非透视3点问题的解析解

由于在机器人导航等领域新的图像装置不符合透视规则,无法运用传统的透视位置估计方法,采用吴氏零点分解方法,给出非透视3点问题的解析解.利用新图像装置的几何条件,建立了非透视3点方程系统.     

Mapping Turbidity, Surface Temperature and Water Circulation Patterns with the Aid of Satellite Imagery

Water turbidity, surface temperature and circulation of Barton and Wroxham Broads, two of the Norfolk Broads, were studied using one reflective (TM1) and the thermal (TM6) TM band. The results indicated that the through-flowing path of the River Ant in Barton Broad is the major influence on the distribution of these parameters. In Wroxham Broad, both turbidity and surface temperature displayed a uniform distribution. This could be explained by the absence of a predominant direction of flow.     

红外化学遥感数字信号处理算法的研究进展

介绍了几种为远距离红外蒸汽信号监测开发的分类器设计，频域数字滤波，时域数字滤波等数字信号处理技术。对线性分类器，分段线性分类器和神经网络分类器等在红外化学遥感系统中的应用做了初步的探讨。对背景扣除技术，频域数字滤波技术，数字滤波的切趾方法等做了简要的描述。阐述了时域滤波技术的基本原理及实际应用。展望了红外化学遥感数字信号处理算法的研究前景。     

压电类智能层合结构的力学和计算模型综述

综述了压电类智能层合结构的力学和计算模型。基于大量国内外有关压电类智能层合结构的文献 ,根据各自采用的运动学假设、场变量的近似、压电层的表达法以及曲率和温度的影响 ,对压电层合结构的力学和计算模型进行了分类 ,阐述了各种理论的特点及一般形式。     

湿敏半导体材料电导率计算方法的研究

结合修正了的耗尽层理论和多孔湿敏半导体的特点，本文提出了一个简单的物理模型作为ｎ型湿敏半导体特征的一级近似。描述了多孔湿敏半导体的导电机理，并给出了湿度与宏观电导率的关系。     

Electronic Sensing of Aromatic Volatiles for Quality Sorting of Blueberries

An electronic sniffer using semi-conductor gas sensors that nondestructively measured aromatic volatile gas emissions from fruit was developed to assess blueberry quality. The sniffer detected soft and damaged fruit in packaged containers at a 5% level of damage and distinguished four of five fruit ripeness classes: (1) mature-green and green-pink; (2) blue-pink; (3) blue; and (4) ripe fruit. Sniffer response increased as fruit ripened, as did total concentration of aromatic volatiles. Sniffer response correlated with berry firmness, pH, titratable acidity, and color, and detected differences among 10 cultivars, as did impact response analysis. The electronic sniffer is rapid, nondestructive and may be used to sort and quality check for presence of unripe or damaged fruit in closed packs of fresh berries.     

Neural architecture for robotic vision sensor that discounts uneven illumination in a manufacturing environment

Computer vision algorithms for inspection or pick-and-place operations often depend on spatially uniform illumination of a workplace. This necessitates expensive lighting fixtures. To discount effects of uneven illumination we designed and tested a neural network that can adaptively control light sensitivity at the photosensor level. Our neural network architecture consists of multiple layers with hexagonally arranged nodes. All nodes have partially overlapping receptive fields of different spatial frequencies. Feedforward connections are excitatory while feedback pathways subserve lateral inhibition. The outputs of these nodes are combined so as to maximize the signal-to-noise ratio while constantly resetting thresholds to maintain high sensitivity. Our connectionist architecture can account for many characteristics attributed to the lightness constancy phenomenon observed in biological systems. The results suggest that our module maintains high sensitivity over the whole domain of intensities without interfering with transmission of visual information embedded in spatial discontinuities of intensity.     

A geometry-based error estimation for cross-ratios

For choosing specific cross-ratios as 2D projective coordinates in various computer vision applications, a reasonable error analysis model is usually required. This investigation adopts the assumption of normal distribution for positioning errors of point features in an image to formulate the error variances of cross-ratios. Based on a geometry-based error analysis, a straightforward way of identifying the cross-ratios with minimum error variances is proposed. Simulation results show that the proposed approach, as well as a further simplified alternative, yield much better estimations of minimum error variances in terms of accuracy, cost, and stability compared with some other methods, e.g., the one based on the rule given by Georis et al. (IEEE Trans. Pattern Anal. Mach. Intell. 20 (4) (1998) 366). Some causes of the performance differences in the estimations are explained using a special configuration of point features.     

Satellite SAR Remote Sensing of Great Lakes Ice Cover,Part 2. Ice Classification and Mapping

During the 1997 winter season, shipborne polarimetric backscatter measurements of Great Lakes (freshwater) ice types using the Jet Propulsion Laboratory C-band scatterometer, together with surface-based ice physical characterization measurements and environmental parameters, were acquired concurrently with Earth Resource Satellite 2 (ERS-2) and RADARSAT Synthetic Aperture Radar (SAR) data. This polarimetric data set, composed of over 20 variations of different ice types measured at incident angles from 0° to 60° for all polarizations, was processed to radar cross-section to establish a library of signatures (look-up table) for different ice types. The library is used in the computer classification of calibrated satellite SAR data. Computer analysis of ERS-2 and RADARSAT ScanSAR images of Great Lakes ice cover using a supervised classification technique indicates that different ice types in the ice cover can be identified and mapped, and that wind speed and direction can have an influence on the classification of water as ice based on single frequency, single polarization data. Once satellite SAR data are classified into ice types, the ice map provides important and necessary input for environmental protection and management, ice control and ice breaking operations, and ice forecasting and modeling efforts.     

基于非接触精密视觉测量的硬盘磁头头偏测量系统

应用基于机器视觉的非接触测量技术对计算机硬盘磁头的头偏测量，不仅满足了磁头生产防污、防ESD的需要，而且实现了精密测量的需要；对测量数据的采集处理以及SPC实时监控，有效地实现了生产过程的控制和预报；与FIS系统的连接实现了生产数据的可追溯性，为生产管理现代化提供一个强有力的工具。系统已广泛应用于硬盘磁头生产线。