基于样本选择的RGBD图像协同显著目标检测

协同显著目标检测的目的是在包含两张及以上相关图像的图像组中检测共同显著的物体。该文提出一种利用机器学习的方法对协同显著目标进行检测。首先,基于4个评分指标从图像组中选择部分显著目标易于检测的简单图像,构成简单图像集;接着,基于协同一致性的原则,从简单图像集中提取正负样本,并用深度学习模型提取的高维语义特征表示正负样本;再者,利用正负样本训练的协同显著分类器对图像中的超像素进行分类,得到协同显著目标区域;最后,经过一个平滑融合的操作,得到最终的协同显著图。在公开数据集上的测试结果表明,所提算法在检测精度和检测效率上优于目前的主流算法,并具有较强的鲁棒性。     

基于深度学习的RGBD图像协同显著目标检测

本文旨在研究一种基于深度学习的RGBD图像协同显著目标检测模型。首先,本文构建了多分支的编码器结构,有效地提取RGBD图像的深层卷积特征;然后,使用多模态特征融合模块充分融合来自编码器的深层特征;最后,通过基于残差基本块的解码器来预测得到显著性图。此外,本文以深层次监督的方式对整个网络进行约束优化。在两个公开数据集上的测试结果表明,所提模型在预测精度上优于当前6种主流模型,这其中我们的显著性图呈现出更精确的边缘细节。      

基于随机森林的雷达目标多维特征检测方法

传统基于统计模型的雷达目标检测算法往往仅利用雷达探测回波的能量信息分辨雷达目标,其难以适应密集杂波环境下的雷达探测感知场景,从而显著降低雷达目标探测性能。针对密集杂波背景下的雷达目标检测难题,本文引入随机森林算法,提出一种基于随机森林的雷达目标多维特征检测方法。利用随机森林方法充分融合雷达探测目标回波中时间维、空间维、距离维等多个维度的特征,形成对雷达目标与环境杂波差异的深度刻画,从而实现精准鲁棒的雷达目标检测,显著提升雷达的目标检测性能。最终,基于雷达实测数据对本文所提方法的性能进行验证,实验结果充分验证了本文所提算法的有效性。     

融合显著深度特征的RGB-D图像显著目标检测

深度信息被证明是人类视觉的重要组成部分,然而大部分显著性检测工作侧重于2维图像上的方法,并不能很好地利用深度进行RGB-D图像显著性检测。该文提出一种融合显著深度特征的RGB-D图像显著目标检测方法,提取基于颜色和深度显著图的综合特征,根据构图先验和背景先验的方法进行显著目标检测。首先,对原始深度图进行预处理：使用背景顶点区域、构图交点和紧密度处理深度图,多角度融合形成深度显著图,并作为显著深度特征,结合颜色特征形成综合特征;其次,从前景角度,将综合特征通过边连接权重构造关联矩阵,根据构图先验,假设多层中心矩形为前景种子,通过流形排序方法计算出RGB-D图像的前景显著图;从背景角度,根据背景先验以及边界连通性计算出背景显著图;最后,将前景显著图和背景显著图进行融合并优化得到最终显著图。实验采用RGB-D1000数据集进行显著性检测,并与4种不同的方法进行对比,所提方法的显著性检测结果更接近人工标定结果,PR(查准率-查全率)曲线显示在相同召回率下准确率高于其他方法。     

