corn.

Quantitative Relationship between Leaf Area Index and Canopy Reflectance Spectra of Rice under Different Nitrogen Levels

Monitoring rice growth by spectral remote sensing technology can provide scientific basis for the high yield and efficient production of rice. Field experiments with different nitrogen application amounts using Tianyouhuazhan rice as test sam- ples were set up to study the relationship between rice leaf area index （LAI） and canopy reflectance spectral. The results showed that： the LAI increased with the amount of applied nitrogen; the canopy reflectance spectral showed significant re- sponse characteristics to groups with different nitrogen application levels; the corre- lation coefficient of LAI and canopy spectral reflectance reached the maximum at 720 nm red edge region. The mathematical model was constructed to predict the LAI according to the canopy reflectance spectra of rice.

A Methodology for Estimating Leaf Area Index by Assimilating Remote Sensing Data into Crop Model Based on Temporal and Spatial Knowledge

In this paper,a methodology for Leaf Area Index(LAI) estimating was proposed by assimilating remote sensed data into crop model based on temporal and spatial knowledge.Firstly,sensitive parameters of crop model were calibrated by Shuffled Complex Evolution method developed at the University of Arizona(SCE-UA) optimization method based on phenological information,which is called temporal knowledge.The calibrated crop model will be used as the forecast operator.Then,the Taylor′s mean value theorem was applied to extracting spatial information from the Moderate Resolution Imaging Spectroradiometer(MODIS) multi-scale data,which was used to calibrate the LAI inversion results by A two-layer Canopy Reflectance Model(ACRM) model.The calibrated LAI result was used as the observation operator.Finally,an Ensemble Kalman Filter(EnKF) was used to assimilate MODIS data into crop model.The results showed that the method could significantly improve the estimation accuracy of LAI and the simulated curves of LAI more conform to the crop growth situation closely comparing with MODIS LAI products.The root mean square error(RMSE) of LAI calculated by assimilation is 0.9185 which is reduced by 58.7% compared with that by simulation(0.3795),and before and after assimilation the mean error is reduced by 92.6% which is from 0.3563 to 0.0265.All these experiments indicated that the methodology proposed in this paper is reasonable and accurate for estimating crop LAI.

Effect of climate change on seasonal water use efficiency in subalpine Abies fabri

Abies fabri is a typical subalpine dark coniferous forest in southwestern China. Air temperature increases more at high elevation areas than that at low elevation areas in mountainous regions,and climate change ratio is also uneven in different seasons. Carbon gain and the response of water use efficiency(WUE) to annual and seasonal increases in temperature with or without CO_2 fertilization were simulated in Abies fabri using the atmospheric-vegetation interaction model(AVIM2). Four future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5(CMIP5) were selectively investigated. The results showed that warmer temperatures have negative effects on gross primary production(GPP) and net primary production(NPP) in growing seasons and positive effects in dormant seasons due to the variation in the leaf area index. Warmer temperatures tend to generate lower canopy WUE and higher ecosystem WUE in Abies fabri. However,warmer temperature together with rising CO_2 concentrations significantlyincrease the GPP and NPP in both growing and dormant seasons and enhance WUE in annual and dormant seasons because of the higher leaf area index(LAI) and soil temperature. The comparison of the simulated results with and without CO_2 fertilization shows that CO_2 has the potential to partially alleviate the adverse effects of climate warming on carbon gain and WUE in subalpine coniferous forests.

New Contour Detection Model Working on Gray-Scale Image of Blended Yarn Cross-Section

The traditional Contour Tracing algorithm works on the binary image. It is developed that a new model called Facula Diffusion which can work directly on gray-scaled images according to the principle of human vision. The diffusion operation is controlled by four factors including approximation, closing, length-limiting, and hit-rate. Based on this model, three shape indices, i. e., dimension index, abnormity index, and fluctuation index, were put forward to describe the shape of objects. The rule of shape indices selection was discussed subsequently. Finally, the fibers in polyester/cotton blended yam are classified and the blending ratio is determined.

Identification of Probable Type of Bituminous Mix Depending on Higher Combined Index of Stone Aggregates

The stone aggregates constitute over 90% of the mass of a pavement and hence its properties play a very vital role in design of satisfactory unbound as well as bituminous mixes. Also since the aggregates availability is continuously becoming scarce, attempts have to be made to identify use of some marginal as well as inferior aggregates without sacrificing the desirable qualities of resulting mixes. One of the most important properties therefore is the combined index of aggregates which deals with aggregates shape and is the sum of flakiness and elongation indices of aggregates measured related to their thickness and length respectively. The Ministry of Road Transport and Highways （MoRTH） specifies an upper limit of 30% combined index for different types of bituminous mixes. In the field, many times the aggregates supplied have combined index of more than 30%. A pre determined mix such as bituminous concrete, dense or semi dense bituminous macadam etc. has to be designed using such aggregates. It may be easy to design the mix but to get the aggregates with desirable combined index is often very difficult. In order to overcome this, in many projects, secondary crushers are used to crush the aggregates for the second time just to bring down the combined index. This obviously increases the project cost. It is quite possible that the aggregates with higher combined index may be suitable for a particular type of mix. It is therefore appropriate to study what type of a bituminous mix can be satisfactorily prepared depending on the availability of aggregates with generally higher values of combined index. Hence, it was attempted to determine the usual Marshall test properties of two most commonly used types of bituminous mixes namely bituminous concrete and dense bituminous macadam using aggregates with combined index 30%, 40% and 50%. It was found that aggregates with higher combined index are more suitable to design dense bituminous macadam mix than bituminous concrete.

Separating impacts of vegetation change and climate variability on streamflow using hydrological models together with vegetation data

Vegetation information is seldom considered in lumped conceptual rainfall-runoff models.This paper uses two modified rainfall-runoff models,the Xinanjiang-ET and SIMHYD-ET models in which vegetation leaf area index is incorporated,to investigate impacts of vegetation change and climate variability on streamflow in a Southern Australian catchment,the Crawford River experimental catchment,where Tasmanian blue gum plantations were introduced gradually from 1998 till 2005.The Xinanjiang-ET and SIMHYD-ET models incorporate remotely-sensed leaf area index(LAI) data obtained from the Advanced Very High Resolution Radiometer(AVHRR) on board NOAA polar orbiting satellites.Compared to the original versions,the Xinanjiang-ET and SIMHYD-ET models show marginal improvements in runoff simulations in the pre-plantation period(1882-1997).The calibrated Xinanjaing-ET and SIMHYD-ET models are then used to simulate plantation impact on streamflow in the post-plantation period.The total change in streamflow between the pre-plantation and post-plantation periods is 32.4 mm/a.The modelling results from the two models show that plantation reduces streamflow by 20.5 mm/a,and climate variability reduces streamflow by 11.9 mm/a.These results suggest that increase in plantations can reduce streamflow substantially,even more than climate variability.