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1.
未知环境下地面的不平坦性和机器人相对障碍物位姿的不确定性会造成障碍物特征提取困难,为了准确检测障碍物特征,采用3维相机获取机器人周围环境的灰度图像和距离信息。在此基础上,提出基于灰度信息和3维信息的"阈值法"进行障碍物区域的提取,并针对3维信息"阈值法"剔除的地面与障碍物过渡区域过多,以及机器人相对斜坡方位的不确定性引起的障碍物特征检测不准确,提出区域的恢复算法和表面法线坡度计算。实验结果表明,所提出的算法具有简单、有效、准确和鲁棒性强的优点。  相似文献   

2.
王程  张骏  高隽 《中国图象图形学报》2020,25(12):2630-2646
目的 光场相机一次成像可以同时获取场景中光线的空间和角度信息,为深度估计提供了条件。然而,光场图像场景中出现高光现象使得深度估计变得困难。为了提高算法处理高光问题的可靠性,本文提出了一种基于光场图像多视角上下文信息的抗高光深度估计方法。方法 本文利用光场子孔径图像的多视角特性,创建多视角输入支路,获取不同视角下图像的特征信息;利用空洞卷积增大网络感受野,获取更大范围的图像上下文信息,通过同一深度平面未发生高光的区域的深度信息,进而恢复高光区域深度信息。同时,本文设计了一种新型的多尺度特征融合方法,串联多膨胀率空洞卷积特征与多卷积核普通卷积特征,进一步提高了估计结果的精度和平滑度。结果 实验在3个数据集上与最新的4种方法进行了比较。实验结果表明,本文方法整体深度估计性能较好,在4D light field benchmark合成数据集上,相比于性能第2的模型,均方误差(mean square error,MSE)降低了20.24%,坏像素率(bad pixel,BP)降低了2.62%,峰值信噪比(peak signal-to-noise ratio,PSNR)提高了4.96%。同时,通过对CVIA (computer vision and image analysis) Konstanz specular dataset合成数据集和Lytro Illum拍摄的真实场景数据集的定性分析,验证了本文算法的有效性和可靠性。消融实验结果表明多尺度特征融合方法改善了深度估计在高光区域的效果。结论 本文提出的深度估计模型能够有效估计图像深度信息。特别地,高光区域深度信息恢复精度高、物体边缘区域平滑,能够较好地保存图像细节信息。  相似文献   

3.
睢丹  高国伟 《计算机科学》2015,42(3):316-320
由于未知像素点先验信息缺失,因此模块匹配和边缘结构信息未知,全息修复困难。传统方法采用子空间特征信息多维搜索方法未能实现对图像纹理的微细结构信息的模板匹配,效果不好。引入人工鱼群算法,提出一种基于人工鱼群微细分解和亮度补偿的先验未知像素点全息修复算法,即采用子空间特征信息多维搜索方法进行先验未知像素点置信度的更新,以保持被修复的图像破损区域的连续性。构建人工鱼群算法的图像微细分解模型,结合边缘特征点亮度补偿策略,来实现对先验未知像素点的图像信息修复改进。实验结果表明,改进的图像修复算法具有良好的视觉效果,修复时间和计算开销较少,提高了稳定性和收敛性,图像修复后的信噪比误差较小,保持在6%以内,因此该算法的性能优越。  相似文献   

4.
为充分利用遥感图像的场景信息,提高场景分类的正确率,提出一种基于空间特征重标定网络的场景分类方法。采用多尺度全向高斯导数滤波器获取遥感图像的空间特征,通过引入可分离卷积与附加动量法构建特征重标定网络,利用全连接层形成的瓶颈结构学习特征通道间的相关性,对多尺度空间特征进行权重筛选以实现特征重标定,并结合卷积神经网络训练得到最终的分类结果。实验结果表明,该方法在UCM_LandUse与机载SAR图像数据上的分类正确率分别达到94.76%和95.38%,与MNCC、MS-DCNN、PCA-CNN等算法相比,其遥感图像分类精度与泛化能力显著提升。  相似文献   

