一种自适应视频信号调光的新方法

着重介绍摄象机输出的全电视信号，经过一系列加工处理后，控制靶面照度，即控制陶瓷滤光片的透过率，从而实现了视频信号自适应调光的新方法，以及介绍构成调光系统的原理、电路设计特点、实验过程和实际应用效果等。调光系统，自适应控制系统，视频信号，电视跟踪系统。     

弱小目标检测与跟踪算法

低对比度小目标检测与跟踪算法的研究是电视跟踪领域的关键技术之一。针对弱小目标的目标特性，按USAN原则进行目标检测，利用目标的特征参数及目标运动的一致性、连续性排除噪声干扰，实现对目标的稳定跟踪。实验采用VC＋＋仿真平台验证该算法的可行性和有效性，并移植到DSP专用图像处理平台上，达到了工程的5可靠性与实时性要求。     

一种新的多波门跟踪系统

本文从波门形成的原理出发,着重介绍一种新的多波门对多目标跟踪的电视跟踪系统,并给出控制流程图和原理图及实验结果。     

高帧频电视跟踪算法的研究

在对高速运动目标进行高精度的跟踪测量时 ,常采用高帧频电视跟踪系统。针对高帧频电视象元分辨率低、处理时间短、信噪比低的特点 ,对其定位跟踪算法进行了研究。     

吊仓双视场CCD电视跟踪测量系统研究

为减少或避开地球表面稠密大气层对可见光、红外辐射背景以及减少地面杂电波的严重干扰，从空中直接摄取与精密跟踪测量动态目标的光辐射及电波特性具有十分重要意义。国外机载，弹工及星载等先进测量手段皆采取了避开大气层的测量模式，本文设计了一种适用于飞机吊仓使用的ＴＶ跟踪测量系统，特别是从地面上直接遥控空中机载ＴＶ测量设备，采用双镜头双视场共用一个ＣＣＤ接收器件进行二档大变双视场自动变焦技术以便能获取高清晰度     

扩展目标的精确定位研究

本文在分析电视跟踪算法精度的基础上,对扩展目标的精确定位作了初步的讨论。对几种亚象素边缘定位算法和曲线拟合算法作了计算机仿真,并给出了仿真结果。     

精稳控制系统与跟踪架运动耦合问题的分离

精密稳定控制系统控制对象是可见电视光路中安装在跟踪架上的可控反射镜，跟踪架的运动将对反射镜的运动产生耦合作用。根据坐标转换主光电平台摄像记录系统的光路结构，提出了复合运动关系新的解耦方法，试验证明，该方法有效地分离了跟踪架的耦合作用，开拓了运动关系分离的新思路。     

实时电视测量系统中值滤波器设计

在电视测量系统中，为了有效可靠地对目标进行跟踪，测量，有必要对ＣＣＤ传感器输入图象信号进行滤波处理，提高图象信号的信噪比，本文中采用一种适合于硬件实现的流水线中值滤波算法，利用快速ＲＡＭ器件和ＦＰＧＡ器件，完成８ｂｉｔ灰度图象３×３窗口任意形状中值滤波器。并给出理论分析和实验结果，整个电路去噪效果明显。     

电视跟踪器的实时输出精度分析

本文详细地讨论了引起电视跟踪测量系统误差的因素,提出了具有实用意义的计算误差的表达式。研究表明,电视跟踪测量系统的实时输出精度是由各个误差源(系统误差及随机误差)造成的总的均方根误差值决定的。     

序列图象目标的一种特殊数字跟踪方法

介绍了在数，模检测相结合的电视跟踪器的一项关键技术－指寅区域的位置和大小的序列图象的实时捕获和处理，并以ＡＤＳＰ２１８１为核心实现了一实际的系统，分析了其中的关键硬件技术介绍了可编程器件ＥＰＬＤ在其中的应用，给出了实时采集处理的结果。     

Recognition of interacting rotational and prismatic machining features from 3-D mill-turn parts

Current feature recognition methods generally recognize and classify machining features into two classes: rotational features and prismatic features. Based on the different characteristics of geometric shapes and machining methods, rotational features and prismatic features are recognized using different methods. Typically, rotational features are recognized using two-dimensional (2-D) edge and profile patterns. Prismatic features are recognized using 3-D geometric characteristics, for example, patterns in solid models such as 3-D face adjacency relationships. However, the current existing feature recognition methods cannot be applied directly to a class of so-called mill-turn parts where interactions between rotational and prismatic features exist. This paper extends the feature recognition domain to include this class of parts with interacting rotational and prismatic features. A new approach, called the machining volume generation method, is developed. The feature volumes are generated by sweeping boundary faces along a direction determined by the type of machining operation. Different types of machining features can be recognized by generating different forms of machining volumes using various machining operations. The generated machining volumes are then classified using face adjacency relationships of the bounding faces. The algorithms are executed in four steps, classification of faces, determining machining zones, generation of rotational machining volumes and prismatic machining volumes, and classification of features. The algorithms are implemented using the 3-D boundary representation data modelled on the ACIS solid modeller. Example parts are used to demonstrate the developed feature recognition method.     