基于跨模态特征融合的RGB-D显著性目标检测

显著性目标检测(SOD)作为目前计算机视觉以及计算机图形学领域中研究的基本课题之一,是许多其他复杂任务的预处理阶段的任务,对例如图像理解与解释、视觉追踪、语义分割,视频分析等对象级应用的发展起到了极大的推动作用。随着深度传感器的普及,深度图像中蕴含的空间信息线索在显著性检测研究中提供了与RGB图像中蕴含的不同模态的辅助补充特征信息,这对于检测精度的提升来说愈发重要,因此如何有效地融合RGB与深度图像中的不同模态间的特征信息成为了RGB-D显著性目标检测课题中研究的重要问题。针对RGB与Depth模态间的特征融合问题,本文设计了一种基于跨模态特征信息融合的双流RGB-D显著目标检测网络模型,通过使用设计的跨模态特征融合模块去除某些低质量深度图带入的冗余与噪音,随后提取放大被优化改良过后的深度特征线索与RGB特征线索间的相似性与差异性,完成跨模态特征信息的有效融合。除此之外在网络编码结构的顶端增加了改良的非局部模块,通过自注意力机制更好地捕捉了的上下文信息以及像素间的长距离依赖。通过使用的两个数据集上的实验表明,这一模型在4个评价指标上取得了较好的表现。     

基于显著图和稳定区域融合的小目标检测算法

在真实场景中,物体的尺寸往往是多样的,基于大 图像的目标检测很难检测所有的物体。为了检测较小尺寸目标,本文利用显著图和稳定区域 融合,建立小目标检测算法模型。首先利用基于颜色名空间的显著性检测算法生成显著图, 同时采用基于最大稳定极值区域(MSER)算法提取局部稳定区域,MSER算法是目前针对图像 变形最为稳定的特征检测算法;其次采用像素乘性融合稳定区域和显著图以降低虚警概率; 最后调用一些图像处理过程,包括形态学重建操作、灰度变换、形态空穴填充操作,能够有 效抑制背景,同时均匀的突出显著性目标,以推断和优化最终结果。为了验证该算法的有效 性和实用性,以PR曲线为评价指标,比较了几种主流算法的性能,包括AZ-NET、FPN、PGAN 。通过对Sky数据集和Ground数据集的测试,表明该算法能够很好地适应目标尺寸的变化, 在检准率和检全率方面优于现有的小目标检测算法,具有良好的鲁棒性。     

基于Faster区域卷积神经网络的目标检测

针对Faster区域卷积神经网络目标检测算法,提出了一种自适应候选区域建议网络.在训练过程中根据当前损失反馈调节候选区域数目,使候选区域在一定范围内动态变化,进而节省开销,并记录下表现最好的候选区域数目;在测试时用记录的候选区域数目进行测试.针对Softmax函数对候选区域进行分类时需要人为选取置信度阈值带来的时间成本和小目标检测准确率下降问题,提出了一种自适应置信度阈值选取算法.实验结果表明,相比传统算法,本算法的检测速度提升了25％,平均检测精度提高了1.9个百分点.     

红外图像显著目标检测算法

提出一种简单快速的红外图像显著目标检测算法,算法可以分为三步:首先,对原始红外图像进行预处理以增强目标与背景的对比度;然后,在log频谱中提取预处理后图像的频谱残差,通过相应的反变换及简单的阈值分割,可以得到显著目标的大致区域;最后,采用一个滑动窗口在目标候选区域内进行搜索确定显著目标的准确位置,这个过程采用由目标及其周围区域在原始图像中的灰度分布得到的半局部特征对比度的概率表达得到每个像素点的显著性值,进行阈值分割得到显著目标,改变滑动窗口的大小可以检测出不同尺度的目标。采用大量的红外图像对算法进行测试,实验结果表明该算法具有高效性和鲁棒性。     

基于结构标签学习的显著性目标检测

提出了一种基于结构标签学习的显著性目标检测算法,将结构化学习方法应用到显著性目标检测中。首先从含有标记的图像中随机采集固定大小的矩形区域,并记录其结构标签;然后使用含结构标签的区域特征构建决策树集合;最后采用监督学习的方法对图像进行优化预测,得到最终的显著图。实验结果表明,本文方法能较准确地检测出图像库中图像的显著性区域,在数据库MSRA5000和BSD300的AUC值分别为0.891 8、0.705 2,说明本文方法具有较好的显著性检测效果。     

