Construction of a Structural-Parametric Diagnostic Model from the Results of Processing Measurement Information

A justification is given of an approach in which it is proposed to use diagnostic information concerning the structural characteristics of an object, in the form of a structural-parametric mathematical model, together with data concerning the results of measurements of its important parameters in order to determine the technical state of the diagnosed object.     

Feature classification techniques in model-based object recognition

We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.     

Maximum entropy methods in coherent radar imaging

There are some compelling reasons for viewing the problem of image reconstruction from noisy or incomplete data as one of statistical estimation, i.e., of choosing, from the infinity of images consistent with the data, that image which, in some statistical sense, is most plausible. Among these reasons are the soundness of the philosophical underpinning of the resulting image reconstruction process, a greater realization of the image resolution which is inherent in the data, and freedom from many of the artifacts encountered in commonly used ad hoc reconstruction schemes. One successful technique employing a principle of statistical inference is the maximum entropy technique, in which the data-consistent image with maximum configurational entropy is chosen. It is a computationally intensive approach involving a conjugate gradient search over a convex function of a vector in a space of dimensionality equal to the number of image pixels. This technique has been employed with success in situations where the data samples are modeled as linearly related to a real non-negative object. We investigate application of maximum entropy image reconstruction to the problem of high-resolution radar diagnostic imaging. The problem differs from others in which maximum entropy has been applied in that the object to be imaged is complex. Although the desired image is of the magnitude of the complex object and is thus real and non-negative, there is no linear relationship beween object magnitude and data. Rather, the data are linearly related to the complex object. Several earlier proposed methods for applying the maximum entropy principle to this problem are identified and analyzed. A method that more closely approximates true Bayesian estimation is proposed.     

Improving closely spaced dim object detection through multiframe blind deconvolution of near stellar neighbourhoods

A new iterative algorithm is proposed to improve the detection of dim stellar objects that are in the neighbourhood of a bright object, using short-exposure images. This method separates data functions into the primary bright object function, the neighbourhood system function, and the background function. This approach uses the principles of the Expectation-Maximization algorithm with the Gerchberg-Saxton phase retrieval algorithm to overcome the image degradation caused by the photon counting noise from the charge-coupled devices and the turbulent atmospheric conditions. The performance of this new neighbourhood system algorithm is compared with that of the multiframe blind deconvolution algorithm, using laboratory data and computer-simulated data. This paper provides an improved technique to image closely spaced dim objects.     

远程设备监测与诊断系统的DCOM组件设计与实现方法

在分析组件开发技术特点的基础上，提出了组件设计的三点原则和系统的三层组件工作模型，详细阐述设备远程监测与故障诊断系统中利用组件技术进行设计开发的基本思路，同时利用DCOM技术实现了高效的数据访问。     

Marginal estimation of aberrations and image restoration by use of phase diversity

We propose a novel method called marginal estimator for estimating the aberrations and the object from phase-diversity data. The conventional estimator found in the literature concerning the technique first proposed by Gonsalves has its basis in a joint estimation of the aberrated phase and the observed object. By means of simulations, we study the behavior of the conventional estimator, which is interpretable as a joint maximum a posteriori approach, and we show in particular that it has undesirable asymptotic properties and does not permit an optimal joint estimation of the object and the aberrated phase. We propose a novel marginal estimator of the sole phase by maximum a posteriori. It is obtained by integrating the observed object out of the problem. This reduces drastically the number of unknowns, allows the unsupervised estimation of the regularization parameters, and provides better asymptotic properties. We show that the marginal method is also appropriate for the restoration of the object. This estimator is implemented and its properties are validated by simulations. The performance of the joint method and the marginal one is compared on both simulated and experimental data in the case of Earth observation. For the studied object, the comparison of the quality of the phase restoration shows that the performance of the marginal approach is better under high-noise-level conditions.     

Region-based surveillance video retrieval with effective object representation

Retrieving the most relevant video frames that contain the object specified in a given query (query-by-region) remains a challenging task. Two common challenges of region-based retrieval approaches are to accurately extract or segment object(s) and select a proper matching strategy. This paper addresses these problems by proposing a retrieval approach that uses a new region-based matching technique equipped with an effective object representation method. In the first stage, the proposed approach selects the most informative instances of each object that appeared in the video by utilizing an adapted clustering algorithm over the extracted features. In the retrieval stage, the new matching technique returns the most relevant sequences of video by mapping a given region with those identified representative instances of objects based on their similarity scores. The proposed approach is evaluated on standard datasets and the results demonstrate a 31% improvement in the retrieval performance compared to other state-of-the-art methods.     

