Bayesian modeling of dynamic scenes for object detection

Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foregrounds modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.     

Color image segmentation—an innovative approach

In this paper we describe a color image segmentation system that performs color clustering in a color space and then color region segmentation in the image domain. For color segmentation, we developed a fuzzy clustering algorithm that iteratively generates color clusters using a uniquely defined fuzzy membership function and an objective function for clustering optimization. The fuzzy membership function represents belief value of a color belonging to a color cluster and the mutual interference of neighboring clusters. The region segmentation algorithm merges clusters in the image domain based on color similarity and spatial adjacency. We developed three different methods for merging regions in the image domain. Unlike many existing clustering algorithms, the image segmentation system does not require the knowledge about the number of the color clusters to be generated at each stage and the resolution of the color regions can be controlled by one single parameter, the radius of a cluster. The color image segmentation system has been implemented and tested on a variety of color images including satellite images, car and face images. The experiment results are presented and the performance of each algorithm in the segmentation system is analyzed. The system has shown to be both effective and efficient.     

不规则像素簇显著性检测算法

目的 显著性检测领域的研究重点和难点是检测具有复杂结构信息的显著物体。传统的基于图像块的检测算法,主要根据相对规则的图像块进行检测,计算过程中不能充分利用图像不规则的结构和纹理的信息,对算法精度产生影响。针对上述问题,本文提出一种基于不规则像素簇的显著性检测算法。方法 根据像素点的颜色信息量化颜色空间,同时寻找图像的颜色中心,将每个像素的颜色替代为最近的颜色中心的颜色。然后根据相同颜色标签的连通域形成不规则像素簇,并以连通域的中心为该簇的位置中心,以该连通域对应颜色中心的颜色为该簇整体的颜色。通过像素簇的全局对比度得到对比度先验图,利用目标粗定位法估计显著目标的中心,计算图像的中心先验图。然后将对比度先验图与中心先验图结合得到初始显著图。为了使显著图更加均匀地突出显著目标,利用图模型及形态学变化改善初始显著图效果。结果 将本文算法与5种公认表现最好的算法进行对比,并通过5组图像进行验证,采用客观评价指标精确率—召回率（precision-recall,PR）曲线以及精确率和召回率的调和平均数F-measure进行评价,结果表明本文算法在PR曲线上较其他算法表现良好,在F-measure方面相比其他5种算法均有00.3的提升,且有更佳的视觉效果。结论 本文通过更合理地对像素簇进行划分,并对目标物体进行粗定位,更好地考虑了图像的结构和纹理特征,在显著性检测中有较好的检测效果,普适性强。     

基于DCT变换的多通道特征的行人检测

在行人检测中,针对目前多通道检测算法特征利用不充分的问题,提出一种基于DCT变换的多通道特征级联的行人检测算法。通过一种2层卷积网络模型将图像信息DCT变换后的数据进行整理,形成新的频域通道特征,该通道能描述行人的复杂纹理特征。结合梯度方向直方图特征、颜色空间特征和DCT频域特征,基于Adaboost算法训练了低开销的多通道特征行人检测器。在典型的公开行人库上的实验结果表明,该方法能提高检测的性能,在较低误检率时效果更加显著。     

基于域适应的X光图像的目标检测

随着卷积神经网络的发展,X光安全检查图像的自动目标检测算法已经取得了重大进步.但是,当将这些目标检测算法应用到不同于训练集数据的新数据,即训练域数据和测试域数据的图像数据服从不一致的分布时,这些检测算法的性能通常会降低.根据X光成像的变化,提出一种基于上下文的透射率自适应域对齐方法,用于解决检测算法的域不适应问题.首先...     

Texture Mapping Real‐World Objects with Hydrographics

In the digital world, assigning arbitrary colors to an object is a simple operation thanks to texture mapping. However, in the real world, the same basic function of applying colors onto an object is far from trivial. One can specify colors during the fabrication process using a color 3D printer, but this does not apply to already existing objects. Paint and decals can be used during post‐fabrication, but they are challenging to apply on complex shapes. In this paper, we develop a method to enable texture mapping of physical objects, that is, we allow one to map an arbitrary color image onto a three‐dimensional object. Our approach builds upon hydrographics, a technique to transfer pigments printed on a sheet of polymer onto curved surfaces. We first describe a setup that makes the traditional water transfer printing process more accurate and consistent across prints. We then simulate the transfer process using a specialized parameterization to estimate the mapping between the planar color map and the object surface. We demonstrate that our approach enables the application of detailed color maps onto complex shapes such as 3D models of faces and anatomical casts.     

