利用立体视觉的室内场景分类

场景分类的目标是为各种视觉处理任务建立语义上下文,尤其是为目标识别。双目视觉系统现已广泛配备在智能机器人上,然而场景分类的任务大多只是使用单目图像。由于室内场景的复杂性,使用单目图像进行场景分类的性能很低。提出了一种基于双目视觉的室内场景分类方法,使用在一些特定区域里拟合出的若干平面的参数作为场景的特征。采用层级的分类方法,依据视差图,场景被分为开放场所类和封闭场所类,利用提出的场景特征和Gist特征对上述两类进行细分。为了验证提出的方法,建立了一个包含四种场景类别的图像数据集。实验结果表明提出的方法取得了较好的分类性能。     

An integrated approach for scene understanding based on Markov Random Field model

In this paper, we propose a Markov Random Field model-based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. In our proposed scheme we formulate the image segmentation and interpretation problem as an integrated scheme and solve it through a general optimization algorithm. More specifically, the image is first segmented into a set of disjoint regions by a conventional region-based segmentation technique which operates on image pixels, and a Region Adjacency Graph (RAG) is then constructed from the resulting segmented regions based on the spatial adjacencies between regions. Our scheme then proceeds on the RAG by defining the region merging and labeling problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into the energy function to be minimized by a general optimization technique. In the proposed scheme, the image segmentation and interpretation processes cooperate in the simultaneous optimization process such that the erroneous segmentation and misinterpretation due to incomplete knowledge about each problem domain can be compensately recovered by continuous estimation of the single unified energy function. We exploit the proposed scheme to segment and interpret natural outdoor scene images.     

Texture segmentation using fractal dimension

This paper deals with the problem of recognizing and segmenting textures in images. For this purpose the authors employ a technique based on the fractal dimension (FD) and the multi-fractal concept. Six FD features are based on the original image, the above average/high gray level image, the below average/low gray level image, the horizontally smoothed image, the vertically smoothed image, and the multi-fractal dimension of order two. A modified box-counting approach is proposed to estimate the FD, in combination with feature smoothing in order to reduce spurious regions. To segment a scene into the desired number of classes, an unsupervised K-means like clustering approach is used. Mosaics of various natural textures from the Brodatz album as well as microphotographs of thin sections of natural rocks are considered, and the segmentation results to show the efficiency of the technique. Supervised techniques such as minimum-distance and k-nearest neighbor classification are also considered. The results are compared with other techniques     

外极面图像的运动遮挡模型和运动纹理方向检测算法

运动遮挡边界处的运动估计是一种困难的问题,外极面图像方法将运动估计转化为转迹线的检测,人造物体的轨迹线容易通过边缘跟踪的方法获得,但对于纹理复杂的自然景物,轨迹跟踪较为困难。     

User assisted separation of reflections from a single image using a sparsity prior

When we take a picture through transparent glass the image we obtain is often a linear superposition of two images: the image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there are an infinite number of valid decompositions. In this paper we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images we use a sparsity prior over derivative filters. This sparsity prior is optimized using the terative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.     

自然场景图像与合成图像的快速分类

目的 随着现代通信和传感技术的快速发展,互联网上多媒体数据日益增长,既为人们生活提供了便利,又给信息有效利用提出了挑战。为充分挖掘网络图像中蕴含的丰富信息,同时考虑到网络中图像类型的多样性,以及不同类型的图像需要不同的处理方法,本文针对当今互联网中两种主要的图像类型：自然场景图像与合成图像,设计层次化的快速分类算法。方法 该算法包括两层,第1层利用两类图像在颜色,饱和度以及边缘对比度上表现出来的差异性提取全局特征,并结合支持向量机（SVM）进行初步分类,第1层分类结果中低置信度的图像会被送到第2层中。在第2层中,系统基于词袋模型（bag-of-words）对图像不同类型的局部区域的纹理信息进行编码得到局部特征并结合第2个SVM分类器完成最终分类。针对层次化分类框架,文中还提出两种策略对两个分类器进行融合,分别为分类器结果融合与全局+局部特征融合。为测试算法的实用性,同时收集并发布了一个包含超过30 000幅图像的数据库。结果 本文设计的全局与局部特征对两类图像具有较强的判别性。在单核Intel Xeon（R）（2.50 GHz）CPU上,分类精度可达到98.26%,分类速度超过40帧/s。另外通过与基于卷积神经网络的方法进行对比实验可发现,本文提出的算法在性能上与浅层网络相当,但消耗更少的计算资源。结论 本文基于自然场景图像与合成图像在颜色、饱和度、边缘对比度以及局部纹理上的差异,设计并提取快速有效的全局与局部特征,并结合层次化的分类框架,完成对两类图像的快速分类任务,该算法兼顾分类精度与分类速度,可应用于对实时性要求较高的图像检索与数据信息挖掘等实际项目中。     

