Depth estimation of supervised monocular images based on semantic segmentation

In recent years, the research method of depth estimation of target images using Convolutional Neural Networks (CNN) has been widely recognized in the fields of artificial intelligence, scene understanding and three-dimensional (3D) reconstruction. The fusion of semantic segmentation information and depth estimation will further improve the quality of acquired depth images. However, how to deeply combine image semantic information with image depth information and use image edge information more accurately to improve the accuracy of depth image is still an urgent problem to be solved. For this purpose, we propose a novel depth estimation model based on semantic segmentation to estimate the depth of monocular images in this paper. Firstly, a shared parameter model of semantic segmentation information and depth estimation information is built, and the semantic segmentation information is used to guide depth acquisition in an auxiliary way. Then, through the multi-scale feature fusion module, the feature information contained in the neural network on different layers is fused, and the local feature information and global feature information are effectively used to generate high-resolution feature maps, so as to achieve the goal of improving the quality of depth image by optimizing the semantic segmentation model. The experimental results show that the model can fully extract and combine the image feature information, which improves the quality of monocular depth vision estimation. Compared with other advanced models, our model has certain advantages.     

图像语义提取方法研究

为解决从图像的低层视觉特征到高层语义特征的＂语义鸿沟＂问题,对当前的语义提取方法进行研究,简单介绍了图像语义层次模型,并根据语义信息的来源不同,归纳总结了图像语义中基于处理范围的方法,基于机器学习的方法,基于人机交互的方法和基于外部信息源的提取方法,这些工作为图像语义提取和图像语义检索等研究提供有益参考。     

Unsupervised image-to-image translation by semantics consistency and self-attention

Unsupervised image-to-image translation is a challenging task for computer vision. The goal of image translation is to learn a mapping between two domains, without corresponding image pairs. Many previous works only focused on image-level translation but ignored image features processing, which led to a certain semantics loss, such as the changes of the background of the generated image, partial transformation, and so on. In this work, we propose a method of image-to-image translation based on g...     

基于内容的多层次语义视频对象提取方法研究

视频对象的提取在序列图像的分析中起着重要作用.提出一个基于内容的多层次视频的对象提取算法,利用高斯马尔可夫模型对其进行颜色和纹理的混合特征图像分割.利用Normalize-cut准则,对其运动信息进行分析,然后进行区域聚合,即得到具有语义的视频对象.对于背景运动信息较丰富的序列图像可以取得良好的提取效果.     

Research on image feature extraction and retrieval algorithms based on convolutional neural network

With the rapid development of mobile Internet and digital technology, people are more and more keen to share pictures on social networks, and online pictures have exploded. How to retrieve similar images from large-scale images has always been a hot issue in the field of image retrieval, and the selection of image features largely affects the performance of image retrieval. The Convolutional Neural Networks (CNN), which contains more hidden layers, has more complex network structure and stronger ability of feature learning and expression compared with traditional feature extraction methods. By analyzing the disadvantage that global CNN features cannot effectively describe local details when they act on image retrieval tasks, a strategy of aggregating low-level CNN feature maps to generate local features is proposed. The high-level features of CNN model pay more attention to semantic information, but the low-level features pay more attention to local details. Using the increasingly abstract characteristics of CNN model from low to high. This paper presents a probabilistic semantic retrieval algorithm, proposes a probabilistic semantic hash retrieval method based on CNN, and designs a new end-to-end supervised learning framework, which can simultaneously learn semantic features and hash features to achieve fast image retrieval. Using convolution network, the error rate is reduced to 14.41% in this test set. In three open image libraries, namely Oxford, Holidays and ImageNet, the performance of traditional SIFT-based retrieval algorithms and other CNN-based image retrieval algorithms in tasks are compared and analyzed. The experimental results show that the proposed algorithm is superior to other contrast algorithms in terms of comprehensive retrieval effect and retrieval time.     