复合域的显著性目标检测方法

目的 针对显著性目标检测方法生成显著图时存在背景杂乱、检测区域不准确的问题,提出基于复合域的显著性目标检测方法。方法 首先,在空间域用多尺度视网膜增强算法对原图像进行初步处理;然后,在初步处理过的图像上建立无向图并提取节点特征,重构超复数傅里叶变换到频域上得到平滑振幅谱、相位谱和欧拉谱,通过多尺度高斯核的平滑,得到背景抑制图;同时,利用小波变换在小波域上的具有多层级特性对图像提取多特征,并计算出多特征的显著性图;最后,利用提出的自适应阈值选择法将背景抑制图与多特征的显著性图进行融合,选择得到最终的显著图。结果 对标准测试数据集MSRA10K和THUR15K中的图像进行显著性目标检测实验,同目前较流行的6种显著性目标检测方法对比,结果表明上述问题通过本文方法得到了很好地解决,即使在背景复杂的情况下,本文算法的准确率、召回率均高于对比算法,在MSRA10K数据集中,平均绝对误差（MAE）值为0.106,在THUR15K数据集中,平均绝对误差（MAE）值降低至0.068,平均结构性指标S-measure值为0.844 9。结论 基于复合域的显著性目标检测方法,融合多个域的优势,在抑制杂乱的背景的同时提高了准确率,适用于自然景物、生物、建筑以及交通工具等显著性目标图像的检测。     

基于小波变换的彩色图象数字水印算法

提出了一种基于离散小波变换（DWT）域的彩色图象序列加密数字水印新方法，算法选用了彩色图象RGB色彩空间的G分量嵌入水印，数字水印图象含有密钥信息，因此算法具有很好的安全性。同时利用人类视觉系统（HVS）的亮度掩蔽特性和纹理掩蔽特性，把低频分量根据局部纹理的强弱分成两类，将二值水印图象加密后自适应的嵌入到宿主图象的DWT域的低频分量中，从而很好的兼顾了水印的不可见性与鲁棒性。大量仿真结果表明本算法对于诸如JPEG压缩、高斯噪声干扰和图象剪切具有很好的鲁棒性。     

Salient object detection using content-sensitive hypergraph representation and partitioning

As an important problem in image understanding, salient object detection is essential for image classification, object recognition, as well as image retrieval. In this paper, we propose a new approach to detect salient objects from an image by using content-sensitive hypergraph representation and partitioning. Firstly, a polygonal potential Region-Of-Interest (p-ROI) is extracted through analyzing the edge distribution in an image. Secondly, the image is represented by a content-sensitive hypergraph. Instead of using fixed features and parameters for all the images, we propose a new content-sensitive method for feature selection and hypergraph construction. In this method, the most discriminant color channel which maximizes the difference between p-ROI and the background is selected for each image. Also the number of neighbors in hyperedges is adjusted automatically according to the image content. Finally, an incremental hypergraph partitioning is utilized to generate the candidate regions for the final salient object detection, in which all the candidate regions are evaluated by p-ROI and the best match one will be the selected as final salient object. Our approach has been extensively evaluated on a large benchmark image database. Experimental results show that our approach can not only achieve considerable improvement in terms of commonly adopted performance measures in salient object detection, but also provide more precise object boundaries which is desirable for further image processing and understanding.     

融合目标增强与稀疏重构的显著性检测

目的 为了解决图像显著性检测中存在的边界模糊,检测准确度不够的问题,提出一种基于目标增强引导和稀疏重构的显著检测算法（OESR）。方法 基于超像素,首先从前景角度计算超像素的中心加权颜色空间分布图,作为前景显著图;由图像边界的超像素构建背景模板并对模板进行预处理,以优化后的背景模板作为稀疏表示的字典,计算稀疏重构误差,并利用误差传播方式进行重构误差的校正,得到背景差异图;最后,利用快速目标检测方法获取一定数量的建议窗口,由窗口的对象性得分计算目标增强系数,以此来引导两种显著图的融合,得到最终显著检测结果。结果 实验在公开数据集上与其他12种流行算法进行比较,所提算法对具有不同背景复杂度的图像能够较准确的检测出显著区域,对显著对象的提取也较为完整,并且在评价指标检测上与其他算法相比,在MSRA10k数据集上平均召回率提高4.1%,在VOC2007数据集上,平均召回率和F检验分别提高18.5%和3.1%。结论 本文提出一种新的显著检测方法,分别利用颜色分布与对比度方法构建显著图,并且在显著图融合时采用一种目标增强系数,提高了显著图的准确性。实验结果表明,本文算法能够检测出更符合视觉特性的显著区域,显著区域更加准确,适用于自然图像的显著性目标检测、目标分割或基于显著性分析的图像标注。     

Genetic programming based blind image deconvolution for surveillancesystems

Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.     

