A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Unconstrained face images are interfered by many factors such as illumination,posture,expression,occlusion,age,accessories and so on,resulting in the randomness of the noise pollution implied in the original samples.In order to improve the sample quality,a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary.First,the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary.Then,a block cooperation framework is presented to perform sparse coding for different local structures of human face,and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients.Finally,by synthesising the sparse representation results of all visual salient block dictionaries,the global coding residual is obtained and the class label is given.The experimental results on four databases,that is,AR,extended Yale B,LFW and PubFig,indicate that the combination of visual saliency dictionary,block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

Hierarchical Visual Attention Model for Saliency Detection Inspired by Avian Visual Pathways

Visual attention is a mechanism that enables the visual system to detect potentially important objects in complex environment. Most computational visual attention models are designed with inspirations from mammalian visual systems.However, electrophysiological and behavioral evidences indicate that avian species are animals with high visual capability that can process complex information accurately in real time. Therefore,the visual system of the avian species, especially the nuclei related to the visual attention mechanism, are investigated in this paper. Afterwards, a hierarchical visual attention model is proposed for saliency detection. The optic tectum neuron responses are computed and the self-information is used to compute primary saliency maps in the first hierarchy. The "winner-takeall" network in the tecto-isthmal projection is simulated and final saliency maps are estimated with the regularized random walks ranking in the second hierarchy. Comparison results verify that the proposed model, which can define the focus of attention accurately, outperforms several state-of-the-art models.This study provides insights into the relationship between the visual attention mechanism and the avian visual pathways. The computational visual attention model may reveal the underlying neural mechanism of the nuclei for biological visual attention.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Rate Control Algorithm of Wireless Video Based on Visual Saliency Map Model

In order to further improve the efficiency of video compression, we introduce a perceptual characteristics of Human Visual System (HVS) to video coding, and propose a novel video coding rate control algorithm based on human visual saliency model in H.264/AVC. Firstly, we modifie Itti's saliency model. Secondly, target bits of each frame are allocated through the correlation of saliency region between the current and previous frame, and the complexity of each MB is modified through the saliency value and its Mean Absolute Difference (MAD) value. Lastly, the algorithm was implemented in JVT JM12.2. Simulation results show that, comparing with traditional rate control algorithm, the proposed one can reduce the coding bit rate and improve the reconstructed video subjective quality, especially for visual saliency region. It is very suitable for wireless video transmission.

Visual Attention Modeling in Compressed Domain:From Image Saliency Detection to Video Saliency Detection

Saliency detection models, which are used to extract salient regions in visual scenes, are widely used in various multimedia processing applications. It has attracted much attention in the area of computer vision over the past decades. Since most images or videos over the Internet are stored in compressed domains such as images in JPEG format and videos in MPEG2 format, H.264 format, and MPEG4 Visual format, many saliency detection models have been proposed in the compressed domain recently. We provide a review of our works on saliency detection models in the compressed domain in this paper.Besides, we introduce some commonly used fusion strategies to combine spatial saliency map and temporal saliency map to compute the final video saliency map.

Saliency detection and edge feature matching approach for crater extraction

Craters are salient terrain features on planetary surfaces, and provide useful information about the relative dating of geological unit of planets. In addition, they are ideal landmarks for spacecraft navigation. Due to low contrast and uneven illumination, automatic extraction of craters remains a challenging task. This paper presents a saliency detection method for crater edges and a feature matching algorithm based on edges informa- tion. The craters are extracted through saliency edges detection, edge extraction and selection, feature matching of the same crater edges and robust ellipse fitting. In the edges matching algorithm, a crater feature model is proposed by analyzing the relationship between highlight region edges and shadow region ones. Then, crater edges are paired through the effective matching algorithm. Experiments of real planetary images show that the proposed approach is robust to different lights and topographies, and the detection rate is larger than 90%.

Visual tracking based on transfer learning of deep salience information

In this paper,we propose a new visual tracking method in light of salience information and deep learning.Salience detection is used to exploit features with salient information of the image.Complicated representations of image features can be gained by the function of every layer in convolution neural network(CNN).The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network.This motivates us to improve the representation ability of CNN with functions of salience detection.We adopt the fully-convolution networks(FCNs)to perform salience detection.We take parts of the network structure to perform salience extraction,which promotes the classification ability of the model.The network we propose shows great performance in tracking with the salient information.Compared with other excellent algorithms,our algorithm can track the target better in the open tracking datasets.We realize the 0.5592 accuracy on visual object tracking 2015(VOT15)dataset.For unmanned aerial vehicle 123(UAV123)dataset,the precision and success rate of our tracker is 0.710 and 0.429.