基于多尺度信息处理和Gabor初始化的图像显著性对象检测

基于全卷积网络的图像显著性检测获得了广泛的关 注,并取得了令人瞩目的检测性能 。然而,该类型神经网络依然存在许多问题,如高复杂网络导致难以训练、显著性对象边缘 结果不准确等。针对这些问题,本文提出基于Gabor初始化的卷积神经网络。该网络主要特 点包括:1) 利用Gabor特征初始化卷积神经网络,提高神经网络训练效率; 2) 构建多尺 度 桥接模块,有效衔接编码和解码阶段,进而提高显著性检测结果; 3) 提出加权交叉熵损失 函数,提高训练效果。实验结果表明,本文提出的神经网络在三个不同的数据集上均显示出 优异的显著性对象检测性能。     

High-level background prior based salient object detection

Salient object detection is a fundamental problem in computer vision. Existing methods using only low-level features failed to uniformly highlight the salient object regions. In order to combine high-level saliency priors and low-level appearance cues, we propose a novel Background Prior based Salient detection method (BPS) for high-quality salient object detection.Different from other background prior based methods, a background estimation is added before performing saliency detection. We utilize the distribution of bounding boxes generated by a generic object proposal method to obtain background information. Three background priors are mainly considered to model the saliency, namely background connectivity prior, background contrast prior and spatial distribution prior, allowing the proposed method to highlight the salient object as a whole and suppress background clutters.Experiments conducted on two benchmark datasets validate that our method outperforms 11 state-of-the-art methods, while being more efficient than most leading methods.     

Global guidance-based integration network for salient object detection in low-light images

Most of current salient object detection (SOD) methods focus on well-lit scenes, and their performance drops when generalized into low-light scenes due to limitations such as blurred boundaries and low contrast. To solve this problem, we propose a global guidance-based integration network (G2INet) customized for low-light SOD. First, we propose a Global Information Flow (GIF) to extract comprehensive global information, for guiding the fusion of multi-level features. To facilitate information integration, we design a Multi-level features Cross Integration (MCI) module, which progressively fuses low-level details, high-level semantics, and global information by interweaving. Furthermore, a U-shaped Attention Refinement (UAR) module is proposed to further refine edges and details for accurate saliency predictions. In terms of five metrics, extensive experimental results demonstrate that our method outperforms the existing twelve state-of-the-art models.     

多边形近似及形状特征匹配的二维目标检测

在计算机视觉中形状是目标识别和检测的重要特征,而目标边缘是形状特征最直接的表现,因此基于边缘信息进行形状特征描述是最直接有效的方法.针对目前大多数形状特征描述的全局性以及对旋转、缩放等变化的敏感性,采用一种基于目标近似多边形的形状特征描述,这种描述方式具有局部性和紧凑性,同时结合运动参数预测及递归估计的方法实现二维目标...     

基于特征帧构建的运动目标检测方法

非参数密度估计在样本分析建模方面得到了很大的关注,尤其是核密度估计方法。但由于核密度估计方法计算量大,应用到运动目标检测方面很难达到实时效果。提出了一种特征帧构建的核密度估计方法。因为核密度估计不需要假设背景模型的密度分布函数,所有样本值又满足独立同分布的原则,所以可以通过特征帧构建的方法进行背景建模,同时应用此方法进行背景更新。实验结果表明:该方法能够适应环境变化且具有运算速度快、实时性好等特点,可以将其应用到复杂背景下的监控系统中。     

Bayesian salient object detection based on saliency driven clustering

Salient object detection is essential for applications, such as image classification, object recognition and image retrieval. In this paper, we design a new approach to detect salient objects from an image by describing what does salient objects and backgrounds look like using statistic of the image. First, we introduce a saliency driven clustering method to reveal distinct visual patterns of images by generating image clusters. The Gaussian Mixture Model (GMM) is applied to represent the statistic of each cluster, which is used to compute the color spatial distribution. Second, three kinds of regional saliency measures, i.e, regional color contrast saliency, regional boundary prior saliency and regional color spatial distribution, are computed and combined. Then, a region selection strategy integrating color contrast prior, boundary prior and visual patterns information of images is presented. The pixels of an image are divided into either potential salient region or background region adaptively based on the combined regional saliency measures. Finally, a Bayesian framework is employed to compute the saliency value for each pixel taking the regional saliency values as priority. Our approach has been extensively evaluated on two popular image databases. Experimental results show that our approach can achieve considerable performance improvement in terms of commonly adopted performance measures in salient object detection.     