An object-oriented neutral feature model for feature conversion

This paper describes the application of neutral features to facilitate feature conversion. Feature conversion among various applications is achieved through a two-way conversion process. When an application modifies its own features, the corresponding neutral features will be adjusted. This modification will be propagated to all the other applications through the conversion process. A prototype feature conversion system has been established. The system currently supports the design and manufacture of prismatic machined parts. Three applications are defined: design application, machining planning application and set-up planning application. Each application has its own set of application features. Object oriented technology is employed in system development. The structures of the neutral features and application features are developed using the object-oriented technique. The development of the representation schemes and the corresponding algorithms are divided into box-shaped and non-box-shaped. Object-oriented messages for the maintenance of both the box-shaped and non-box-shaped feature models are developed. The process of conversion from an application feature to neutral features is developed using the object-oriented technique; algorithms are implemented in the form of object-oriented messages. The conversion from neutral features to application features is handled by the rule-based reasoning approach.     

Feature reasoning for sheet metal components

The objective of this paper is to develop a set of principles for extracting, recognizing and reasoning about features from sheet metal parts which are created in the CAD system. Recognition of such features will enable the automatic evaluation of designs and the development of process plans by among other things, mapping form features to tools needed to produce the form. The principles are developed by studying the face-oriented representations of features and deriving concepts which relate the features geometrically and topologically. Subsequently, the processes that relate to the geometrical forms are used to further uniquely identify the features.     

Quantifying data for group technology with weighted fuzzy features

The high potential of using group technology in manufacturing has attracted the interest of both practitioners and researchers. Group technology is based on clustering parts which have similar features. Very often it is very hard to quantify successfully data regarding these features. This is because in many real applications features are fuzzy. This paper identifies two types of fuzzy features: qualitative features, and quantitative ones with subjective meaning. The paper presents a methodology for quantifying the data that refer to the fuzzy features. The proposed methodology deals with crisp and fuzzy data in a unified manner. Finally, some clustering approaches which process the quantified features are also discussed     

Assembly code generation from a feature-based geometric model

Design and implementation of a system for assembly code generation (from a feature-based geometric model) is discussed. Assembly code is further used in design for assembly evaluation procedures for produce assembly analysis. Assembly features are classified into three groups: geometric assembly features, non-geometric assembly features and operational assembly features. The first two groups of features have existed in computer-aided design databases, while the last one has existed in an assembly sequence. Algorithms for identifying the first two groups of features are presented. A tree processor to convert assembly features into assembly code is also developed. Finally, examples are presented.     

Color Image Steganalysis Based on Residuals of Channel Differences

This study proposes a color image steganalysis algorithm that extracts high-dimensional rich model features from the residuals of channel differences. First, the advantages of features extracted from channel differences are analyzed, and it shown that features extracted in this manner should be able to detect color stego images more effectively. A steganalysis feature extraction method based on channel differences is then proposed, and used to improve two types of typical color image steganalysis features. The improved features are combined with existing color image steganalysis features, and the ensemble classifiers are trained to detect color stego images. The experimental results indicate that, for WOW and S-UNIWARD steganography, the improved features clearly decreased the average test errors of the existing features, and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms. Specifically, when the payload is smaller than 0.2 bpc, the average test error decreases achieve 4% and 3%.     

A novel approach for characterisation of ischaemic stroke lesion using histogram bin-based segmentation and gray level co-occurrence matrix features

Among the various brain diseases, stroke is the major cause of death worldwide, next to heart attack. This paper proposes an algorithm in predicting the ischaemic stroke lesion using midline sketching and histogram bin-based technique. The visible ischaemic stroke lesion region and the normal region of the same computed tomography image are segmented with the help of histogram bins and the features are extracted. The first- and second-order statistical features for both regions are analysed. The differences in the features are utilised to categorise the lesion and non-lesion region. The statistical t-test analysis-based observations with a confidence interval of 95% for each feature are tabulated. These observations indicate that among the nine features, as per the statistical analysis, six features provide the clear differentiation between normal and abnormal regions.     

A Recommendation System Based on Fusing Boosting Model and DNN Model

In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can construct new features related before and after tree. Convolutional neural networks have a better perception of local features. In this paper, we take advantage of convolutional networks to capture the local features. The features are constructed by the node leaf of gradient boosting decision tree. This paper employs the tree leaf node to mine the user behavior path features, and uses the deep model to extract the user abstract features. Based on a Kaggle competition, our model performs better in the test data than any other model.     

Feature patterns in recognizing non-interacting and interacting primitive,circular and slanting features using a neural network

Many varied techniques have long been suggested for the recognition of features from solid modellers, and the systems which have incorporated these techniques have achieved a moderate success. However the problem of recognition of the wide variety of features, e.g. interacting and non-interacting primitive, circular and slanting features, that any real life component may have, requires complex systems which are inflexible and hence limited in their use. Here, we present a simple and flexible system in which the features are defined as patterns of edges and vertices to deal with all the above types of features. The system starts by searching a B-rep solid model, using a cross-sectional layer method, for volumes which can be considered to represent features. Once the volumes are detected, their edges and vertices are processed and arranged into feature patterns which are used as input for a neural network to recognize the features. Simple conventions used in this work enable the creation of feature patterns for primitive, circular and slanting features. Learning, generalizing and tolerating incomplete data are some of the neural network's attributes exploited in this work to deal with interacting and non-interacting features.