结合兴趣点和边缘的建筑物和物体识别方法

提出了多种图像特征相结合的建筑物和物体识别方法．使用尺度不变特征描述器描述的Harris-Laplace兴趣点以及边缘颜色直方图描述的边缘特征表示图像．边缘和兴趣点包含图像的重要信息．对2种特征的抽取同时进行：基于Harris检测器可以直接得到边缘特征;在多个尺度下进行Harris兴趣点检测,利用Laplace公式得到Harris-Laplace兴趣点．进行物体识别时,根据兴趣点的数目自适应地改变兴趣点和边缘特征的相似性权重．与同类方法相比较表明,该方法具有更高的识别正确率,在视点变化、光照条件变化等情况下具有较好的性能．     

面向工业零件分拣系统的低纹理目标检测

目的 随着工业领域智能分拣业务的兴起,目标检测引起越来越多的关注。然而为了适应工业现场快速部署和应用的需求,算法只能在获得少量目标样本的情况下调整参数;另外工控机运算资源有限,工业零件表面光滑、缺乏显著的纹理信息,都不利于基于深度学习的目标检测方法。目前普遍认为Line2D可以很好地用于小样本情况的低纹理目标快速匹配,但Line2D不能正确匹配形状相同而颜色不同的两个零件。对此,提出一种更为鲁棒的低纹理目标快速匹配框架CL2D (color Line2D)。方法 首先使用梯度方向特征作为物体形状的描述在输入图像快速匹配,获取粗匹配结果;然后通过非极大值抑制和颜色直方图比对完成精细匹配。最后根据工业分拣的特点,由坐标变换完成对目标的抓取点定位。结果 为了对算法性能进行测试,本文根据工业分拣的实际环境,提出了YNU-BBD 2020(YNU-building blocks datasets 2020)数据集。在YNU-BBD 2020数据集上的测试结果表明,CL2D可以在CPU平台上以平均2.15 s/幅的速度处理高分辨率图像,在精度上相比于经典算法和深度学习算法,mAP (mean average precision)分别提升了10%和7%。结论 本文针对工业零件分拣系统的特点,提出了一种快速低纹理目标检测方法,能够在CPU平台上高效完成目标检测任务,并且相较于现有方法具有显著优势。     

Salient object detection using color spatial distribution and minimum spanning tree weight

Salient object detection is very useful in many computer vision applications such as image segmentation, content-based image editing and object recognition. In this paper, we present a salient object detection algorithm by using color spatial distribution (CSD) and minimum spanning tree weight (MSTW). We first use a segmentation algorithm to decompose an image into superpixel-level elements, then use these elements as nodes to construct a minimum spanning tree (MST), each connected edge weight is the mean color difference between two nodes. CSD of each element can be computed by integrating color, spatial distance and MSTW. Note that if the color of one element is the most widely distributed over the entire image, it should have the biggest CSD value, we regard this element as a background node (BG Node). Then we use the MSTW between other element and BG node to generate a MSTW map. The superpixel-level saliency map can be obtained by combining the CSD map and MSTW map. Finally, we use a guided filter to get the pixel-level saliency map. Experimental results on two databases demonstrate that our proposed method outperforms other previous state-of-the-art approaches.     

直方图反向投影多目标检测优化算法

针对目标检测中图像背景信息复杂以及反向投影方法目标颜色类型单一的问题,提出了一种改进的直方图反向投影目标检测优化算法．在建立多个目标模型弥补一般反向投影方法不能同时检测含有不同颜色信息目标这一不足的同时,通过对目标概率图像进行一系列优化处理以减小复杂背景对目标检测的干扰．实验结果表明,该算法在满足对视频图像实时处理要求的同时可准确检测目标位置,且检测效果及计算效率优于传统反向投影算法及其他方法．     

