Shadow detection via multi-scale feature fusion and unsupervised domain adaptation

Shadow detection is significant for scene understanding. As a common scenario, soft shadows have more ambiguous boundaries than hard shadows. However, they are rarely present in the available benchmarks since annotating for them is time-consuming and needs expert help. This paper discusses how to transfer the shadow detection capability from available shadow data to soft shadow data and proposes a novel shadow detection framework (MUSD) based on multi-scale feature fusion and unsupervised domain adaptation. Firstly, we set the existing labeled shadow dataset (i.e., SBU) as the source domain and collect an unlabeled soft shadow dataset (SSD) as the target domain to formulate an unsupervised domain adaptation problem. Next, we design an efficient shadow detection network based on the double attention module and multi-scale feature fusion. Then, we use the global–local feature alignment strategy to align the task-related feature distributions between the source and target domains. This allows us to obtain a robust model and achieve domain adaptation effectively. Extensive experimental results show that our method can detect soft shadows more accurately than existing state-of-the-art methods.     

Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.     

融入残差注意力机制的DeepLabV3+图像拼接篡改取证网络

针对现有图像拼接检测网络模型存在边缘信息关注度不够、像素级精准定位效果不够好等问题,提出一种融入残差注意力机制的DeepLabV3+图像拼接篡改取证方法,该方法利用编-解码结构实现像素级图像的拼接篡改定位。在编码阶段,将高效注意力模块融入ResNet101的残差模块中,通过残差模块的堆叠以减小不重要的特征比重,凸显拼接篡改痕迹;其次,利用带有空洞卷积的空间金字塔池化模块进行多尺度特征提取,将得到的特征图进行拼接后通过空间和通道注意力机制进行语义信息建模。在解码阶段,通过融合多尺度的浅层和深层图像特征提升图像的拼接伪造区域的定位精度。实验结果表明,在CASIA 1.0、COLUMBIA和CARVALHO数据集上的拼接篡改定位精度分别达到了0.761、0.742和0.745,所提方法的图像拼接伪造区域定位性能优于一些现有的方法,同时该方法对JPEG压缩也具有更好的鲁棒性。     

Image-Based Traffic Monitoring With Shadow Suppression

For a vision-based traffic monitoring and enforcement system, shadows of moving objects often cause serious errors in image analysis due to misclassification of shadows and moving vehicles. An effective shadow suppression method is thus required to improve the accuracy of image analysis and this paper proposes a novel color-space ratio model for detecting shadow pixels in traffic imagery. The proposed approach does not require many image sequences for constructing the model. Instead the model can be easily built up using a shadow region in a single image frame. To increase the accuracy of shadow detection, we design two types of spatial analysis to verify actual shadow pixels. Comparative results show that the proposed method works better than several well-known methods. The proposed methods have been applied to an image-based traffic monitoring system for detecting shadow pixels in traffic imagery. The experimental results not only validate the feasibility of the proposed algorithm but also successfully estimate traffic parameters such as traffic flows, traffic densities, vehicle turn ratios and vehicle speeds, all with satisfactory accuracy     

先验引导的特征金字塔阴影检测网络

阴影检测向来是计算机视觉领域的一个基础性挑战。它需要网络理解图像的全局语义和局部细节信息。本文提出了一种检测阴影区域的先验特征金字塔网络结构。该网络搭建了先验加权模块来提取图像中蕴含的阴影先验信息,通过使用阴影先验信息加权卷积特征,引导网络学习到阴影区域。同时,该网络还应用了特征融合模块来融合粗略的语义信息和自上而下路径中的精细特征,并且加入了后处理,进一步优化网络的预测结果。本文在两个公开的阴影检测基准数据集上进行了实验来评估其网络性能。实验表明,本文的方法能够更准确地检测到阴影,和过去最先进的方法相比也表现出色,在SBU数据集上正确率达到了96.6%,平衡检测错误因子为6.22。      

融合多层级特征的脑肿瘤图像分割方法北大核心CSCD

针对脑肿瘤图像分割中网络模型信息损耗、上下文信息联系不足及网络泛化能力较差导致分割精度较低的问题,提出了一种新型的脑肿瘤图像分割方法,该方法是通过深度门控卷积模块(depth gate convolution,DGC)和特征增强模块(feature enhancement module,FEM)组成的多层级连接(multi-level connection,MC)脑肿瘤分割模型。采用深度卷积模块降低特征信息在逐层传递的信息损耗;使用控制门单元(control gate unit,CGU)实现各个尺度的特征图的MC,其中组合池化来减少下采样过程中的信息丢失;通过FEM增强分割区域的特征权重。实验结果表明,预测分割脑肿瘤的整体肿瘤区(whole tumor,WT)、核心肿瘤区(tumor core,TC)和增强肿瘤区(enhancement tumor,ET)的Dice系数分别达到了0.92、0.84和0.83,Hausdorff距离达到了0.77、1.50和0.92,脑肿瘤分割精度相较于当前较多方法分割精度和计算效率较高,具有良好的分割性能。     

