CCFNet: Cross-Complementary fusion network for RGB-D scene parsing of clothing images

Schemes to complement context relationships by cross-scale feature fusion have appeared in many RGB-D scene parsing algorithms; however, most of these works conduct multi-scale information interaction after multi-modal feature fusion, which ignores the information loss of the two modes in the original coding. Therefore, a cross-complementary fusion network (CCFNet) is designed in this paper to calibrate the multi-modal information before feature fusion, so as to improve the feature quality of each mode and the information complementarity ability of RGB and the depth map. First, we divided the features into low, middle, and high levels, among which the low-level features contain the global details of the image and the main learning features include texture, edge, and other features. The middle layer features contain not only some global detail features but also some local semantic features. Additionally, the high-level features contain rich local semantic features. Then, the feature information lost in the coding process of low and middle level features is supplemented and extracted through the designed cross feature enhancement module, and the high-level features are extracted through the feature enhancement module. In addition, the cross-modal fusion module is designed to integrate multi-modal features of different levels. The experimental results verify that the proposed CCFNet achieves excellent performance on the RGB-D scene parsing dataset containing clothing images, and the generalization ability of the model is verified by the dataset NYU Depth V2.     

双向特征融合的快速精确任意形状文本检测

现有的基于分割的场景文本检测方法仍较难区分相邻文本区域,同时网络得到分割图后后处理阶段步骤复杂导致模型检测效率较低。为了解决此问题,该文提出一种新颖的基于全卷积网络的场景文本检测模型。首先,该文构造特征提取器对输入图像提取多尺度特征图。其次,使用双向特征融合模块融合两个平行分支特征的语义信息并促进两个分支共同优化。之后,该文通过并行地预测缩小的文本区域图和完整的文本区域图来有效地区分相邻文本。其中前者可以保证不同的文本实例之间具有区分性,而后者能有效地指导网络优化。最后,为了提升文本检测的速度,该文提出一个快速且有效的后处理算法来生成文本边界框。实验结果表明：在相关数据集上,该文所提出的方法均实现了最好的效果,且比目前最好的方法在F-measure指标上最多提升了1.0%,并且可以实现将近实时的速度,充分证明了该方法的有效性和高效性。     

双向特征融合的快速精确任意形状文本检测

现有的基于分割的场景文本检测方法仍较难区分相邻文本区域,同时网络得到分割图后后处理阶段步骤复杂导致模型检测效率较低.为了解决此问题,该文提出一种新颖的基于全卷积网络的场景文本检测模型.首先,该文构造特征提取器对输入图像提取多尺度特征图.其次,使用双向特征融合模块融合两个平行分支特征的语义信息并促进两个分支共同优化.之后,该文通过并行地预测缩小的文本区域图和完整的文本区域图来有效地区分相邻文本.其中前者可以保证不同的文本实例之间具有区分性,而后者能有效地指导网络优化.最后,为了提升文本检测的速度,该文提出一个快速且有效的后处理算法来生成文本边界框.实验结果表明:在相关数据集上,该文所提出的方法均实现了最好的效果,且比目前最好的方法在F-measure指标上最多提升了1.0％,并且可以实现将近实时的速度,充分证明了该方法的有效性和高效性.     

Combining scene model and fusion for night video enhancement

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene information and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.     

模糊逻辑及其在数据融合中的应用

自从１９６５年Ｚａｄｅｈ发表关于模糊集理论的文章以来,模糊集理论已在工业控制、医疗诊断、经济决策、模式识别等领域得到广泛应用,随着模糊逻辑和可能性理论的提出和深入研究,它们在不确定推理模型的设计和多传感器信息融合中显示出越来越强大的优势,文中探讨它们在多传感器数据融合中的应用。     

多尺度语义信息融合的目标检测

针对当前目标检测算法对小目标及密集目标检测效果差的问题,该文在融合多种特征和增强浅层特征表征能力的基础上提出了浅层特征增强网络(SEFN),首先将特征提取网络VGG16中Conv4_3层和Conv5_3层提取的特征进行融合形成基础融合特征;然后将基础融合特征输入到小型的多尺度语义信息融合模块中,得到具有丰富上下文信息和...     