基于深度学习的图像显著对象检测

显著区域检测可应用在对象识别、图像分割、视 频/图像压缩中,是计算机视觉领域的重要研究主题。然而,基于不 同视觉显著特征的显著区域检测法常常不能准确地探测出显著对象且计算费时。近来,卷积 神经网络模型在图像分析和处理 领域取得了极大成功。为提高图像显著区域检测性能,本文提出了一种基于监督式生成对抗 网络的图像显著性检测方法。它 利用深度卷积神经网络构建监督式生成对抗网络,经生成器网络与鉴别器网络的不断相互对 抗训练,使卷积网络准确学习到 图像显著区域的特征,进而使生成器输出精确的显著对象分布图。同时,本文将网络自身误 差和生成器输出与真值图间的 L1距离相结合,来定义监督式生成对抗网络的损失函数,提升了显著区域检测精度。在MSRA 10K与ECSSD数据库上的实 验结果表明,本文方法 分别获得了94.19%与96.24%的准确率和93.99%与90.13%的召回率,F -Measure值也高达94.15%与94.76%,优于先 前常用的显著性检测模型。     

FD-SSD: An improved SSD object detection algorithm based on feature fusion and dilated convolution

Objects that occupy a small portion of an image or a frame contain fewer pixels and contains less information. This makes small object detection a challenging task in computer vision. In this paper, an improved Single Shot multi-box Detector based on feature fusion and dilated convolution (FD-SSD) is proposed to solve the problem that small objects are difficult to detect. The proposed network uses VGG-16 as the backbone network, which mainly includes a multi-layer feature fusion module and a multi-branch residual dilated convolution module. In the multi-layer feature fusion module, the last two layers of the feature map are up-sampled, and then they are concatenated at the channel level with the shallow feature map to enhance the semantic information of the shallow feature map. In the multi-branch residual dilated convolution module, three dilated convolutions with different dilated ratios based on the residual network are combined to obtain the multi-scale context information of the feature without losing the original resolution of the feature map. In addition, deformable convolution is added to each detection layer to better adapt to the shape of small objects. The proposed FD-SSD achieved 79.1% mAP and 29.7% mAP on PASCAL VOC2007 dataset and MS COCO dataset respectively. Experimental results show that FD-SSD can effectively improve the utilization of multi-scale information of small objects, thus significantly improve the effect of the small object detection.     

Global and local information aggregation network for edge-aware salient object detection

Aggregation of local and global contextual information by exploiting multi-level features in a fully convolutional network is a challenge for the pixel-wise salient object detection task. Most existing methods still suffer from inaccurate salient regions and blurry boundaries. In this paper, we propose a novel edge-aware global and local information aggregation network (GLNet) to fully exploit the integration of side-output local features and global contextual information and utilization of contour information of salient objects. The global guidance module (GGM) is proposed to learn discriminative multi-level information with the direct guidance of global semantic knowledge for more accurate saliency prediction. Specifically, the GGM consists of two key components, where the global feature discrimination module exploits the inter-channel relationship of global semantic features to boost representation power, and the local feature discrimination module enables different side-output local features to selectively learn informative locations by fusing with global attentive features. Besides, we propose an edge-aware aggregation module (EAM) to employ the correlation between salient edge information and salient object information for generating estimated saliency maps with explicit boundaries. We evaluate our proposed GLNet on six widely-used saliency detection benchmark datasets by comparing with 17 state-of-the-art methods. Experimental results show the effectiveness and superiority of our proposed method on all the six benchmark datasets.