基于迭代神经网络的图像结构表示和分类

由于缺少结构化的表示,基于内容的图像分类存在一定的问题,据此提出了一种基于迭 代神经网络的自然图像表示和分类的方法。利用Berkeley分割算法将图像分割成不同的区域,采用 基于人工的多叉树或基于邻接区域的二叉树的方法进行区域合并,同时提取区域统计特征,得到图像 的树型结构表示。根据BPTS算法对网络进行训练,训练好的网络就具备了图像分类的功能。实验 结果表明,基于迭代神经网络的结构表示和分类方法具有很强的结构学习能力,同时人工生成的多叉 树涵盖更多的语义信息且能得到较好的分类结果。     

Reconstruction of high contrast images for dynamic scenes

High Dynamic Range (HDR) imaging requires one to composite multiple, differently exposed images of a scene in the irradiance domain and perform tone mapping of the generated HDR image for displaying on Low Dynamic Range (LDR) devices. In the case of dynamic scenes, standard techniques may introduce artifacts called ghosts if the scene changes are not accounted for. In this paper, we consider the blind HDR problem for dynamic scenes. We develop a novel bottom-up segmentation algorithm through superpixel grouping which enables us to detect scene changes. We then employ a piecewise patch-based compositing methodology in the gradient domain to directly generate the ghost-free LDR image of the dynamic scene. Being a blind method, the primary advantage of our approach is that we do not assume any knowledge of camera response function and exposure settings while preserving the contrast even in the non-stationary regions of the scene. We compare the results of our approach for both static and dynamic scenes with that of the state-of-the-art techniques.     

Beyond pixels: Exploiting camera metadata for photo classification

Semantic scene classification based only on low-level vision cues has had limited success on unconstrained image sets. On the other hand, camera metadata related to capture conditions provide cues independent of the captured scene content that can be used to improve classification performance. We consider three problems, indoor-outdoor classification, sunset detection, and manmade-natural classification. Analysis of camera metadata statistics for images of each class revealed that metadata fields, such as exposure time, flash fired, and subject distance, are most discriminative for each problem. A Bayesian network is employed to fuse content-based and metadata cues in the probability domain and degrades gracefully even when specific metadata inputs are missing (a practical concern). Finally, we provide extensive experimental results on the three problems using content-based and metadata cues to demonstrate the efficacy of the proposed integrated scene classification scheme.     

GIS支持下遥感图象中采矿塌陷地提取方法研究

采矿塌陷地动态监测是工矿区资源管理与环境保护的重要方面 ,遥感技术可在其中发挥重要作用 ,从遥感图象中提取采矿塌陷地是遥感应用于矿山资源环境监测的重要研究课题 .传统的提取方法主要基于光谱特征 ,精度与效率都难以满足应用要求 ,为了以较高的精度 ,从遥感图象中提取塌陷地 ,必须建立新的方法与模型 .将遥感技术与 GIS相结合进行专题信息提取是有效的途径之一 .本文根据研究区的特点 ,以具体应用为指导 ,遥感技术与 GIS相结合 ,提出了 GIS支持下的分层分类、基于 GIS变化区域识别的分类、基于 GIS和领域知识对遥感分类图象进行后处理、GIS支持下采矿塌陷地的直接提取等方法与模型 ,充分应用光谱特征、地学特征与信息、领域和专家知识及其他统计数据辅助进行遥感图象处理与专题信息提取 .这些方法在精度、效率等方面均较传统方法有较大提高 ,最大提取精度可达到 89% ,能够有效地对工矿区土地塌陷态势进行动态监测     

深度学习在航拍场景分类中的应用

最近几十年来,航拍图片和视频在城市规划、沿海地区监视、军事任务等方面都得到了广泛的运用。因而了解航拍图片中所包含的内容,研究航拍视频所拍摄的场景类型就显得异常重要。目前流行的场景分类算法大多是针对自然场景的,很少有针对高分辨率航拍场景分类的算法。针对高分辨率航拍图片的场景分类给出了一种分层式算法。该算法首先用尺度不变特征转换（scale-invariant feature transform,SIFT）算法提取鲁棒的块局部特征,然后在视觉词袋的基础上,用经局限型波兹曼模型（restricted Boltzmarm machine,RBM）初始化的深层信念网络（deep belief network,DBN）来表示低层特征与高层视频特征之间的关系;同时深层信念网络也起到了分类器的作用。实验结果表明,该算法在处理高分辨率航拍图片场景分类问题时都要略好于目前主流算法。     