Multimodal region-consistent saliency based on foreground and background priors for indoor scene

Visual saliency is a very important feature for object detection in a complex scene. However, image-based saliency is influenced by clutter background and similar objects in indoor scenes, and pixel-based saliency cannot provide consistent saliency to a whole object. Therefore, in this paper, we propose a novel method that computes visual saliency maps from multimodal data obtained from indoor scenes, whilst keeping region consistency. Multimodal data from a scene are first obtained by an RGB+D camera. This scene is then segmented into over-segments by a self-adapting approach to combine its colour image and depth map. Based on these over-segments, we develop two cues as domain knowledge to improve the final saliency map, including focus regions obtained from colour images, and planar background structures obtained from point cloud data. Thus, our saliency map is generated by compounding the information of the colour data, the depth data and the point cloud data in a scene. In the experiments, we extensively compare the proposed method with state-of-the-art methods, and we also apply the proposed method to a real robot system to detect objects of interest. The experimental results show that the proposed method outperforms other methods in terms of precisions and recall rates.     

基于最小二乘估计的多站交会方法

在多台电视经纬仪组成的测量网络中,为了提高目标的捕获、跟踪能力,减少交会模型切换带来的引导数据的波动,提出一种基于最小二乘估计的多站交会方法.该方法根据电视经纬仪实时跟踪被测目标时的几何关系,利用电视经纬仪的站点位置和实时获得的目标测角信息,对于每台电视经纬仪都构造两个垂直相交于其测量方向线的平面,再用最小二乘原理估计被测目标的空间坐标.在两台电视经纬仪实时交会时,异面交会法是该方法的特例,于是证明该方法是有效的.仿真试验结果表明,与异面交会法相比,此方法产生的引导数据平滑、无波动,二者的定位误差均小于0.18 m,能满足电视经纬仪多站交会定位的要求.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Image focus volume enhancement in shape from focus systems

Three-dimensional object reconstructions is an active research area in digital imaging. In shape from focus approach, erroneous focus measurements result in inaccuracy in the depth map reconstruction of 2D object. Conventionally, to enhance the image focus volume, focus values are aggregated within a window, which is a linear filtering approach. Owing to the inherent limitation of linear process, optimal results may not be obtained. In order to overcome this limitation, a non-linear filtering approach is proposed to enhance the image focus volume for accurate depth estimation. The noisy focus values are restored in two steps. First, noisy focus values are detected using min–max operators. In order to increase the dynamic range between the minimum and the maximum focus values within the window, an appropriate power law function is designed. In second step, only the noisy measurements are replaced with the estimated ones. A refined depth map is obtained from the updated focus volume. This process continues until the difference between the previous and the current depth maps becomes very small. The performance of the proposed non-linear filtering approach is obtained for various synthetic and real objects. The results highlight the depth map estimates of the proposed approach more accurate while preserving object edges. Comparative analysis demonstrates the effectiveness of the proposed approach.     

Enhanced reconstruction of three-dimensional shape and texture from integral photography images

A method for the reconstruction of 3D shape and texture from integral photography (IP) images is presented. Sharing the same principles with stereoscopic-based object reconstruction, it offers increased robustness to noise and occlusions due to the unique characteristics of IP images. A coarse-to-fine approach is used, employing what we believe to be a novel grid refinement step in order to increase the quality of the reconstructed objects. The proposed method's properties include configurable depth accuracy and direct and seamless triangulation. We evaluate our method using synthetic data from a computer-simulated IP setup as well as real data from a simple yet effective digital IP setup. Experiments show reconstructed objects of high-quality indicating that IP can be a competitive modality for 3D object reconstruction.     