5.
针对传统图像匹配算法在几何差异场景下匹配精度低的问题,提出一种改进SIFT特征描述符和邻域投票相结合的图像匹配算法。使用8个邻域像素的平均值代替原始极值点,通过SIFT提取图像中的特征点,利用Sobel算子计算特征点的梯度幅度和方向,结合8个仿射形式的同心圆邻域生成64维描述符,根据欧氏距离确定初始匹配点,采用邻域投票的方法剔除错误的匹配点,实现图像的精确匹配。实验结果表明,该算法在显著提高匹配精度的同时缩短了匹配时间,对复杂场景的匹配性能明显提升。  相似文献   

6.
针对光照变化下图像局部或整体灰度剧烈变化而容易导致目标跟踪失败的问题,提出一种结合颜色信息和Retinex灰度增强的改进时空上下文算法.首先比较单尺度Retinex算法和多尺度Retinex算法,确定使用多尺度Retinex算法对图像进行灰度增强以减弱光照变化对图像灰度的影响;然后比较各种视觉模型的颜色特征,确定引入基于色相信息的目标模型,并将该模型与多尺度Retinex灰度增强模型相结合作为跟踪的目标模型.实验结果表明,文中算法比原算法在跟踪成功率上有较大提升,在Shaking场景下跟踪成功率为95%,比传统的时空上下文跟踪算法的跟踪成功率提高约24%;相比其他主流算法,该算法在平均跟踪成功率和跟踪精度上的表现也更高、更可靠.  相似文献   

7.
针对真实场景图像的目标分类问题,提出一种基于多尺度上下文信息的分类算法.首先运用一种软判决采样机制对图像进行局部信息采样,使场景内混合的各类信息以一种鲁棒的方式得到有效分离;然后,进一步基于软判决采样和统计特征表达机制,计算各空间尺度下的目标上下文统计特征;最后,通过逻辑回归分类算法有效地融合多尺度的上下文信息,并作出分类决策.实验表明,所提出的算法能更好地刻画真实场景下目标的特性,明显提高图像目标分类性能.  相似文献   

8.
Canny边缘特征18维描述符在图像拼接中的应用   总被引:1,自引:0,他引:1  
《计算机工程》2017,(9):310-315
为将同一场景中具有重叠区域的序列图像合成为一幅宽视角、高分辨率的图像,基于尺度不变特征变换(SIFT)算法,提出一种结合Canny特征边缘18维特征描述的图像拼接方法。采用SIFT算法提取图像特征点,利用12个圆形区域中的梯度方向累加值、3个同心圆区域中的灰度累加值及3个灰度差分值建立18维特征描述符,保留图像Canny边缘16邻域特征点,选用RANSAC算法进行特征匹配对提纯,计算出不变换矩阵H,并用渐进渐出算法完成图像融合。实验结果表明,与传统SIFT算法相比,该方法对光线变化、旋转、尺度缩放等图像均能取得较好的拼接效果。  相似文献   

9.
针对如何更有效地使用卷积神经网络从训练图像中学习到的分布式特征进行研究,提出了一种面向人脸表情识别的ROI区域二级投票机制。将图像划分成一系列感兴趣区域(ROI)图像输入到卷积神经网络中进行训练;然后将测试图像的ROI图像输入到卷积神经网络中,统计所有ROI图像的判别结果;最后采用二级投票机制确定测试图像的最终类别,得到最终判别结果。针对卷积神经网络不能从人脸图像中学习到旋转等空间位置信息,引入了STN(spatial transformer network),提高算法在解决复杂情况下的表情识别问题的能力。实验表明,ROI区域二级投票机制能够更有效地使用卷积神经网络从训练图像中学习到的分布式特征,比直接使用ROI图像进行投票的方法准确率提升了1.1%,引入STN能够有效提升卷积神经网络的鲁棒性,比未引入STN的方法准确率提升了1.5%。  相似文献   

