A weighted H1 seminorm regularization method for Fredholm integral equations of the first kind

Many problems in mathematics and engineering lead to Fredholm integral equations of the first kind, e.g. signal and image processing. These kinds of equations are difficult to solve numerically since they are ill-posed. Therefore, regularization is required to obtain a reasonable approximate solution. This paper presents a new regularization method based on a weighted H¹ seminorm. Details of numerical implementation are given. Numerical examples, including one-dimensional and two-dimensional integral equations, are presented to illustrate the efficiency of the proposed approach. Numerical results show that the proposed regularization method can restore edges as well as details.     

基于多种特征的红外目标识别技术研究

红外图像独特的热辐射特性,通常是由其各个子部件的红外热辐射特性所形成的。文中在传统红外热图像特征量的基础上,通过分析红外热图像自身特性以及它的形成与其自身姿态的关系,提出了对目标物体红外热图像基于目标姿态的子分类,并对目标红外热图像进行了子区域分割,通过多目标分类器进行识别,形成了新的红外图像特征量。     

基于DSP的机动车抗眩目系统设计

为了解决车辆在夜间行驶时,对面车灯给驾驶员造成的眩目问题,提出了一种机动车抗眩目系统的设计方案.采用视频解码芯片SAA7111A和视频编码芯片AL250完成视频信号的A/D和D/A转换;利用数字信号处理器DSP对亮度值过高的像素点进行消亮处理,以达到抗眩目的目的;可编程逻辑器件CPLD完成对整个系统的逻辑控制以及提供存储器写数据的地址.系统经实际运行,性能稳定,抗眩目效果明显.     

MATLAB图像处理在水产养殖中的应用

介绍了一种通过MATLAB图像处理的方法,对水产养殖中鱼的病变情况进行监控的系统。根据中值滤波的原理,对灰度图像进行滤波,抑制噪声;根据二值化原理,将灰度图像进行二值化处理;根据收缩与扩张的原理,编写程序,对二值图像进行数次收缩与扩张,以达到去除杂质点的目的。最后采用区域矩特征的概念,找出判断鱼类出现病变的判断依据,即零阶矩,也就是白色像素的面积,只要统计出白色像素的数目,与统计数据相比较,即可判断出是否出现鱼类病变等异常情况。     

在VC中利用Kodak控件采集图像

本文在详细介绍Kodak图像扫描控件的属性和方法上,在VC＋＋中用Kodak图像扫描控件实现上位机的动态采集外部图像数据.     

衍射图像的信号采集和数据处理

针对衍射图像边缘弥散状态不易切准的问题，运用光电积分的方法确定采集点，然后将二值化的数据通过计算机采用最小二乘法进行计算，从而保证了测量结果的正确性     

Colour classification by neural networks in graphic arts

This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected. Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.     

Polarization Phase-Based Method For Material Classification In Computer Vision

A robust and accurate polarization phase-based technique for material classification is presented. The novelty of this technique is three-fold in (i) its theoretical development, (ii) application, and, (iii) experimental implementation. The concept of phase of polarization of a light wave is introduced to computer vision for discrimination between materials according to their intrinsic electrical conductivity, such as distinguishing conducting metals, and poorly conducting dielectrics. Previous work has used intensity, color and polarization component ratios. This new method is based on the physical principle that metals retard orthogonal components of light upon reflection while dielectrics do not. This method has significant complementary advantages with respect to existing techniques, is computationally efficient, and can be easily implemented with existing imaging technology. Experiments for real circuit board inspection, nonconductive and conductive glass, and, outdoor object recognition have been performed to demonstrate its accuracy and potential capabilities.     

A guided tour into subcellular colocalization analysis in light microscopy

It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well‐characterized markers. However, image‐analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object‐based approach.     

Preserving global features of fluid animation from a single image using video examples

We synthesize animations from a single image by transferring fluid motion of a video example globally.Given a target image of a fluid scene,an alpha matte is required to extract the fluid region.Our me...