Improved Denoising Autoencoder for Maritime Image Denoising and Semantic Segmentation of USV

Unmanned surface vehicle(USV)is currently a hot research topic in maritime communication network(MCN),where denoising and semantic segmentation of maritime images taken by USV have been rarely studied.The former has recently researched on autoencoder model used for image denoising,but the existed models are too complicated to be suitable for real-time detection of USV.In this paper,we proposed a lightweight autoencoder combined with inception module for maritime image denoising in different noisy environments and explore the effect of different inception modules on the denoising performance.Furthermore,we completed the semantic segmentation task for maritime images taken by USV utilizing the pretrained U-Net model with tuning,and compared them with original U-Net model based on different backbone.Subsequently,we compared the semantic segmentation of noised and denoised maritime images respectively to explore the effect of image noise on semantic segmentation performance.Case studies are provided to prove the feasibility of our proposed denoising and segmentation method.Finally,a simple integrated communication system combining image denoising and segmentation for USV is shown.     

改进生成对抗网络实现红外与可见光图像融合

红外与可见光图像融合技术能够同时提供红外图像的热辐射信息和可见光图像的纹理细节信息,在智能监控、目标探测和跟踪等领域具有广泛的应用。两种图像基于不同的成像原理,如何融合各自图像的优点并保证图像不失真是融合技术的关键,传统融合算法只是叠加图像信息而忽略了图像的语义信息。针对该问题,提出了一种改进的生成对抗网络,生成器设计了局部细节特征和全局语义特征两路分支捕获源图像的细节和语义信息;在判别器中引入谱归一化模块,解决传统生成对抗网络不易训练的问题,加速网络收敛;引入了感知损失,保持融合图像与源图像的结构相似性,进一步提升了融合精度。实验结果表明,提出的方法在主观评价与客观指标上均优于其他代表性方法,对比基于全变分模型方法,平均梯度和空间频率分别提升了55.84%和49.95%。     

Automatic image annotation based on Gaussian mixture model considering cross-modal correlations

Automatic image annotation has been an active topic of research in the field of computer vision and pattern recognition for decades. In this paper, we present a new method for automatic image annotation based on Gaussian mixture model (GMM) considering cross-modal correlations. To be specific, we first employ GMM fitted by the rival penalized expectation-maximization (RPEM) algorithm to estimate the posterior probabilities of each annotation keyword. Next, a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity by seamlessly integrating the information from both image low level visual features and high level semantic concepts together, which can effectively avoid the phenomenon that different images with the same candidate annotations would obtain the same refinement results. Followed by the rank-two relaxation heuristics over the built label similarity graph is applied to further mine the correlation of the candidate annotations so as to capture the refining annotation results, which plays a crucial role in the semantic based image retrieval. The main contributions of this work can be summarized as follows: (1) Exploiting GMM that is trained by the RPEM algorithm to capture the initial semantic annotations of images. (2) The label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels. (3) Refining the candidate set of annotations generated by the GMM through solving the max-bisection based on the rank-two relaxation algorithm over the weighted label graph. Compared to the current competitive model SGMM-RW, we can achieve significant improvements of 4% and 5% in precision, 6% and 9% in recall on the Corel5k and Mirflickr25k, respectively.     

基于多尺度上下文语义信息的图像场景分类算法

传统视觉词典模型没有考虑图像的多尺度和上下文语义共生关系.本文提出一种基于多尺度上下文语义信息的图像场景分类算法.首先,对图像进行多尺度分解,从多个尺度提取不同粒度的视觉信息;其次利用基于密度的自适应选择算法确定最优概率潜在语义分析模型主题数;然后,结合Markov随机场共同挖掘图像块的上下文语义共生信息,得到图像的多尺度直方图表示;最后结合支持向量机实现场景分类.实验结果表明,本文算法能有效利用图像的多尺度和上下文语义信息,提高视觉单词的语义准确性,从而改善场景分类性能.     

