图像和机器视觉技术概述

图像和机器视觉系统是一项快速发展中的应用技术,介绍了机器视觉系统的组成和基本工作原理,及机器视觉在半导体制造领域中的应用。     

PC机与DSP之间串行通信的实现

概要地介绍了TMS320F2812 DSP的基本性能,重点介绍了在Visual Basic 6.0下.利用MSComm控件实现上位PC机与下位机DSP之间异步串行通信的具体实现方式,并给出了一个采用RS-232串行通信标准来实现信息传输的实例.实验结果表明,串行通信简单,界面友好,改善了系统的性能,应用领域广泛.     

基于数码相机的双向反射率分布函数测量方法

除传统领域外,双向反射率分布函数(BRDF)在计算机图形学、虚拟现实等方面具有广泛的应用,但缺乏价格适中的测量工具。试验了一种基于单个数码相机的BRDF测量方法。该法综合了摄影测量学、光学、计算机图形学等学科的知识,测量装置成本低廉、易于实现,并集成于专门编制的测量程序中。测量操作过程方便、易行,测量数据可直接以视化形式输出。给出了某种油漆的测量实例,初步验证了该方法的可行性。     

马铃薯深加工工艺废液化学絮凝研究

以钙盐、铝盐、铁盐及聚丙烯酰胺为絮凝剂在不同温度、不同pH等条件下对马铃薯深加工的废液进行化学絮凝。结果发现聚丙烯酰胺加聚合氯化铝絮凝的效果最好。CODCr能由15000mg/L降到5500mg/L,沉降率60%左右。SS能由9500mg/L降到1000mg/L,沉降率为90%左右。能达到进行生物絮凝的要求。     

利用基于导数更新的双基机载雷达杂波距离依赖性补偿方法

该文提出了两种利用基于导数更新(DBU)的双基机载雷达杂波距离依赖性补偿新方法,分别以接收载机方向图主瓣方向的接收斜距和接收载机方向图主瓣方向的接收俯仰角余弦为更新变量,利用该方法对回波数据进行补偿后,能够有效降低双基机载雷达杂波的距离依赖性,进而大大提高空时自适应处理 (STAP)的地面动目标检测(GMTI )性能。仿真数据处理结果证明了所提方法的有效性。     

Influence of processing and clay type on nanostructure and stability of polypropylene–clay nanocomposites

Melt processing is a critical step in the manufacture of polymer articles and is even more critical when dealing with inhomogeneous polymer–clay nanocomposites systems. The chemical composition, and in particular the clay type and its organic modification, also plays a major contribution in determining the final properties and in particular the thermal and long-term oxidative stability of the resulting polymer nanocomposites. Proper selection and tuning of the process variable should, in principle, lead to improved characteristics of the fabricated product. With multiphase systems containing inorganic nanoclays, however, this is not straightforward and it is often the case that the process conditions are chosen initially to improve one or more desired properties at the expense of others.     

A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

Scanning schemes in white light Photoelasticity – Part I: Critical assessment of existing schemes

The use of white light based Three Fringe Photoelasticity (TFP)/RGB Photoelasticity has gained importance in the recent years. With recent advances in TFP, it is possible to resolve fringe orders upto twelve. The main advantage of this technique is that it requires only a single image for isochromatic demodulation, which makes it suitable especially for problems where recording multiple images is difficult. The accuracy of isochromatic data obtained using TFP/RGB Photoelasticity is dependent on the scanning scheme used to refine the data, which is necessary to incorporate fringe order continuity. In this paper, the existing scanning schemes are critically evaluated for their ability to scan the entire model domain, influence of seed point selection and noise propagation. The scanning schemes are assessed using four problems of increasing level of geometric complexity – Circular disc under compression (simply connected), bi-axially loaded cruciform specimen with an inclined crack, a thick ring subjected to internal pressure and a finite plate with a hole (multiply connected).     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.     

Sample selection for visual domain adaptation via sparse coding

Visual domain adaptation has attracted much attention and has made great achievement in recent years. It deals with the problem of distribution divergence between source and target domains. Current methods mostly focus on transforming images from different domains into a common space to minimize the distribution divergence. However, there are many irrelevant source samples for target domain even after the transformation. In order to eliminate the irrelevant samples, we develop a sample selection algorithm using sparse coding theory. We do the sample selection in a common subspace of source and target data to find as many as relevant source samples. In the common subspace, data characteristics are preserved by using graph regularization. Therefore, we can select the most relevant samples for our target image classification task. Moreover, in order to build a discriminative classifier for the target domain, we use not only the common part of source and target domains learned in the common subspace but also the specific part of target domain. The algorithm can be extended to handle samples from multiple source domains. Experimental results show that our visual domain adaptation method on the image classification tasks can be very effective for the state-of-the-art datasets.