基于Hough圆卷积变换的孔组检测

为降低孔组测量的成本,提高测量的精确度,提出一种基于Hough国卷积的孔组检测方法.对孔组图像进行中值滤波、拉普拉斯锐化增强和边缘检测;根据孔组半径大小的变化范围,对Hough圆锥进行改进;在此基础上,利用改进的Hough圆锥与边缘检测后的图像进行三维卷积,得到Hough参数空间,计算出孔组的坐标和半径值;采用快速傅立叶变换法代替三维卷积,减少算法的计算时间.实验结果表明,该方法的检测精度高、实时性强,可以用于孔组的自动化检测.     

结构光视觉引导的焊接机器人系统自标定技术

为实现结构光视觉引导的焊接机器人系统的标定,解决现有标定方法复杂,标定靶标制作要求高等缺点,提出一种基于主动视觉的自标定方法。该标定方法对场景中3个特征点取像,通过精确控制焊接机器人进行5次平移运动,标定摄像机内参数和手眼矩阵旋转部分;通过进行2次带旋转运动,结合激光条在特征点平面的参数方程,标定手眼矩阵平移部分和结构光平面在摄像机坐标系下的平面方程;并针对不同焊枪长度进行修正。在以Denso机器人为主体构建的结构光视觉引导的焊接机器人系统上的测试结果稳定,定位精度可达到±0.93 mm。该标定方法简单,特征选取容易,对焊接机器人系统在实际工业现场的使用有重要意义。     

基于IMAQ Vision和PLC的水果大小自动分级系统设计

水果大小是水果分级的一个重要依据,随着计算机技术和图像处理技术的飞速发展,计算机视觉技术被广泛用于水果品质的检测。所设计的水果大小自动分级系统由机器视觉检测系统和基于PLC的水果传送分拣机构组成。使用LabbVIEW软件编写水果大小自动分级系统的监控界面,并实现对CCD摄像机的控制及获取图片;使用IMAQ Vision工具包对所获取的水果图片进行处理,并进行大小分析,根据大小的等级由LabVIEW通过串口通信发送命令给PLC,由PLC控制水果的传送并进入对应的分级口,从而完成水果的自动分级。实际运行证明该系统能实时进行动态采集图片,有效地进行水果大小分析及自动分级。     

一种基于增强现实的虚拟眼镜试戴的方法

选用单目的 CCD(Charge-coupled device)摄像头作为输入传感器,提出了一种基于标识物特征与人脸特征共同作用的增强现实技术设计并实现了一个虚拟眼镜试戴系统。通过单目摄像头采集现实场景的信息,对采集的图像进行分析,识别,并跟踪标识物与人脸特征,使虚拟眼镜模型可以准确地叠加在人脸之上,从而实现虚拟眼镜的试戴。实验证明,该方法可以准确地识别标识物,快速完成虚拟眼镜的试戴,为眼镜试戴技术提供了新的思路。     

基于全局和局部特征融合的显著性提取方法

显著性提取方法在图像处理、计算机视觉领域有着广泛的应用.然而,基于全局特征和基于局部特征的显著性区域提取算法存在各自的缺点,为此本文提出了一种融合全局和局部特征的显著性提取算法.首先,对图像进行不重叠地分块,当每个图像块经过主成分分析(Principle component analysis,PCA)映射到高维空间后,根据孤立的特征点对应显著性区域的规律得到基于全局特征的显著图;其次,根据邻域内中心块与其他块的颜色不相似性得到基于局部特征的显著图;最后,按照贝叶斯理论将这两个显著图融合为最终的显著图.在公认的三个图像数据库上的仿真实验验证了所提算法在显著性提取和目标分割上比其他先进算法更有效.     

基于机器视觉的转辙机缺口检测方法研究

针对现有转辙机缺口检测方法所存在的问题,本文介绍了一种基于机器视觉技术的转辙机缺口检测基本方法及其原理和步骤。文章详细论述了LSD算法的工作原理,同时本文作者对该算法做出了改进。最后将改进的LSD算法应用在转辙机缺口检测当中,结果表明该检测算法具有良好的稳定性和快速性,并且能够很好的解决转辙机缺口检测的问题。     

Multimodal Pre-training Method for Vision-language Understanding and Generation

Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like loss functions (masked language modeling and image-text matching) during pre-training. Despite their good performance in the understanding of downstream tasks, such as visual question answering, image-text retrieval, and visual entailment, these methods cannot generate information. To tackle this problem, this study proposes Unified multimodal pre-training for Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generation tasks. It expands existing pre-training paradigms and uses random masks and causal masks simultaneously, where causal masks are triangular masks that mask future tokens, and such pre-trained models can have autoregressive generation abilities. Moreover, several vision-language understanding tasks are turned into text generation tasks according to specifications, and the prompt-based method is employed for fine-tuning of different downstream tasks. The experiments show that there is a trade-off between understanding tasks and generation tasks when the same model is used, and a feasible way to improve both tasks is to use more data. The proposed UniVL framework attains comparable performance to recent vision-language pre-training methods in both understanding tasks and generation tasks. Moreover, the prompt-based generation method is more effective and even outperforms discriminative methods in few-shot scenarios.     

基于多尺度特征融合的皮肤烧伤创面分级算法

为实现遭受重大火灾等灾害后,对伤员皮肤烧伤自动化分级,加快诊断效率,提出提出一种用于皮肤烧伤分类的轻量化模型BI-YOLOv5算法。替换Swish激活函数,提高模型收敛能力及检测效率;使用K-means++算法对anchors聚类分析,增强对不同尺度目标的适应能力;修改特征提取网络,提取多个尺度的特征信息,建立多尺度特征融合网络,提高模型对深层特征信息的利用率,提高小面积烧伤的识别精度。实验结果表明,BI-YOLOv5算法在检测并区分不同烧伤类别及环境干扰下烧伤检测有较高的精度和效率,mAP达到97.6,对比YOLOv5提升8.4个百分点。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.