红外目标的边界检测和标注

边界检测和标注是红外目标探测和识别的一个重要环节。自然背景复杂的纹理特征和红外图像中的噪声影响,以及红外目标模糊的边缘,给红外目标的边界检测和分割带来一定的困难。分水岭算法作为一种数学形态学的图像分割方法,为进行目标特征分析和识别提供了可能,然而,分水岭算法产生的过度分割,又为背景和噪声抑制增加了难度。文中在分水岭算法的基础上,结合模糊逻辑方法,对自然背景下的红外人工目标的边界检测和分割以及红外背景的抑制进行了探讨。     

Underwater image processing method for fish localization and detection in submarine environment

Object detection is an important process in image processing, it aims to detect instances of semantic objects of a certain class in digital images and videos. Object detection has applications in many areas of computer vision such as underwater fish detection. In this paper we present a method for preprocessing and fish localization in underwater images. We are based on a Poisson–Gauss theory, because it can accurately describe the noise present in a large variety of imaging systems. In the preprocessing step we denoise and restore the raw images. These images are split into regions utilizing the mean shift algorithm. For each region, statistical estimation is done independently in order to combine regions into objects. The method is tested under different underwater conditions. Experimental results show that the proposed approach outperforms state of the art methods.     

Rapid detection of camera tampering and abnormal disturbance for video surveillance system

Camera tampering may indicate that a criminal act is occurring. Common examples of camera tampering are turning the camera lens to point to a different direction (i.e., camera motion) and covering the lens by opaque objects or with paint (i.e., camera occlusion). Moreover, various abnormalities such as screen shaking, fogging, defocus, color cast, and screen flickering can strongly deteriorate the performance of a video surveillance system. This study proposes an automated method for rapidly detecting camera tampering and various abnormalities for a video surveillance system. The proposed method is based on the analyses of brightness, edge details, histogram distribution, and high-frequency information, making it computationally efficient. The proposed system runs at a frame rate of 20–30 frames/s, meeting the requirement of real-time operation. Experimental results show the superiority of the proposed method with an average of 4.4% of missed events compared to existing works.     

基于模糊逻辑与数据融合的智能导盲手杖

在分析国内外现有导盲设备的基础上,引入模糊逻辑与数据融合算法,对传统超声波探测方式进行改进。自行设计并组建超声波障碍物探测网络,进行数据采集、融合与分析,实现了对障碍物方位与距离的综合探测。此外,利用GPS模组、网络通信技术以及语音合成芯片设计,并实现了智能导盲手杖的GPS位置信息实时获取、GPRS短信提示、蓝牙语音提示等功能,以满足盲人日常生活的需求。     

Application of fuzzy logic in weighted information fusion of hand geometry and palm prints

Based on the theory of fuzzy logic, the method of obfuscating coefficient and reliability to fuse the information of hand geometry and palm prints for identity discrimination is proposed. The experiment proves that the method is useful and effective. Its identification rate is up to 90%, which is 20%-30% higher than that of using hand geometry or palm prints singly, thus it can be widely used in highly demanded security field, such as finance, entrance guard, etc.     

增进数据准确度的散射辐射融合测量方法

分析了非线性响应特性和发射功率波动对散射测量的联合效应,以及单独的散射测量在这种联合效应作用下产生误差的原因.推导建立了这种联合效应的定标方法的数学模型.利用散射辐射组合系统,构建了一种散射辐射融合测量方法用于实现这一定标方法.数值模拟了该融合测量方法相对于单独的散射测量方法在数据准确度、测量动态范围等性能上的改进效果.     

