基于视觉显著性的立体图像质量评价方法

人眼的视觉显著性能够影响图像质量评价,结合平面显著性和中央偏移因子,提出基于视觉显著性的立体图像质量评价算法(VS-SSIM);同时考虑中央偏移因子的影响,提出基于中央偏移的结构相似度算法(CB-SSIM)。实验结果表明,中央偏移因子和视觉显著性均能提高立体图像质量评价算法;相比CB-SSIM,VS-SSIM算法与主观评价结果的相关性更高。     

Non-local spatial redundancy reduction for bottom-up saliency estimation

In this paper we present a redundancy reduction based approach for computational bottom-up visual saliency estimation. In contrast to conventional methods, our approach determines the saliency by filtering out redundant contents instead of measuring their significance. To analyze the redundancy of self-repeating spatial structures, we propose a non-local self-similarity based procedure. The result redundancy coefficient is used to compensate the Shannon entropy, which is based on statistics of pixel intensities, to generate the bottom-up saliency map of the visual input. Experimental results on three publicly available databases demonstrate that the proposed model is highly consistent with the subjective visual attention.     

Combining multi-layer integration algorithm with background prior and label propagation for saliency detection

In this paper, we propose a novel approach to automatically detect salient regions in an image. Firstly, some corner superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the background labels based on ranking algorithm. Subsequently, we further employ an objectness measure to pick out and propagate foreground labels. Furthermore, an integration algorithm is devised to fuse both background-based saliency map and foreground-based saliency map, meanwhile an original energy function is acted as refinement before integration. Finally, results from multiscale saliency maps are integrated to further improve the detection performance. Our experimental results on five benchmark datasets demonstrate the effectiveness of the proposed method. Our method produces more accurate saliency maps with better precision-recall curve, higher F-measure and lower mean absolute error than other 13 state-of-the-arts approaches on ASD, SED, ECSSD, iCoSeg and PASCAL-S datasets.     

基于最简视觉显著性的红外目标快速提取方法

在红外深空目标跟踪系统中,为了能够从深空红外图像中快速提取微小目标,通过分析红外深空图像的特点,提出一种基于最简视觉显著性的红外目标快速提取方法。该方法在传统的视觉显著性的基础上,通过计算局部灰度最大值和目标像素的灰度平均值与邻域像素的灰度加权值的对比度组成特征向量,构造显著性模型,抑制背景并凸显目标,使之不但能够减少运算耗时,而且能够保证提取精度。通过对红外深空图像进行处理,实验结果表明该算法的运算时间仅为传统的视觉显著性算法的28%,且有较好的处理结果,证明了该算法的有效性。     

IR saliency detection via a GCF-SB visual attention framework

Infrared (IR) saliency detection with high detection accuracy is a challenging task due to the complex background and low contrast of IR images. In this paper, an IR saliency detection method via a new visual attention framework is proposed, which comprises two phases. In the first phase, a Gray & Contrast Features (GCF) model is established, in which the IR image is processed in two feature channels, a gray feature channel and a contrast feature channel. And then a primary feature map can be obtained by fusing the gray and contrast features from these two channels, which is the basis of the second phase. In the second phase, a Similarity-based Bayes (SB) model is established, in which two prior probabilities and two likelihood functions are calculated according to the previously obtained primary feature map. Finally, the saliency map is calculated with the obtained prior probabilities and likelihood functions by Bayes formula. Experimental results indicate that the proposed method can effectively reduce noise and enhance contrast of IR images with complex background and low contrast, and obtain a higher detection accuracy and robustness than seven state-of-the-art methods.     

基于视觉显著性的飞机蒙皮缺陷检测方法

飞机蒙皮缺陷是导致航空安全事故的重要因素之一.针对当前蒙皮缺陷依赖人工目视检测,存在效率低、实时性差、人工经验敏感等问题,提出一种基于局部对比度的显著性模型缺陷检测方法.算法采用 自动γ校正算法对输入缺陷图像进行修正,增强缺陷与背景的动态范围;在基于局部对比度的显著性检测模型中融入HSI颜色空间中的I分量以提升缺陷的显...     

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.     

Improved slope estimation for SAR Doppler ambiguity resolution

The idea of using the Radon transform to measure the alignment of linear features in synthetic aperture radar (SAR) data has breathed new life into the "look displacement" class of Doppler ambiguity resolution algorithms. In these algorithms, the slope of target energy is estimated to obtain the satellite beam pointing angle accurately enough to resolve the Doppler ambiguity. After explaining the method and adding some minor improvements, it is shown how it can work well on satellite SAR data. Then, an alternate method is developed that combines the ideas of the Radon and look displacement algorithms to obtain a computationally simpler and more accurate algorithm. In addition, the quality checks of the "spatial diversity" approach are used to increase the robustness of the algorithm. Even though the algorithm was conceived for high-contrast scenes, it works remarkably well in low to medium contrast scenes as well.     