基于GAN的ISAR图像语义分割方法

逆合成孔径雷达（ISAR）成像技术能够对空间目标进行远距离成像,刻画目标的外形、结构和尺寸等信息。ISAR图像语义分割能够获取目标的感兴趣区域,是ISAR图像解译的重要技术支撑,具有非常重要的研究价值。由于ISAR图像表征性较差,图像中散射点的不连续和强散射点存在的旁瓣效应使得人工精准标注十分困难,基于交叉熵损失的传统深度学习语义分割方法在语义标注不精准情况下无法保证分割性能的稳健。针对这一问题,提出了一种基于生成对抗网络（GAN）的ISAR图像语义分割方法,采用对抗学习思想学习ISAR图像分布到其语义分割图像分布的映射关系,同时通过构建分割图像的局部信息和全局信息来保证语义分割的精度。基于仿真卫星目标ISAR图像数据集的实验结果证明,本文方法能够取得较好的语义分割结果,且在语义标注不够精准的情况下模型更稳健。     

基于深度卷积网络的高分遥感图像语义分割

随着我国高分对地观测系统的不断发展,对高分影像智能化分析与处理的应用需求愈来愈多,基于深度学习语义分割的影像分类也受到高度关注。作为近景图像语义分割的热点模型,Deeplab网络在应用时取得了良好的效果。为了解决多尺度高分辨率遥感影像语义分割问题,本文首先利用空洞卷积扩大Atrous空间金字塔池化（ASPP）结构的感受野,然后对DeepLabv3进行改进并将其用于高分2号遥感影像的分类处理。我们以郴州地区的高分遥感影像为研究对方法进行了验证,首先对原始影像进行预处理,再对预处理图像进行数据增强与扩充,最后通过对不同参数条件下的分类结果进行对比,分析该模型的适应性和精确性。在我们的数据集中,本文方法的实验分类像素精度为88.2％,MIoU达到72.5％,得到了比Deeplab更好的分类效果。      

A memory learning framework for effective image retrieval.

Most current content-based image retrieval systems are still incapable of providing users with their desired results. The major difficulty lies in the gap between low-level image features and high-level image semantics. To address the problem, this study reports a framework for effective image retrieval by employing a novel idea of memory learning. It forms a knowledge memory model to store the semantic information by simply accumulating user-provided interactions. A learning strategy is then applied to predict the semantic relationships among images according to the memorized knowledge. Image queries are finally performed based on a seamless combination of low-level features and learned semantics. One important advantage of our framework is its ability to efficiently annotate images and also propagate the keyword annotation from the labeled images to unlabeled images. The presented algorithm has been integrated into a practical image retrieval system. Experiments on a collection of 10,000 general-purpose images demonstrate the effectiveness of the proposed framework.     

Structural image retrieval using automatic image annotation and region based inverted file

Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.     

基于DeepLabv3+网络的电流互感器红外图像分割方法

红外图像智能分析是变电设备故障诊断的一种有效方法,目标设备分割是其关键技术。本文针对复杂背景下电流互感器整体分割难的问题,采用基于ResNet50的DeepLabv3+神经网络,用电流互感器的红外图像训练语义分割模型的方法,对收集到的样本采用限制对比度自适应直方图均衡化方法实现图像轮廓增强,构建样本数据集,并运用图像变换扩充样本数据集,搭建语义分割网络训练语义分割模型,实现电流互感器像素与背景像素的二分类。通过文中方法对420张电流互感器红外图像测试,结果表明,该方法的平均交并比（Mean Intersection over Union, MIoU）为87.5%,能够从测试图像中精确分割出电流互感器设备,为后续电流互感器的故障智能诊断做铺垫。     

Creative and high-quality image composition based on a new criterion

Image compositing techniques are primarily utilized to achieve realistic composite results. Some existing image compositing methods, such as gradient domain and alpha matting, are widely used in the field of computer vision, and can typically achieve realistic results, especially for seamless boundaries. However, when the candidate composite images and the target images have obvious differences, such as color, texture and brightness, the composite results are unrealistic and inconsistent. At the same time, traditional compositing methods focus on basic feature matching, ignoring semantic rationality in composition processing. Quite a few compositing methods thus generate composite results without semantic rationality.In this paper, a new multi-scale image composition method has been presented. In the composition process, wavelet pyramid and basic feature handling were used to achieve multi-scale compositions. More importantly, a new criterion was established, based on the semantic rationality of images, which could ensure that the composite images are semantically valid. A large database was created to facilitate experimentation. The experiments showed that the methodology introduced in this paper produced superior results compared to traditional composition methods; the composite results were not only consistent and seamless, but were also semantically valid.     