10.
针对当前基于深度信息的虚实遮挡处理技术面临的实时性差和精度低的问题,提出一种基于局 部区域深度估计和基于 patch 相似性噪声点投票融合的实时虚实遮挡处理算法。该算法将真实场景视频序列作 为输入,首先利用局部区域深度估计算法通过稀疏重建估算出稀疏关键点的深度信息,对稀疏深度施加目标区 域的约束限制深度向周围像素的传播,从而快速恢复出目标区域的相对深度图;然后,噪声点投票融合算法利 用深度比较确定虚实物体的前后位置关系,基于 patch 相似性和投票决策的方法对区域内的像素进行投票和融 合绘制;最后输出具有真实遮挡关系的融合效果。实验结果表明,该算法不仅可以提高虚实遮挡的实时性,还 能够获得真实和虚拟场景不同空间关系下的良好融合效果。  相似文献   

11.
This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider ‘unsupervised scene segmentation’ a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images; the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.  相似文献   

12.
In this paper, we propose a novel scene categorization method based on contextual visual words. In the proposed method, we extend the traditional ‘bags of visual words’ model by introducing contextual information from the coarser scale and neighborhood regions to the local region of interest based on unsupervised learning. The introduced contextual information provides useful information or cue about the region of interest, which can reduce the ambiguity when employing visual words to represent the local regions. The improved visual words representation of the scene image is capable of enhancing the categorization performance. The proposed method is evaluated over three scene classification datasets, with 8, 13 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results show that the proposed method achieves 90.30%, 87.63% and 85.16% recognition success for Dataset 1, 2 and 3, respectively, which significantly outperforms the methods based on the visual words that only represent the local information in the statistical manner. We also compared the proposed method with three representative scene categorization methods. The result confirms the superiority of the proposed method.  相似文献   

13.
Scene image understanding has drawn much attention for its intriguing applications in the past years. In this paper, we propose a unified probabilistic graphical model called Topic-based Coherent Region Annotation (TCRA) for weakly-supervised scene region annotation. The multiscale over-segmented regions within a scene image are considered as the “words” of our topic model, which impose neighborhood contextual constraints on topic level through spatial MRF modeling, and incorporate an annotation reasoning mechanism for learning and inferring region labels automatically. Mean field variational inference is provided for model learning. The proposed TCRA has the following two main advantages for understanding natural scene images. First, spatial information of multiscale over-segmented regions is explicitly modeled to obtain coherent region annotations. Second, only image-level labels are needed for automatically inferring the label of every region within the scene. This is particularly helpful in reducing human burden on manually labeling pixel-level semantics in the scene understanding research. Thus, given a scene image that has no textual prior, the regions in it can be automatically labeled using the learned TCRA model. The experimental results conducted on three benchmarks consisting of the MSRCORID image dataset, the UIUC Events image dataset and the SIFT FLOW dataset show that the proposed model outperforms the recent state-of-the-art methods.  相似文献   

14.
图像场景分析是目前计算机视觉领域的研究热点,体现了场景与目标之间的包含关系。在分析过程中合理的使用基于上下文关系的知识可以提高场景分析模型的适用性和目标识别的准确率。从"图像集——场景——目标——部分——视觉词汇"这种层次的角度进行场景分析,将全局上下文信息和局部上下文信息同时融入到基于HDP的生成图模型中,在场景层和目标层这两个不同的层次上,共同作用于场景分析。场景分析的结果可以用来约束目标识别,目标识别的结果可以反馈作用于场景分析。  相似文献   

15.
Annotating image regions with keywords has received increasing attention in the computer vision community in recent years. Recent studies have shown that graphical modeling techniques, such as Conditional Random Fields (CRF), greatly improves the accuracy of image annotation by utilizing contextual information among image regions. However, training and predicting with the high-order CRF is computational expensive so that only adjacent regions can be utilized to build its graph structure. In this paper, we develop a light-weight classification model, Approximated Supporting Region Graph (ASRG), in order to handle more relevant regions efficiently, with which a large number of supporting regions are selected and their features are utilized to represent the contextual information in the training and prediction for each image region. Experimental results show that our model is much more computational efficient and achieves competitive performance comparing with CRF and other state-of-art methods.  相似文献   