Color transmission analysis of color computer-generated holography

A color transmission approach between a computer display and a color computer-generated holography (CCGH) colorimetric system is proposed based on color matching theory. Firstly, the conversion between color quantities of a computer display and a CCGH colorimetric system is discussed based on color matching theory. Secondly, the isochromatic transfer relationship of color quantity and amplitude of the object light field is proposed. Thirdly, the color object light field was encoded into a hologram, and then the hologram was reconstructed numerically. The simulation results demonstrate that our novel approach is feasible.     

Control logic verification for an automotive body assembly line using simulation

In this paper, an approach is proposed that verifies the controller logic processes for the automobile industry via simulation. For this purpose, a state-based object model that creates a virtual car body assembly line is proposed and a verification methodology using observed signal sequences during the simulation is proposed. This approach was applied to an assembly line controlled by a PLC and the effectiveness of the proposed system was explained in a case study.     

Multi-objective design for vibration and buckling of laminated plates by statistical treatment

A design approach is presented for dealing with multiple (six or even more) object functions in the optimal design problem of laminated composite plates. The proposed approach is divided into some steps. First, the design variables are discretized with a certain increment that depends on the required accuracy, and all the values of physical parameters (used as object functions) are calculated for the discrete values of design variables. The second step is based on statistical treatment of the data. The calculated values of each physical parameter for discrete design variables are normalized and modified to be the deviated values on the assumption that the values are subjected to the Gaussian distribution with the prescribed standard deviation and average. At the last step, the best design in global sense is determined by considering the weighted sum of the deviated object functions and also graphical presentations. In the application, a three-layered rectangular plate with angle-ply stacking sequence [θ/−θ/θ] is considered where a representative fiber orientation angle θ is a single design variable. The object functions are taken to be six physical parameters of vibration and various buckling problems.     

Image retargeting approach for ranging cameras

This paper presents a novel image retargeting approach for ranging cameras. The proposed approach first extracts three feature maps: depth map, saliency map and gradient map. Then, the depth map and the saliency map are used to separate the main contents and the background and thus compute a map of saliency objects. After that, the proposed approach constructs an importance map which combines the four feature maps by the weighted sum. Finally, the proposed approach constructs the target image using the seam carving method based on the importance map. Unlike previous approaches, the proposed approach preserves the salient object well and maintains the gradients and visual effects in the background. Moreover, it protects the salient object from being destroyed by the seam carving algorithm. The experimental results show that the proposed approach performs well in terms of the resized quality.     

Exact analytical approach for six-degree-of-freedom measurement using image-orientation-change method

An exact analytical approach is proposed for measuring the six-degree-of-freedom (6-DOF) motion of an object using the image-orientation-change (IOC) method. The proposed measurement system comprises two reflector systems, where each system consists of two reflectors and one position sensing detector (PSD). The IOCs of the object in the two reflector systems are described using merit functions determined from the respective PSD readings before and after motion occurs, respectively. The three rotation variables are then determined analytically from the eigenvectors of the corresponding merit functions. After determining the three rotation variables, the order of the translation equations is downgraded to a linear form. Consequently, the solution for the three translation variables can also be analytically determined. As a result, the motion transformation matrix describing the 6-DOF motion of the object is fully determined. The validity of the proposed approach is demonstrated by means of an illustrative example.     

约束对象的产品族模型知识表示及配置方法

针对目前产品族模型存在冗余、配置效率低、不易更新的问题,研究了基于约束对象的产品族模型的知识表示方法,有效地降低了产品结构冗余以及配置的复杂度,实现模型知识的有效封装.在此基础上研究了基于约束和实例的配置推理方法,给出了具体的配置推理过程.最后,给出了一个摩托车产品族建模的案例.该模型及其推理方法能够方便地利用计算机实现,可用于建立一个高效的产品族配置系统.     

模糊理论与BP网络在目标识别中的应用

针对利用神经网络进行目标识别时特征向量选取中存在的一些问题：如特征向量选取不当，导致不同目标特征向量值可区分性差；相同目标由于大小、平移、旋转角度的不同，导致特征向量值具有较大差异等，首先对样本图像边缘提取，然后对已有的隶属函数进行改造，提出了一种基于模糊理论的阈值分割法，把图像二值化处理，提取出样本图像中目标的边缘轮廓，对其取不变矩．并归一化不变矩．为了避免不变矩数值过小，对其取对数，以此作为BP网络的输入特征向量，进行训练和识别．试验表明该方法能快速有效地识别出目标．