语义驱动的颜色恒常决策算法

不同颜色恒常性算法适用于不同场景下的图像,算法融合是扩展颜色恒常性算法适用范围常用的方法之一,而现有融合性算法在算法选择依据上忽略了语义信息在图像纹理特征描述中的作用,导致光源估计时的精度不高。针对该问题,提出一种语义驱动的颜色恒常决策算法。首先,利用PSPNet(Pyramid Scene Parsing Network)模型对经过一阶灰度边缘算法(1st Gray Edge)偏色预处理后的目标图像进行场景语义分割,并计算场景中各个语义类别的占比;其次,根据语义类别及占比在已训练的决策集合中寻找相似的参考图像,并使用欧氏距离计算两者的语义相似度;最后,将语义相似度与基于多维欧氏空间确定的阈值进行判别,根据判别结果选择合适算法为目标图像实行偏色校正。在Color Checker和NUS-8 camera两种数据集中的实验结果表明,所提算法光源估计角度误差较单一算法均大幅度下降,且较同类型融合性算法分别下降14.02%和8.17%,提高了光源估计的鲁棒性和准确度。     

Effective image retrieval based on hidden concept discovery in image database.

This paper addresses content-based image retrieval in general, and in particular, focuses on developing a hidden semantic concept discovery methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into regions associated with homogenous color, texture, and shape features. By exploiting regional statistical information in each image and employing a vector quantization method, a uniform and sparse region-based representation is achieved. With this representation, a probabilistic model based on statistical-hidden-class assumptions of the image database is obtained, to which the expectation-maximization technique is applied to analyze semantic concepts hidden in the database. An elaborated retrieval algorithm is designed to support the probabilistic model. The semantic similarity is measured through integrating the posterior probabilities of the transformed query image, as well as a constructed negative example, to the discovered semantic concepts. The proposed approach has a solid statistical foundation; the experimental evaluations on a database of 10000 general-purposed images demonstrate its promise and effectiveness.     

基于多特征扩展pLSA模型的场景图像分类

场景图像分类近年来受到人们的广泛关注,而基于统计模型的方法更是场景分类中的研究热点。我们提出了一种新的基于多特征融合和扩展pLSA模型的场景图像分类框架。对每幅图像首先用多尺度规则分割确定局部基元,然后提取每个局部基元的多分辨率直方图矩特征和SIFT特征,最后用扩展的概率生成模型对图像集进行建模,测试。我们的方法不仅能够很好的表示图像的语义特性而且在模型的训练阶段是无监督的。我们针对目前常用的3个数据库,做了三组对比实验,均取得了比以前的方法更好的识别结果。      

一种改进的噪声图像语义分割方法

在图像的获取和传输过程中,可能会出现噪声, 它不仅破坏了图像的真实信息,而且严重影响了图像的视觉效果。因此, 噪声图像的语义分割成为图像分析中最具挑战性的问题之一。为了提高噪声图像的分割性能 ,本文在分析全卷积网络(FCN)的 基础上,提出一种改进的FCN模型(IFCN)对噪声图像语义分割。该算法采用一种新的中值 池化方法代替卷积神经网络的最大值 池化,可以在去除噪声的同时保留更多边缘信息。在训练整个深度网络时,通过反向传播算 法以一种直接的端到端,像素到像素 的方式映射。实验结果表明,提出的模型在PASCAL VOC2012数据集上对噪声图像语义分割 可以获得比较好的分割效果,准确率mean IU达到86.5%。