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.     

3D soft-tissue tracking using spatial-color joint probability distribution and thin-plate spline model

Visual tracking techniques based on stereo endoscope are developed to measure tissue motion in robot-assisted minimally invasive surgery. However, accurate 3D tracking of tissue surfaces remains challenging due to complicated deformation, poor imaging conditions, specular reflections and other dynamic effects during surgery. This study employs a robust and efficient 3D tracking scheme with two independent recursive processes, namely kernel-based inter-frame motion estimation and model-based intra-frame 3D matching. In the first process, target region is represented in joint spatial-color space for robust estimation. By defining a probabilistic similarity measure, a mean-shift-based iterative algorithm is derived for location of the target region in a new image. In the second process, the thin-plate spline model is used to fit the 3D shape of tissue surfaces around the target region. An iterative algorithm based on an efficient second-order minimization technique is derived to compute optimal model parameters. The two processes can be computed in parallel. Their outputs are combined to recover 3D information about the target region. The performance of the proposed method is validated using phantom heart videos and in vivo videos acquired by the daVinci^®${\mathrm{daVinci}}^{\circledR }$ surgical robotic platform and a synthesized data set with known ground truth.     

Gravity optimised particle filter for hand tracking

This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles.     

Laplacian colormaps: a framework for structure‐preserving color transformations

Mappings between color spaces are ubiquitous in image processing problems such as gamut mapping, decolorization, and image optimization for color‐blind people. Simple color transformations often result in information loss and ambiguities, and one wishes to find an image‐specific transformation that would preserve as much as possible the structure of the original image in the target color space. In this paper, we propose Laplacian colormaps, a generic framework for structure‐preserving color transformations between images. We use the image Laplacian to capture the structural information, and show that if the color transformation between two images preserves the structure, the respective Laplacians have similar eigenvectors, or in other words, are approximately jointly diagonalizable. Employing the relation between joint diagonalizability and commutativity of matrices, we use Laplacians commutativity as a criterion of color mapping quality and minimize it w.r.t. the parameters of a color transformation to achieve optimal structure preservation. We show numerous applications of our approach, including color‐to‐gray conversion, gamut mapping, multispectral image fusion, and image optimization for color deficient viewers.