Place recognition based on saliency for topological localization

Based on salient visual regions for mobile robot navigation in unknown environments, a new place recognition system was presented. The system uses monocular camera to acquire omni-directional images of the environment where the robot locates. Salient local regions are detected from these images using center-surround difference method, which computes opponencies of color and texture among multi-scale image spaces. And then they are organized using hidden Markov model (HMM) to form the vertex of topological map. So localization, that is place recognition in our system, can be converted to evaluation of HMM. Experimental results show that the saliency detection is immune to the changes of scale, 2D rotation and viewpoint etc. The created topological map has smaller size and a higher ratio of recognition is obtained.

Automatic salient object segmentation using saliency map and color segmentation

A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.

P^2: a robust and rotationally invariant shape descriptor with applications to mesh saliency

This work presents a robust and rotationally invariant shape descriptor, namely perception pronouncement （called p2）, to mathematically model the eye fixations, p2 takes two criteria - the local consideration of surface curvature and the global consideration of view- independent visibility - into account. Differing from existing works that often computed the intrinsic surface property of visibility in imaging space, a novel approach is proposed to approxi- mate the attribute in object space using Gauss map and Ray tracing. With the presented shape descriptor, mesh saliency detection, which refers to reasoning about which regions or points of a surface axe important, is more sensible, especially when 3D models fall into two categories： （1） the models possess significant interior/exterior structures; （2） the models contain regions where the contrast in visibility is high. For the models that are out of the categories, saliencies achieved by our approach are comparable to or even better than those of state-of-the-axt methods.

基于双通路视觉系统的自适应轮廓检测模型

在轮廓检测领域,背景纹理的干扰容易造成轮廓提取不完整。针对这一问题,本文提出了一种基于双通路视觉系统的自适应轮廓检测模型。首先从皮层下通路的信息采集与评估过程出发,对图像整体的显著性进行评估,以此获得轮廓信息的可能性分布;然后采用自适应尺度的高斯导函数对经典视觉通路中感受野的动态特性进行模拟,加强了模型对轮廓细节的捕获;最后在外周抑制算法的基础上,结合像素的空间分布对所有边缘的稀疏性进行度量,更加准确地区分了轮廓和纹理边缘。实验结果表明,本文模型可以有效抑制背景纹理,提升轮廓连续性,具有较好的轮廓检测性能。

面向目标检测的视觉注意机制波段选择研究

近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光谱图像的目标检测研究无疑会产生相当的促进作用。研究提出引入视觉注意机制理论应用于波段选择研究,构建面向目标检测应用的视觉注意机制波段选择模型。通过分析计算波段图幅的目标与背景的可识别程度,量化所在波段对地物目标与背景的判别能力,提出了基于目标视觉可识别度的波段选择方法;利用LC显著性算法进行空间域的视觉显著性目标分析,计算背景与目标的显著性差异绝对值,提出基于LC显著目标结构分布的波段选择方法。将这两种方法结合提出的改进子空间划分方法,建立面向目标检测的视觉注意机制波段选择模型,并经高光谱遥感AVIRIS San Diego公开数据集进行目标检测实验验证,结果表明所提出的基于视觉注意机制的波段选择模型对于目标检测应用具有较好的检测效果,实现了数据降维和高效的计算处理。

基于眼动追踪的历史街区意象要素视觉显著性分析——以广州永庆坊为例

历史街区城市意象要素是城市空间感知的重要内容。在原有的认知地图问卷调查基础上,以永庆坊为例,以照片和视频为媒介,利用眼动追踪技术定量分析其意象要素视觉显著性的构成特征。首先总结永庆坊认知地图之中意象要素的视觉显著物。其次利用眼动仪展开实验,收集大样本被试的眼动数据,并进行数学统计分析,以静态照片探究单一场景意象要素视觉显著性的构成机制,以动态视频探究不同场景意象要素之间的视觉显著性差异。最终获得历史街区意象要素视觉显著性的构成规律,并为其实践应用提出相关建议。

视觉驱动梯度域滤波重构的自适应渲染算法

蒙特卡罗路径追踪渲染算法结果图像往往受噪声影响,现有去噪算法效率较低且容易丢失图像细节.因此,本文提出一种视觉驱动梯度域滤波重构的自适应渲染算法.首先在预渲染阶段获取特征图像同时通过引导滤波器对特征图像进行预滤波;然后通过图像视觉显著性划分区域进行滤波重构,在显著区域利用融合图像梯度信息的双边滤波器进行平滑去噪,非显著区域利用均值滤波器进行快速去噪;最后利用SURE(Stein′s Unbiased Risk Estimator)计算像素颜色估计量的均方误差引导自适应采样.实验结果表明,与同类算法相比,本文算法可以在更短时间内渲染出具有更优质视觉效果的图像,本文算法的结构相似性(SSIM)和峰值信噪比(PSNR)均有显著提高,运行时间平均降低8.6%以上.