基于形式概念分析的图像场景语义标注模型

为生成有效表示图像场景语义的视觉词典,提高场景语义标注性能,提出一种基于形式概念分析(FCA)的图像场景语义标注模型。该方法首先将训练图像集与其初始的视觉词典抽象为形式背景,采用信息熵标识了各视觉单词的权重,并分别构造了各场景类别概念格结构;然后再利用各视觉单词权重的均值刻画概念格内涵上各组合视觉单词标注图像的贡献,按照类别视觉词典生成阈值,从格结构上有效提取了标注各类场景图像语义的视觉词典;最后,利用K最近邻标注测试图像的场景语义。在Fei-Fei Scene 13类自然场景图像数据集上进行实验,并与Fei-Fei方法和Bai方法相比,结果表明该方法在β=0.05和γ=15时,标注分类精度更优。     

A vision system for robotic inspection and manipulation

A model-based approach has been proposed to make object recognition computationally tractable. In this approach, models associated with objects expected to appear in the scene are recorded in the system's knowledge base. The system extracts various features from the input images using robust, low-level, general-purpose operators. Finally, matching is performed between the image-derived features and the scene domain models to recognize objects. Factors affecting the successful design and implementation of model-based vision systems include the ability to derive suitable object models, the nature of image features extracted by the operators, a computationally effective matching approach, knowledge representation schemes, and effective control mechanisms for guiding the systems's overall operation. The vision system they describe uses gray-scale images, which can successfully handle complex scenes with multiple object types     

基于语义感知的图像美学质量评估方法

当前图像美学质量评估的研究主要基于图像的视觉内容来给出评价结果,忽视了美感是人的认知活动的事实,在评价时没有考虑用户对图像语义信息的理解。为了解决这一问题,提出了一种基于语义感知的图像美学质量评估方法,将图像的物体类别信息以及场景类别信息也用于图像美学质量评估。运用迁移学习的思想,构建了一种可以融合图像多种特征的混合网络。对于每一幅输入图像,该网络可以分别提取出其物体类别特征、场景类别特征以及美学特征,并将这三种特征进行高质量的融合,以达到更好的图像美学质量评估效果。该方法在AVA数据集上的分类准确率达到89.5%,相对于传统方法平均提高了19.9%,在CUHKPQ数据集上的泛化性能也有了很大提升。实验结果表明,所提方法在图像美学质量评估问题上,能够取得更好的分类性能。     

A knowledge-based approach for retrieving images by content

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed     

SVM用于基于内容的自然图像分类和检索

在传统的基于内容图像检索的方法中，由于图像的领域较宽，图像的低级视觉特征和高级概念之间存在着较大的语义间隔，导致检索效果不佳．该文认为更有现实意义的做法是，缩窄图像的领域以减小低级特征和高级概念间的语义间隔，并利用机器学习方法自动建立图像类的模型，从而提供用户概念化的图像查询方式．该文以自然图像领域为例，使用支持向量机(SVM)学习自然图像的类别，学习到的模型用于自然图像分类和检索．实验结果表明作者的方法是可行的．     

Metric learning for weather image classification

Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.     

图像场景分类中视觉词包模型方法综述

目的关于图像场景分类中视觉词包模型方法的综述性文章在国内外杂志上还少有报导,为了使国内外同行对图像场景分类中的视觉词包模型方法有一个较为全面的了解,对这些研究工作进行了系统总结。方法在参考国内外大量文献的基础上,对现有图像场景分类(主要指针对单一图像场景的分类)中出现的各种视觉词包模型方法从低层特征的选择与局部图像块特征的生成、视觉词典的构建、视觉词包特征的直方图表示、视觉单词优化等多方面加以总结和比较。结果回顾了视觉词包模型的发展历程,对目前存在的多种视觉词包模型进行了归纳,比较常见方法各自的优缺点,总结了视觉词包模型性能评价方法,并对目前常用的标准场景库进行汇总,同时给出了各自所达到的最高精度。结论图像场景分类中视觉词包模型方法的研究作为计算机视觉领域方兴未艾的热点研究领域,在国内外研究中取得了不少进展,在计算机视觉领域的研究也不再局限于直接应用模型描述图像内容,而是更多地考虑图像与文本的差异。虽然视觉词包模型在图像场景分类的应用中还存在很多亟需解决的问题,但是这丝毫不能掩盖其研究的重要意义。     

结合感受野增强和全卷积网络的场景文字检测方法

自然场景图像质量易受光照及采集设备的影响,且其背景复杂,图像中文字颜色、尺度、排列方向多变,因此,自然场景文字检测具有很大的挑战性.本文提出一种基于全卷积网络的端对端文字检测器,集中精力在网络结构和损失函数的设计,通过设计感受野模块并引入Focalloss,GIoUloss进行像素点分类和文字包围框回归,从而获得更加稳...