16.
In the field of visual recognition such as scene categorization, representing an image based on the local feature (e.g., the bag-of-visual-word (BOVW) model and the bag-of-contextual-visual-word (BOCVW) model) has become popular and one of the most successful methods. In this paper, we propose a method that uses localized maximum-margin learning to fuse different types of features during the BOCVW modeling for eventual scene classification. The proposed method fuses multiple features at the stage when the best contextual visual word is selected to represent a local region (hard assignment) or the probabilities of the candidate contextual visual words used to represent the unknown region are estimated (soft assignment). The merits of the proposed method are that (1) errors caused by the ambiguity of single feature when assigning local regions to the contextual visual words can be corrected or the probabilities of the candidate contextual visual words used to represent the region can be estimated more accurately; and that (2) it offers a more flexible way in fusing these features through determining the similarity-metric locally by localized maximum-margin learning. The proposed method has been evaluated experimentally and the results indicate its effectiveness.  相似文献   

17.
There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. A context model can rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit of context models has been limited because most of the previous methods were tested on data sets with only a few object categories, in which most images contain one or two object categories. In this paper, we introduce a new data set with images that contain many instances of different object categories, and propose an efficient model that captures the contextual information among more than a hundred object categories using a tree structure. Our model incorporates global image features, dependencies between object categories, and outputs of local detectors into one probabilistic framework. We demonstrate that our context model improves object recognition performance and provides a coherent interpretation of a scene, which enables a reliable image querying system by multiple object categories. In addition, our model can be applied to scene understanding tasks that local detectors alone cannot solve, such as detecting objects out of context or querying for the most typical and the least typical scenes in a data set.  相似文献   

18.
针对场景标注中如何产生良好的内部视觉信息表达和有效利用上下文语义信息两个至关重要的问题,提出一种基于深度学习的多尺度深度网络监督模型。与传统多尺度方法不同,模型主要由两个深度卷积网络组成:首先网络考虑全局信息,提取大尺度图像低层特征;其次网络利用图像局部信息,结合低层特征获取一组稠密的、完备的图像特征,有效地捕获图像像素的纹理特征、颜色特征和上下文信息。对比许多经典方法,该算法不依赖图像分割技术和人工制作特征,在Stanford Background Dataset取得了很好的效果。  相似文献   

19.
Semantic image segmentation aims to partition an image into non-overlapping regions and assign a pre-defined object class label to each region. In this paper, a semantic method combining low-level features and high-level contextual cues is proposed to segment natural scene images. The proposed method first takes the gist representation of an image as its global feature. The image is then over-segmented into many super-pixels and histogram representations of these super-pixels are used as local features. In addition, co-occurrence and spatial layout relations among object classes are exploited as contextual cues. Finally the features and cues are integrated into the inference framework based on conditional random field by defining specific potential terms and introducing weighting functions. The proposed method has been compared with state-of-the-art methods on the MSRC database, and the experimental results show its effectiveness.  相似文献   

20.
史静  朱虹  王栋  杜森 《中国图象图形学报》2017,22(12):1750-1757
目的 目前对于场景分类问题,由于其内部结构的多样性和复杂性,以及光照和拍摄角度的影响,现有算法大多通过单纯提取特征进行建模,并没有考虑场景图像中事物之间的相互关联,因此,仍然不能达到一个理想的分类效果。本文针对场景分类中存在的重点和难点问题,充分考虑人眼的视觉感知特性,利用显著性检测,并结合传统的视觉词袋模型,提出了一种融合视觉感知特性的场景分类算法。方法 首先,对图像进行多尺度分解,并提取各尺度下的图像特征,接着,检测各尺度下图像的视觉显著区域,最后,将显著区域信息与多尺度特征进行有机融合,构成多尺度融合窗选加权SIFT特征(WSSIFT),对场景进行分类。结果 为了验证本文算法的有效性,该算法在3个标准数据集SE、LS以及IS上进行测试,并与不同方法进行比较,分类准确率提高了约3%~17%。结论 本文提出的融合视觉感知特性的场景分类算法,有效地改善了单纯特征描述的局限性,并提高了图像的整体表达。实验结果表明,该算法对于多个数据集都具有较好的分类效果,适用于场景分析、理解、分类等机器视觉领域。  相似文献   

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