多传感器信息融合NFE模型的研究及应用

该文针对复杂环境下的多传感器信息融合问题,提出了一种将神经网络(NN)、模糊推理(FR)与专家系统(ES)有机地结合在一起的智能化的信息融合NFE模型。从工程角度出发,分析提出3维完备的智能化信息融合模型的必要,综合考虑影响传感器置信度的各种因素(传感器性能、天气状况、电子干扰、杂波强度等),并进行权重分析,设计NFE置信度估计器,对传感器的输入信息进行置信度估计。仿真结果表明,该模型在置信度偏低、传感器发生故障等环境下,能有效地实现目标识别,并且识别结果更可靠。     

融合色彩和轮廓特征的火焰检测

基于图像的火焰检测能适应大空间和恶劣环境火灾预防应用的需要,首先分析了火焰图像的色彩及轮廓信息等重要特征,然后提出了基于连通区域面积阈值化的单像素宽度目标轮廓特征的抽取方法,再应用所建立的火焰检测色彩模型,融合火焰色彩和边缘轮廓动态变化等特性,实现了在视频序列图像中火焰的自动检测.实验结果显示,该方法能实时检测火焰,误判率低.     

信号层融合在慢动小目标检测中的应用

针对海上慢动小目标检测背景，提出了一种基于信号层融合的多雷达融合检测方法，并对实际应用中需要解决的问题进行了分析讨论。该方法利用目标和海杂波在不同频段下的统计性能差异，对多个雷达的观测信息在检测前进行融合，然后在相关域进行积累检测，与传统的检测方法相比，能够显著改善信号的信杂比，提高目标检测概率。     

融合整体和局部信息的三维人脸识别

针对三维人脸识别中单一特征信息不足,采用一种基于整体信息和局部信息相融合的识别算法,以提高识别率。首先将预处理的三维点云用多层次B样条曲面拟合,获取精确的人脸曲面拟合函数,将控制点映射为深度图像,并根据人脸曲面函数和生理特征提取过鼻尖的中分轮廓线和水平轮廓线;其次对深度图像采用二维主元分析(2D-PCA)算法提取整体信息,对轮廓线采用改进的ICP算法匹配,作为局部信息;最后用加权求和法在决策级进行信息融合。采用CASIA3D人脸库完成识别测试,实验结果表明,本文算法明显优于单一特征信息下识别算法,且对姿态有较好的鲁棒性,同时不增加算法复杂度。     

基于自编码器与多模态数据融合的视频推荐方法

现今常用的线性结构视频推荐方法存在推荐结果非个性化、精度低等问题,故开发高精度的个性化视频推荐方法迫在眉睫。提出了一种基于自编码器与多模态数据融合的视频推荐方法,对文本和视觉两种数据模态进行视频推荐。具体来说,所提方法首先使用词袋和TF-IDF方法描述文本数据,然后将所得特征与从视觉数据中提取的深层卷积描述符进行融合,使每个视频文档都获得一个多模态描述符,并利用自编码器构造低维稀疏表示。本文使用3个真实数据集对所提模型进行了实验,结果表明,与单模态推荐方法相比,所提方法推荐性能明显提升,且所提视频推荐方法的性能优于基准方法。     

传感器网络数据融合隐私保护及安全框架

在传感器网络中,隐私保护和入侵检测是一对矛盾关系,调和两者的矛盾非常重要。在传感器网络中传感数据融合是一个基本操作,研究隐私保护和入侵检测在传感数据融合中的关系并提出一个框架,可以探测错误数据融合,但不需要知道实际的传感数据内容,因而得以保证传感数据的隐蔽性。实验结果显示,实际的原始数据和聚合传感数据可以得到很好隐蔽的同时能够检测到大部分错误数据融合。     

多进制小波与自适应PCNN的可见光/毫米波图像融合

以安检中隐匿物品检测为应用背景,提出一种基于多进制小波与自适应脉冲耦合神经网络的可见光/毫米波图像融合算法.首先可见光图与毫米波图经多进制小波分解处理,得到低频分量与高频分量.其次,低频系数采取改进区域方差融合处理,高频系数则是将子带改进拉普拉斯能量和作为PCNN中对应神经元的链接强度β,将子带八方向边缘区域能量作为PCNN的外部激励输入,经点火处理得到点火映射图,通过判决算子选取融合图像的高频系数,然后重构得到融合图.实验仿真结果分析表明,提出的融合方法在主观与客观评价上均优于现有文献中的一些典型融合方法,能获得更好的融合效果.