基于视觉显著性的监控视频动态目标跟踪

视频动态目标检测与跟踪是智能化视频分析的基础,是实现智能监控的关键技术之一。基于人类视觉对运动的方向和速度非精确感知的特点,结合HR生物相关运动检测模型改进Itti Saliency算法,建立颜色、方向、亮度和运动四特征通道的特征图提取算法,对特征图进行跨尺度融合及归一化,从而提取视频图像中动态目标的视觉显著图。对视频序列图像的显著图逐一显示,便可实现对运动目标的跟踪。提出的运动感知模型,改善了对运动目标视觉显著性的检测效果,能够准确检测并跟踪监控视频中复杂背景、遮挡、多物体的动态目标。     

A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

基于视觉显著性和目标置信度的红外车辆检测技术

提出了一种复杂地面背景下的红外车辆检测算法。首先,提出一种新的自适应分段线性灰度拉伸方法来增强当图像整体亮度偏低时的目标信息。其次,利用拉伸后图像的显著性图生成目标潜在的兴趣区。再次,利用平均梯度法在兴趣区内进行目标的边缘再分割,完成目标精确分割检测。最后,利用车辆的红外融合特征计算目标置信度,对目标进行评估和确认。实验结果表明:对实际拍摄的红外图像进行检测的算法可有效地检测出地面车辆目标。     

Deep learning-based channel estimation for wireless ultraviolet MIMO communication systems

To solve the problems of pulse broadening and channel fading caused by atmospheric scattering and turbulence, multiple-input multiple-output(MIMO) technology is a valid way. A wireless ultraviolet(UV) MIMO channel estimation approach based on deep learning is provided in this paper. The deep learning is used to convert the channel estimation into the image processing. By combining convolutional neural network(CNN) and attention mechanism(AM), the learning model is designed to extract the depth f...     

基于视觉显著模型的遥感图像舰船快速检测

为了降低传统高分辨率海面遥感图像舰船目标检测方法的计算复杂度,提高检测速度,在舰船目标检测中引入了基于直方图对比度的视觉显著模型和空间降维算法,提出一种新的高分辨率海面遥感图像舰船目标快速检测算法。首先对高分辨率遥感图像进行空间降维,然后计算降维图的视觉显著图,突出感兴趣目标区域,最后利用最大类间方差法分割视觉显著图以获取舰船目标候选区域。结果表明,目标检测所消耗的时间减小为原来的10%~12%,弱化了复杂海面纹理背景对目标检测的影响。该研究提高了高分辨率遥感图像舰船目标的检测效率。     

A method for solving DOA estimation ambiguity in esprit algorithm

This paper introduces a method for solving DOA estimation ambiguity in ESPRIT algorithm with the conventional beamformer. With the help of it, for any space of two subarrays, the signal DOA in [-π/2 ,π/2] can be estimated effectively by using ESPRIT algorithm. Finally, some simulation results to verify the theoretical analyses are presented.     

A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

With the emerging development of three-dimensional (3D) related technologies, 3D visual saliency modeling is becoming particularly important and challenging. This paper presents a new depth perception and visual comfort guided saliency computational model for stereoscopic 3D images. The prominent advantage of the proposed model is that we incorporate the influence of depth perception and visual comfort on 3D visual saliency computation. The proposed saliency model is composed of three components: 2D image saliency, depth saliency and visual comfort based saliency. In the model, color saliency, texture saliency and spatial compactness are computed respectively and fused to derive 2D image saliency. Global disparity contrast is considered to compute depth saliency. Particularly, we train a visual comfort prediction function to distinguish stereoscopic image pair as high comfortable stereo viewing (HCSV) or low comfortable stereo viewing (LCSV), and devise different computational rules to generate a visual comfort based saliency map. The final 3D saliency map is obtained by using a linear combination and enhanced by a “saliency-center bias” model. Experimental results show that the proposed 3D saliency model outperforms the state-of-the-art models on predicting human eye fixations and visual comfort assessment.     

基于视觉显著和CNN的花卉图像检索软件

花卉图像检索是图像检索领域的热门研究方向,高效、快速地检索数据库中的花卉图像一直是该方向的重点课题。为了检索花卉图像,文中设计了一个基于视觉显著模型和CNN的图像哈希算法,并根据此算法设计和开发出一个高效、快速的花卉图像检索软件。软件具有查询花卉类别、检索相似花卉、浏览花卉信息等功能。     

An enhanced image quality assessment by synergizing superpixels and visual saliency

Superpixel and saliency-based evaluation methods play important roles in full reference image quality assessment (FR IQA). However, we find that these methods have one complementary principle and three limitations: (1) the weighted maps of superpixel-based methods conflict with the perception of the human visual system; (2) saliency-based methods are inefficient in terms of the block distortion; (3) the general two-direction gradient extraction factor must be extended to be multidirectional. To address these limitations, we propose an enhanced image quality assessment by synergizing superpixels and visual saliency. Specifically, the calculation of a newly proposed framework involves three similarities and two strategies: the saliency, superpixel and multidirectional gradient similarities of the neighborhoods, and the saliency pooling strategy, the fusion strategy of these similarities. Theoretical analysis and experimental results show that the proposed method can effectively address the limitations noted above and outperform the existing methods.     

Background estimation and motion saliency detection using total variation-based video decomposition

As human vision system is highly sensitive to motion present in a scene, motion saliency forms an important feature in a video sequence. Motion information is used for video compression, object segmentation, object tracking and in many other applications. Though its applications are extensive, accurate detection of motion in a given video is complex and computationally expensive for the solutions reported in the literature. Decomposing a video into visually similar and residual videos is a robust way to detect motion salient regions. The existing decomposition techniques require large execution time as the standard form of the problem is NP-hard. We propose a novel algorithm which detects the motion salient regions by decomposing the input video into background and residual videos in much lesser time without sacrificing the accuracy of the decomposition. In addition, the proposed algorithm is completely parallelizable that ensures further reduction in computational time with the use of advanced multicore processors.