双向特征融合的快速精确任意形状文本检测

现有的基于分割的场景文本检测方法仍较难区分相邻文本区域,同时网络得到分割图后后处理阶段步骤复杂导致模型检测效率较低.为了解决此问题,该文提出一种新颖的基于全卷积网络的场景文本检测模型.首先,该文构造特征提取器对输入图像提取多尺度特征图.其次,使用双向特征融合模块融合两个平行分支特征的语义信息并促进两个分支共同优化.之后,该文通过并行地预测缩小的文本区域图和完整的文本区域图来有效地区分相邻文本.其中前者可以保证不同的文本实例之间具有区分性,而后者能有效地指导网络优化.最后,为了提升文本检测的速度,该文提出一个快速且有效的后处理算法来生成文本边界框.实验结果表明:在相关数据集上,该文所提出的方法均实现了最好的效果,且比目前最好的方法在F-measure指标上最多提升了1.0％,并且可以实现将近实时的速度,充分证明了该方法的有效性和高效性.     

基于3D全卷积网络的腹部动脉CTA分割算法

目前卷积神经网络已成为腹部动脉血管分割领域的研究热点,但经典的卷积网络存在分割精度低和分割血管不连续的问题。为此,文中提出了基于改进3D全卷积网络的腹部动脉血管分割算法。该方法在网络的编码路径上构造不同尺度的侧输入,并将侧输入卷积后的图像与下采样卷积后的图像进行融合,提取更多的特征信息。同时,网络中嵌入了新的多尺度特征提取模块,该模块将通道注意力与密集扩张卷积进行了融合,有效地捕获了更高层次的特征信息。对腹部动脉血管进行分割的结果表明,与其他分割方法相比,所提方法在直观性和定量性上均有提高,证明了该方法能够提升血管分割精度。     

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods.     

基于帧差法的运动车辆阴影去除算法

在视频交通车辆目标检测中,阴影问题是影响其检测准确性的关键问题之一.为了解决这个问题,提出了一种结合单模高斯模型和帧差法的运动目标阴影去除方法.首先通过单模高斯模型背景建模获取前景包括阴影在内的目标图像,再结合帧差法去除阴影.实验结果证明,该方法得到的车辆目标比较完整,并较好地去除了运动车辆阴影.     

Human action recognition employing negative space features

We proposed a region based method to recognize human actions from video sequences. Unlike other region based methods, it works with the surrounding regions of the human silhouette termed as negative space. This paper further extends the idea of negative space to cope with the changes in viewpoints. It also addresses the problem of long shadows which is one of the major challenges of human action recognition. Some systems attempt suppressing shadows during the segmentation process but our system takes input of segmented binary images of which the shadow is not suppressed. This makes our system less dependent on segmentation process. Further, this approach can complement the positive space (silhouette) based methods to boost recognition. The system consists of a hierarchical processing: histogram analysis on segmented input image, followed by motion and shape feature extraction, pose sequence analysis by employing Dynamic Time Warping and at last classification by Nearest Neighbor classifier. We evaluated our system by most commonly used datasets and achieved higher accuracy than the state of the arts methods. Our system can also retrieve video sequences from queries of human action sequences.     

城市航空影像中基于颜色恒常性的阴影消除

基于颜色恒常性理论,对真彩色和彩红外城市航空影像中高大建筑物形成的阴影进行消除。首先采用光谱比技术和最大类间方差法(Otsu)阈值分割技术进行城市航空影像中建筑物阴影的检测,进而就颜色恒常计算的Shades of Gray算法中明可夫斯基范式(Minkowski norm)的p取不同值情况下的阴影去除效果进行实验,利用亮度、对比度及平均梯度值比较阴影去除效果的好坏。实验表明:在基于航空影像阴影区域及非阴影区域划分的基础上,本文方法比一般的阴影区反差拉伸方法效果好;且与一般场景影像的阴影去除不同,对两类航空影像,p取2时阴影去除效果最佳,说明这两类影像不能简单看成是一个灰色世界影像。     

Joint moving cast shadows segmentation and light source detection in video sequences

This paper proposes a new method which allows a joint estimation of the light source projection on the image plane and the segmentation of moving cast shadows in natural video sequences. It allows improving the segmentation of moving objects by separating clearly cast shadows from moving objects. The method is based on a shadow model which mainly assumes that the cast shadows are projected on plane and Lambertian surfaces, and that the light source is unique. The moving cast shadows, including the penumbra, are detected using a segmentation method based on a comparison between a reference image and the original one. The light source position is estimated using geometrical relations linking the light source, the object and its cast shadow on the 2-D image plane. This is obtained using a robust temporal filtering method. For each image using the current estimation of the light source position and the video object contours, a cast shadow search area is defined. This reduces the risk of false detections during the segmentation process, and thus allows increasing the detection rate and reducing the false alarm one. Experimental results show that good shadow and object contours and light source locations are obtained with the proposed method even if the theoretical assumptions are not fully valid.     