一种基于视觉显著性的码率控制算法

针对当前高效视频编码的码率控制算法未能充分确保视频显著区域的图像质量问题,提出了一种基于视觉显著性的码率控制算法。首先,采用基于图的流形排序显著检测算法获取视频的显著区域;其次,在图像编码过程中,为每个编码单元(Coding Unit,CU)建立基于视觉显著性的加权失真公式,将显著性值作为权重;最后,通过求解得到拉格朗日乘子λ,并根据原始码率控制算法中的λ与量化参数(Quantization Parameter,QP)的关系为每个CU确定QP。试验结果表明,该算法有效地根据视频内容的显著性权重实现了码率的合理分配,并在保持码率控制准确性的同时,提升了视频显著区域的图像质量。

基于谱残差法的主动声呐疑似目标自动拾取

针对主动声呐干扰遍布的B显画面上目标亮点难以拾取的问题,本文提出一种基于谱残差法视觉显著性处理的疑似目标自动拾取方法。在主动声呐图像的概念之下,研讨了自然图像中视觉显著体的发现与主动声呐图像中疑似目标拾取的相似机理,依此进而说明了本文方法的机理、核心框架以及关键处理环节,包括预处理声呐图像、生成显著图、估算拾取阈值以及标定疑似目标等。海试数据处理结果表明:相较于基于局部相对能量分析的对照方法,本文方法对已知目标具有更低的漏报率,同时亮点拾取数量减少约40%,且处理耗时减少约37%。

基于视觉显著性的主动声呐背景均衡方法

针对主动声呐检测输出中的混响与辐射噪声泄漏等干扰抑制问题,提出了一种基于视觉显著性检测的背景均衡方法。首先,建立起主动声呐图像的概念,从视觉显著性统计规律上研讨其与自然图像的相似性;然后,在图像处理框架下提出一种基于二维频谱残差显著性检测的主动声呐背景均衡新方法,并引入图像熵作为均衡效果的评价依据。经海试数据验证,该方法能有效抑制混响片状干扰与辐射噪声条带状干扰,增强回波亮点,相较于常规的加权二维均值背景均衡方法图像熵值降低约2/3,运算速度提高约一个数量级。

单帧红外弱小目标检测技术研究现状与展望

红外探测技术具有探测距离远、抗干扰能力强、隐蔽性强和全天候等优势在天基预警、末敏弹制导等领域得到了广泛应用。通过红外成像技术能够得到目标图像从而对目标进行预警、识别和跟踪。在实际场景中,目标图像往往所占像素比例小,信号强度低,容易湮没在背景图像中;背景图像变化剧烈,存在较强的结构信息、边缘和噪声,红外图像信噪比低,目标检测难度较大,一直是目标检测领域的研究难点和热门话题。为提高红外图像弱小目标检测能力,大量的弱小目标检测算法被提出。现有的主流的检测手段根据图像数据检测方式分为单帧检测和多帧检测两大类,多帧算法依赖大量的图像数据支撑,响应周期长,应用潜力低,而单帧检测算法凭借复杂度低、时效性强、便于硬件实现等特点,被广泛应用在高速运动目标检测、预警等领域。因此本文以单帧红外弱小目标检测算法为主体,从技术方向入手,着重阐述了基于滤波、基于对比度与显著性分析、基于数据优化和深度学习四类单帧弱小目标检测算法的原理与近年来的典型应用,通过仿真试验和算法复杂度对比了不同算法的性能、优势和不足,总结了弱小目标检测算法的研究现状并对本领域的发展趋势进行了展望。本文的工作能帮助读者快速了解本领域的研究现状,为研究人员提供参考。

融合光流特征和显著性检测的目标跟踪算法

传统的核相关滤波(Kernel Correlation Filter,KCF)算法使用HOG特征来获取目标信息,对非刚体目标不鲁棒,容易出现目标跟踪漂移现象。提出一种融合光流特征和显著性检测的目标跟踪算法抑制跟踪的漂移。算法通过在多通道特征表达时融入光流特征,增加运动目标的位置、姿态的变化信息。同时,通过显著性检测位置对漂移目标进行重检测调节,抑制跟踪漂移,提高跟踪的准确性。实验结果表明,该算法在复杂场景中仍可以进行鲁棒的视觉目标跟踪。

视觉显著性和稀疏学习相融合的船舶图像目标检测

为抑制船舶图像目标检测受光照变化、海浪干扰、背景杂波等因素的影响,设计视觉显著性和稀疏表示学习相融合的船舶图像目标检测方法,提升船舶图像目标检测效果。利用船舶图像建立船舶图像字典;通过稀疏表示算法结合字典,稀疏编码船舶图像;依据稀疏编码结果,在船舶图像内提取视觉显著图;通过自适应阈值法,分割视觉显著图,得到船舶目标候选区域,缩小船舶目标检测范围;在概率神经网络内,输入船舶目标候选区域,判断其是否为船舶目标,完成船舶图像目标检测。实验证明,该方法可有效稀疏编码船舶图像,并提取视觉显著图;该方法可有效分割视觉显著图;在简单背景与复杂背景下,该方法均可精准检测船舶目标。