Resolution improvement for enhancing interference fringes of digital holograms with near infrared illuminance

A new resolution improving method of enhancing the interference stripes, which can achieve better reconstruction of the recorded object of infrared digital holography has been proposed in this paper. The experiment is conducted under near infrared illuminance. We create poor quality holograms with very low-resolution interference stripes and enhance it with our new method. The processed holograms have much higher-resolution interference stripes. This method is based on interpolation theory, and we make it works fast, stable and easy to apply. Both the forward and backward interpolation of the oriental and portrait direction of the source pixels have been calculated, and have been used to realize the final aim of resolution improvement. During the experiment, since the responsivity of the CCD is not satisfied with the illuminance laser, which is by design, we also give specific analysis on the experimental setup of our work to make sure the object information can be fully recorded by the optical setup but cannot be reconstructed because of the low-resolution and unobvious of the interference stripes. After the resolution improvement process of the original hologram, the object information can then be fully reconstructed, which is very clear in the paper. Figures and dataflow give evidence and demonstrate the good performance of our method.     

Evaluation of yarn defects by image processing technique

Yarn defect evaluation, since they can drastically deteriorate the quality of the product, is one of the most important problems in textile industry. In this work, the yarn defects are evaluated by image processing technique. Initially, images were taken by scanner, later the obtained images were modified by using several filters. Then, the yarn defects were identified base on their geometric shape and surface area. As the results show, image processing methods is excellently reliable in evaluating number and type of yarn defects.     

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.     

Good match exploration for thermal infrared face recognition based on YWF-SIFT with multi-scale fusion

Stable local feature detection is a critical prerequisite in the problem of infrared (IR) face recognition. Recently, Scale Invariant Feature Transform (SIFT) is introduced for feature detection in an infrared face frame, which is achieved by applying a simple and effective averaging window with SIFT termed as Y-styled Window Filter (YWF). However, the thermal IR face frame has an intrinsic characteristic such as lack of feature points (keypoints); therefore, the performance of the YWF-SIFT method will be inevitably influenced when it was used for IR face recognition. In this paper, we propose a novel method combining multi-scale fusion with YWF-SIFT to explore more good feature matches. The multi-scale fusion is performed on a thermal IR frame and a corresponding auxiliary visual frame generated from an off-the-shelf low-cost visual camera. The fused image is more informative, and typically contains much more stable features. Besides, the use of YWF-SIFT method enables us to establish feature correspondences more accurately. Quantitative experimental results demonstrate that our algorithm is able to significantly improve the quantity of feature points by approximately 38%. As a result, the performance of YWF-SIFT with multi-scale fusion is enhanced about 12% in infrared human face recognition.     

Optical multiresolution analysis with spatial localization

Multiresolution analysis is very useful for characterization of textures, segmentation tasks, and feature enhancement. The development of optical methods to perform such procedures is highly promissory for real-time applications. Usually, the optical implementations of multiresolution analysis consist in the decomposition of the input scene in different frequency bands, obtaining various filtered versions of the scene. However, under certain circumstances it could be useful to provide just one version of the scene where the different filters are applied in different regions. This procedure could be specially interesting for biological and medical applications in situations when the approximate localization of the scale information is known a priori. In this paper we present a fully optical method to perform multiresolution analysis with spatial localization. By means of the proposed technique, the multi-scale analysis is performed at once in a unique image. The experimental set-up consist of a double-pass convergent optical processor. The first stage of the device allows the multiple band decomposition, while the second stage confines the information of each band to different regions of the object and recombines it to achieve the desired operation. Numerical simulations and experimental results, which prove the very good performance of the method, are presented.     

浅海波导界面对点源振速方向的影响

采用“虚源法”分析计算了浅海波导环境中接收点处点源总振速方向与水平面夹角,侧重讨论确定性界面反射对总振速方向的影响。研究结果表明：总振速方向和接收点与声源的水平距离、两者深度,海底、海面特性以及声速剖面等有关。在等声速均匀浅海波导中,由于确定性界面反射的影响,当直达声掠射角为1°～50°时,合成总振速方向偏离直达声方向达1.5°～10.5°,声速剖面呈负梯度时,偏离程度更甚.     

基于CAN总线的某型数字化战车自动测试系统设计

针对现代数字化战车电气与电子系统技术复杂、各分系统关联程度高、维修保障困难等问题,设计了一种基于CAN总线的数字化战车自动测试系统;整体设计基于模块化思想,充分利用车载备用总线,综合运用了DSP+FPGA控制处理技术、AFPN智能故障诊断技术,能够对车载电气与电子系统进行分系统独立测试和全系统联合检测,具有快速故障定位能力和拓扑适应能力;采用适配器与上位机测试软件的分体式设计,实现了平台物理资源复用。应用表明,该系统能够较好的适用于现代数字化战车的维修保障,使用方便,故障诊断快速有效。     

某机载瞄准系统精度评估测试方法

为了评估某型机载瞄准系统的精度,根据飞行试验参数测试需要,提出了通过图像测量与DGPS融合处理方法进行瞄准精度评估的试验方案。该方案利用飞行试验获取的图像数据与其他测量数据相结合,建立精度评估的方法,该评估方法在该机载武器瞄准系统精度评估得到应用,并且可在同类试验课题中推广应用。     

夜视战场环境视觉传感信息采集系统设计

夜视战场环境视觉传感信息的实时获取是跟踪夜视战场图像处理分析的前提,针对夜视战场环境的复杂特征,设计了夜视战场环境视觉传感信息采集系统,分析了系统的总体结构,系统硬件主要包括视觉传感器、传感器控制卡和扩展卡、图像采集与存储以及信号调理电路,给出了系统采集节点的程序设计流程以及传感信息采集的具体程序代码实现过程。实验结果说明,所设计系统可有效采集到夜间军事战场中的传感信息,并且具有较高的采集精度。     

传感器驱动下双路视频采集与处理系统的实现

传统的视频采集使用连续录制的方式,一方面对存储需求较大,另一方面造成视频检索困难。针对这一问题,提出并实现了一种基于传感器驱动的视频采集和处理系统。系统以震动传感器和人体红外传感器作为触发器,实现对视频采集设备的驱动,并将获取的双路视频图像采用PIP技术进行处理。实验证明,这样做不仅能有针对性地获取视频信息,提高视频信息的可用性,而且可以扩大视频信息采集范围,节省存储空间。