使用闪光灯图像对的闪光灯阴影检测

人们在光线较暗的环境拍摄照片时,经常使用闪光灯来增强光照。但闪光灯的使用会引起一些不良效应,如红眼和闪光灯阴影。检测并去除闪光灯图像中的闪光灯阴影区域,会显著提高对象检测和识别等视觉任务的性能。提出了一种使用闪光灯图像对的闪光灯阴影检测算法。它基于以下假设:闪光灯阴影边缘点只会出现在闪光灯图像中,并且闪光灯阴影区域的灰度值低于非闪光灯图像对应区域的灰度值。所提算法包括三个步骤:预处理、闪光灯阴影边缘点检测和闪光灯阴影检测。仿真结果和与已有方法的比较都验证了所提方法的有效性。      

上下文信息融合与分支交互的SAR图像舰船无锚框检测

SAR图像中舰船目标稀疏分布、锚框的设计,对现有基于锚框的SAR图像目标检测方法的精度和泛化性有较大影响,因此该文提出一种上下文信息融合与分支交互的SAR图像舰船目标无锚框检测方法,命名为CI-Net.考虑到SAR图中舰船尺度的多样性,在特征提取阶段设计上下文融合模块,以自底向上的方式融合高低层信息,结合目标上下文信息...     

基于低尺度细节恢复的单幅图像阴影去除方法

为了在光照复杂、纹理丰富的图像上获得更好的去阴影效果,基于生成对抗网络提出了一种新颖的阴影去除方法.首先,所提网络中的阴影检测子网为阴影图像生成阴影掩膜,基于该检测结果提出一种光照敏感的多尺度图像分解方法,在几乎不损失光照信息的同时提取图像纹理信息;然后,蒙版生成子网为分解后的低尺度图像生成相应的蒙版用于去除其中阴影;其次,边界复原子网修复阴影边界实现友好的过渡;最后,使用自适应衰减因子引导图像进行细节恢复以得到纹理丰富的结果.实验结果表明所提方法可以有效地提高阴影去除效果.     

一种基于纹理的车辆阴影消除新算法

基于对智能交通系统(ITS,Intelligent Transport Systems)中视频检测的研究和分析,特别针对其中关键步骤之一的阴影消除展开深入探讨,分析了阴影产生的原理和特点,阐述了现有的阴影去除算法,在现有算法的基础上提出了一种基于区域聚类的阴影消除算法.实践证明,该方法能够较好的去除运动车辆的阴影,保留...     

基于跨模态空间匹配的多模态肺部肿块分割网络

现有多模态分割方法通常先对图像进行配准,再对配准后的图像进行分割.对于成像特点差异较大的不同模态,两阶段的结构匹配与分割算法下的分割精度较低.针对该问题,该文提出一种基于跨模态空间匹配的多模态肺部肿块分割网络(MMSASegNet),其具有模型复杂度低和分割精度高的特点.该模型采用双路残差U型分割网络作为骨干分割网络,...     

基于强化特征提取的视网膜血管分割

视网膜血管的分割精确率对眼科疾病和糖尿病早期诊断有着重要影响。面对现有方法在微血管与病变区域分割性能差的问题,本文提出一种强化提取血管特征的分割模型。该模型在编码部位引入多尺度特征提取残差模块(multi-scale feature extraction residual module,MFE-residual) 和多级残差空洞卷积层,用来扩展感受野,学习多层次图像特征,提高模型对血管信息的利用率;下采样和短连接部位分别融入轻量化注意力机制和多通道注意力模块,增加模型对血管的识别度,降低误分割的可能性。本文基于DRIVE和STARE两种公开数据集进行了实验,来验证改 进模型的分割能力。结果表明,两种数据上的准确率分别为0.965 2和0.971 5,灵敏度分别为0.820 5和0.825 6,与其他算法相比,分割性能更有优势。     

融合注意力分支特征的甲烷泄漏红外图像分割

甲烷是现代化工业生产和社会生活的重要能源之一,实现其有效探测与分割对于及时发现甲烷泄漏事故并识别其扩散范围具有重要意义。针对红外成像条件下甲烷气体图像的轮廓模糊、泄漏的甲烷气体与背景对比度较低、形状易受大气流动因素影响等问题,本文提出一种融合注意力分支特征的红外图像分割网络（Attention Branch Feature Network,ABFNet）实现甲烷气体泄漏探测。首先,为增强模型对红外甲烷气体图像的特征提取能力,设计分支特征融合模块将残差模块1和残差模块2的输出特征与残差模块3以逐像素相加的方法融合,获取红外甲烷气体图像丰富细致的特征表达以提高模型识别精度。其次,为进一步加快模型的推理速度,将标准瓶颈单元中的3×3卷积替换为深度可分离卷积,大幅度减少参数量达到实时检测甲烷气体泄漏。最后,将scSE注意力机制嵌入到分支特征融合模块,更多地关注扩散区域边缘和中心语义信息以克服红外甲烷气体轮廓模糊对比度低等问题提高模型的泛化能力。实验结果表明,本文提出的ABFNet模型AP50@95、AP50、AP60定量分割精度分别达到38.23%、89.63%和75.33%,相比于原始YOL...