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.     

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。     

Supramolecular Engineering Strategy to Construct BODIPY-Based White Light Emission Materials

Two kinds of 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) dyads BDP-OH containing 4-hydroxystyrene groups and BDP-PY bearing pyridinyl units were prepared. In addition, a naphthalene derivative NAP-PY modified by pyridinyl moieties substituent was made. The above three dyads could be used to construct white-light emission (WLE) material by a supramolecular engineering strategy due to their three primary colors of blue, green and red. The supramolecular correlations between the hydroxyl group of BDP-OH and the pyridinyl groups of NAP-PY and BDP-PY were confirmed by ¹H NMR titration, 2D NOESY and FTIR. A fluorescence monitor application was carried out based on the realization of WLE. This work might be useful for designing other WLE supramolecular systems and image display.     

A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain

The aim of infrared and visible image fusion is to enhance the feature in infrared image and preserve abundant detail information in visible image. Based on the fact that the human sense system accepts external stimulation only when the stimulus intensity is greater than a certain value and the reaction of neuronal cells have obvious regional characters, an image fusion algorithm based on region dual-channel unit-linking pulse coupled neural networks (RDU-PCNN) and independent component analysis (ICA) bases in non-subsampled shearlet transform (NSST) domain for infrared and visible images is proposed. RDU-PCNN we constructed has obvious regional characters and much lower computational costs. We trained ICA-bases using a number of images that the content and statistical properties are similar with the fusion images but applied it as low-frequency ICA-bases, which can reduce calculation complexity. Experimental results demonstrate that the proposed method can significantly improved the fusion quality and need less computational costs.     

Influence of the image charge effect on the hydrogen-like impurity-bound polaron in a spherical quantum dot in the presence of an electric field

We have investigated the influence of an external electric field on the binding energies and polaronic shifts of the ground and some first few excited states of a hydrogenic impurity in a spherical quantum dot by taking into account the image charge effect. By using Landau–Pekar variational method the general analytical expression is obtained for the impurity bound-polaron energies. It has been numerically identified the conditions (electric field, nominal radius of quantum dot, etc.) in which the bound-polaron states can be existence in GaAs quantum dot. We have shown that the polaronic shifts in the binding energy of 1s-like state are the same in cases with and without image charge effect while they for 2s-like state are not coincide and have different monotonic behavior versus confinement potential. Electron–phonon interaction lifts the degeneracy of the 2px-, 2py-, and 2pz-like states of a donor impurity and reduces their binding energies.     

Optical asymmetric watermarking using modified wavelet fusion and diffractive imaging

In most of the existing image encryption algorithms the generated keys are in the form of a noise like distribution with a uniform distributed histogram. However, the noise like distribution is an apparent sign indicating the presence of the keys. If the keys are to be transferred through some communication channels, then this may lead to a security problem. This is because; the noise like features may easily catch people׳s attention and bring more attacks. To address this problem it is required to transfer the keys to some other meaningful images to disguise the attackers. The watermarking schemes are complementary to image encryption schemes. In most of the iterative encryption schemes, support constraints play an important role of the keys in order to decrypt the meaningful data. In this article, we have transferred the support constraints which are generated by axial translation of CCD camera using amplitude-, and phase- truncation approach, into different meaningful images. This has been done by developing modified fusion technique in wavelet transform domain. The second issue is, in case, the meaningful images are caught by the attacker then how to solve the copyright protection. To resolve this issue, watermark detection plays a crucial role. For this purpose, it is necessary to recover the original image using the retrieved watermarks/support constraints. To address this issue, four asymmetric keys have been generated corresponding to each watermarked image to retrieve the watermarks. For decryption, an iterative phase retrieval algorithm is applied to extract the plain-texts from corresponding retrieved watermarks.