基于局部纹理特性和图像分割的分步立体匹配

为解决立体匹配难以兼顾精度和速度的问题,提出一种分步立体匹配方法。根据局部纹理特性对图像对进行灰度变换;然后利用均值漂移算法分割参考图像,以任意大小和形状的分割区域为支持窗口进行初次匹配,形成基于色彩分割的视差约束;再以固定窗口为支持窗口进行二次匹配,获得初始视差图;最后通过可信度分类优化初始视差。实验结果表明所提出的算法兼有较高的匹配速度和精度。     

Neuroimage‐based clinical prediction using machine learning tools

Classification of structural brain magnetic resonance (MR) images is a crucial task for many neurological phenotypes that machine learning tools are increasingly developed and applied to solve this problem in recent years. In this study binary classification of T1‐weighted structural brain MR images are performed using state‐of‐the‐art machine learning algorithms when there is no information about the clinical context or specifics of neuroimaging. Image derived features and clinical labels that are provided by the International Conference on Medical Image Computing and Computer‐Assisted Intervention 2014 machine learning challenge are used. These morphological summary features are obtained from four different datasets (each N > 70) with clinically relevant phenotypes and automatically extracted from the MR imaging scans using FreeSurfer, a freely distributed brain MR image processing software package. Widely used machine learning tools, namely; back‐propagation neural network, self‐organizing maps, support vector machines and k‐nearest neighbors are used as classifiers. Clinical prediction accuracy is obtained via cross‐validation on the training data (N = 150) and predictions are made on the test data (N = 100). Classification accuracy, the fraction of cases where prediction is accurate and area under the ROC curve are used as the performance metrics. Accuracy and area under curve metrics are used for tuning the training hyperparameters and the evaluation of the performance of the classifiers. Performed experiments revealed that support vector machines show a better success compared to the other methods on clinical predictions using summary morphological features in the absence of any information about the phenotype. Prediction accuracy would increase greatly if contextual information is integrated into the system. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 89–97, 2017     

极限学习机在图像隐写分析中的应用

为了有效提高图像隐写分析的检测正确率和速度,特结合单隐层前馈神经网络(SLFN)的特点,提出了一种基于极限学习机(ELM)的隐写分析方法.该方法首先根据Fridrich提出的多域特征提取算法从图像DCT域和空域中提取特征;得到193维原始特征;然后使用"主成份分析"法将其约简至18维;最后采用极限学习机作为分类方法构造隐写分析算法.实验表明,与目前隐写分析算法中广泛使用的支持向量机(SVM)相比,极限学习机参数调节少,学习速度快,以较少的隐层节点数取得了与SVM相似的检测正确率,能够实现针对各类JPEG图像隐写算法的有效检测.     

CFD simulation of refrigerated display cabinets

The finite element method is employed for the analysis of velocity and temperature distributions in refrigerated open display cabinets. The CFD code is based on the streamfunction-vorticity formulation, and incorporates a LES turbulence model. As an example of application, a vertical multi-deck cabinet is investigated under different operating conditions. The numerical results have been validated by comparison with experimental tests performed in accordance with the EN441 Standard. The influence of various design parameters has been investigated.     

A fuzzy logic‐based meningioma tumor detection in magnetic resonance brain images using CANFIS and U‐Net CNN classification

This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate.     

Computer-aided detection and characterization of stroke lesion – a short review on the current state-of-the art methods

The fast advancements in the field of computer vision, progress in radiology, image processing, modelling and simulation have changed the medical science to diagnose people in an efficient way. To be exact, the headways in medical imaging have prompted better diagnostic planning and accuracy in surgical methodology with little human–machine intervention. Stroke remains the third driving reason for death, after heart attack and cancer. Automatic computer-aided diagnosis of brain diseases has been gaining significant attention in the last two decades. The aim of this work is to review the current state-of-the-art techniques employed for segmentation, classification and detection of stroke lesion and present the key challenges in it. By investigating the advanced aspects and significant pitfalls of the different surveyed techniques, an overview on the performance of these methods is presented in this work.     

基于自组织映射的产品造型特征辨识研究

为解决长期以来在产品造型设计中缺乏系统化、自动化的设计方法,以汽车侧面造型为例,利用贝塞尔曲线描述汽车造型,确立汽车造型变量,从而可使用数值来表现造型.利用自组织映射神经网络建立模拟消费者的认知机制,分析各造型类别的数值差异,并计算其共同的特性,提取造型特征.该文的研究结果可建立一套能自动提供消费者对产品认知模式的方法,藉以加速设计的流程,辅助设计师有效地进行产品造型设计.     

基于不变特征的标志识别技术研究

标志识别是近距离空间交会对接中的一项重要的关键技术.在空间交会对接过程中,太阳和月球等各种天体以及飞行器上零部件的辐射与反射都会影响CCD的成像,形成大量杂散光干扰.为此,本文首先设计了具有不变特征的标志系统,然后根据标志系统固有的不变特征,详细设计了左右像点匹配、远场像平面不变特征识别、近场像平面预测识别以及像空间模型匹配全等识别等一套完整算法,可以保证彻底消除杂散光的干扰,正确可靠地完成各个标志灯像点的辨识.模拟图像仿真实验和真实图像仿真实验结果验证了算法的正确性和可靠性.     

Liquid film and droplet flow behaviour and heat transfer characteristics of herringbone microfin tubes

Cross-sectional liquid flow rate distribution of vapour liquid two phase flow of R123 in different herringbone microfin tubes has been measured. Droplet and liquid film flow rates are calculated with the measured data and assumptions for droplet distribution and slip ratio. Heat transfer coefficients of evaporation and condensation in herringbone microfin tubes have been measured using R22. Heat transfer enhancement mechanism by the herringbone microfins is discussed by using the measured data and numerically obtained cross-sectional flow field of a single phase flow. Flow rate of thin liquid film flowing on tube sides is affected by the helix angle and fin height. Larger helix angle and higher fin give thinner film. Liquid film flow rates in tube top and bottom are higher than tube sides. Droplet flow rate is increased with increase of helix angle and fin height, although the effect of fin height is not as pronounced as helix angle. Droplet radial mass velocity to tube side walls is increased with helix angle.