结合四元数与最小核值相似区的边缘检测

目的 针对传统彩色图像边缘检测方法中未充分利用图像色度信息、颜色模型间非线性转换过程中时间和空间的大量耗费、算法实现复杂等问题,将四元数引入最小核值相似区（SUSAN）算法中,提出一种RGB空间下的结合四元数与最小核值相似区的边缘检测算法。方法 该算法首先对彩色图像进行四元数描述,然后用改进的SUSAN算子进行边缘检测。针对其中单一几何阈值g的限制,以及检测出的边缘较粗等问题,本文采用Otsu算法自适应获取双几何阈值,再对弱边缘点集进行边缘生长,最后根据USAN重心及其对称最长轴来确定边缘局部方向,实现对边缘点的局部非极大值抑制,得到最终细化后的边缘图像。结果 实验选取1幅合成彩色图像及3幅标准图像库图像,与彩色Canny算法、SUSAN算法,及采用单阈值的本文算法进行对比,并采用Pratt品质因数衡量边缘定位精度。本文算法能够检测出亮度相近的不同颜色区域之间的边缘,且提取的边缘比较连续、细致,漏检边缘较少。与公认边缘检测效果较好的彩色Canny算法相比,本文算法的品质因数提高了0.012 0,耗时缩短了2.527 9 s。结论 本文提出了一种结合四元数与最小核值相似区的边缘检测算法,实现了四元数与SUSAN算子的有效融合。实验结果表明,该算法能够提高边缘定位精度,对弱噪声具有较好的抑制能力,适用于对实时性要求不高的低层次彩色图像处理。     

图象模糊涟缘检测的改进算法

图象在检测技术是图象处理中最重要的内容之一,且已在图象分析和识别领域中得到广泛的应用。针对图象边缘由模糊性引起的不确定性问题,提出了一种图象模糊边缘检测的改进算法,该算法是道德民确定一个阈值参数,然后根据此阈值参数来定义一个新的隶属函数,从而钭图象转化为等效的图象模糊特征平面,通过在模糊特征平面上进行增强运算,将其转换为空域图象,最后再进行边缘提取,同时还对具有多峰直方图分布图象的模糊边缘检测方法进行推广,仿真结果表明,该算法是有效的。     

结合聚合通道和复频域特征的防震锤检测算法

为了解决目前输电线路防震锤的检测采用数字图像处理的方法时,受到复杂背景的影响,导致防震锤检测精度偏低的问题,提出一种结合多尺度聚合通道特征(ACF)和复频域特征在图像复杂背景下防震锤的检测算法。首先,引入聚合通道特征,分别提取图像颜色特征、梯度幅值和梯度方向直方图特征金字塔,构建多尺度ACF;同时,使用多方向对偶树复小波变换(M-DTCWT)对图像进行多尺度多方向复频域变换,在分解得到的低频子带图像中提取图像的形状特征和纹理特征;然后,使用Relief-F算法将得到的ACF特征和复频域特征进行加权融合;最后,采用Adaboost分类器和非极大值抑制算法(NMS)实现图像中防震锤的检测。实验结果表明,该算法与传统提取图像的单特征识别方法相比,提高了在复杂背景下对防震锤检测的精度。     

联合聚焦度和传播机制的光场图像显著性检测

目的 图像显著性检测方法对前景与背景颜色、纹理相似或背景杂乱的场景,存在背景难抑制、检测对象不完整、边缘模糊以及方块效应等问题。光场图像具有重聚焦能力,能提供聚焦度线索,有效区分图像前景和背景区域,从而提高显著性检测的精度。因此,提出一种基于聚焦度和传播机制的光场图像显著性检测方法。方法 使用高斯滤波器对焦堆栈图像的聚焦度信息进行衡量,确定前景图像和背景图像。利用背景图像的聚焦度信息和空间位置构建前/背景概率函数,并引导光场图像特征进行显著性检测,以提高显著图的准确率。另外,充分利用邻近超像素的空间一致性,采用基于K近邻法（K-nearest neighbor,K-NN）的图模型显著性传播机制进一步优化显著图,均匀地突出整个显著区域,从而得到更加精确的显著图。结果 在光场图像基准数据集上进行显著性检测实验,对比3种主流的传统光场图像显著性检测方法及两种深度学习方法,本文方法生成的显著图可以有效抑制背景区域,均匀地突出整个显著对象,边缘也更加清晰,更符合人眼视觉感知。查准率达到85.16%,高于对比方法,F度量（F-measure）和平均绝对误差（mean absolute error,MAE）分别为72.79%和13.49%,优于传统的光场图像显著性检测方法。结论 本文基于聚焦度和传播机制提出的光场图像显著性模型,在前/背景相似或杂乱背景的场景中可以均匀地突出显著区域,更好地抑制背景区域。