A structural difference based image clutter metric with brain cognitive model constraints

Previous clutter metrics have less than the desired accuracy in predicting targeting performance, in this paper, a structural difference based image clutter metric is proposed based on the given definition of image clutter metric. According to the sensitivity of human visual perception to image structural information, a structural similarity measure between the target and clutter images is firstly established. Previous clutter metrics not considering brain cognitive characteristics, we define an information content weight measure by introducing the widely accepted brain cognitive information extracting model in the field of image quality assessment (IQA), and then, pool the structural similarity measure to be a clutter metric, which can be entitled BSD metric. Comparative field tests show that BSD metric makes a more significant improvement than previously proposed metrics in predicting target acquisition performance including detection probability and search time.     

Structural similarity determines search time and detection probability

The recently introduced TSSIM clutter metric is currently the best predictor of human visual search performance for natural images (Chang and Zhang [1]). The TSSIM quantifies the similarity of a target to its background in terms luminance, contrast and structure. It correlates stronger with experimental mean search times and detection probabilities than other clutter metrics (Chang and Zhang [1], [2]). Here we show that it is predominantly the structural similarity component of the TSSIM which determines human visual search performance, whereas the luminance and contrast components of the TSSIM show no relation with human performance. This result agrees with previous reports that human observers mainly rely on structural features to recognize image content. Since the structural similarity component of the TSSIM is equivalent to a matched filter, it appears that matched filtering predicts human visual performance when searching for a known target.     

相位一致性图像及其在目标跟踪中的应用(英文)

针对传统实时相关跟踪方法对照度变化敏感的问题,提出了一种基于相位一致性图像的相关跟踪方法.利用相位一致性函数值在[0,1]区间内且无量纲、对图像的亮度和对比度具有不变性等特点,首先对原始图像进行相位一致性检测,得到相位一致性图像,再利用MAD(Minimum Absolute Difference)等相关跟踪算法在相位一致性图像中对目标进行跟踪运算.对可见光和红外图像的实验表明,在图像的亮度和对比度发生剧烈变化的情况下,算法仍能保持对目标的稳定跟踪.该方法可用于解决传统实时相关跟踪方法普遍存在的因照度变化导致跟踪点漂移甚至跟踪失败的问题.     

Moving target detection approach based on spatio-temporal salient perception

The differences in texture and motion between man-made object and natural scene are the key features for human biological visual system to detect moving object in scenery. The paper proposed a moving target detection approach based on spatio-temporal perception, which is a crucial function of the visual attention mechanism. The spatial feature including edge, orientation, texture and contrast of the image are extracted, and then the corresponding spatial salient map are constructed by fusing the features through difference of Gaussian (DOG) function, which can suppress the common and enhance the difference of local region. Then, the global motion, local motion and relative motion between continuous images are extracted by means of pyramid multi-resolution, and the moving salient map is constructed after the motion difference between moving target and background is confirmed. Finally, the spatio-temporal salient map is constructed by fusing the spatial salient map and the moving salient map through competition strategy, and the moving target could be detected by searching the maximum in the spatio-temporal salient map. Some experiments are included and the results show that the method can accurately detect the moving target in complex background.     

No-Reference Quality Assessment for 3D Synthesized Images Based on Visual-Entropy-Guided Multi-Layer Features Analysis

Multiview video plus depth is one of the mainstream representations of 3D scenes in emerging free viewpoint video, which generates virtual 3D synthesized images through a depth-image-based-rendering (DIBR) technique. However, the inaccuracy of depth maps and imperfect DIBR techniques result in different geometric distortions that seriously deteriorate the users’ visual perception. An effective 3D synthesized image quality assessment (IQA) metric can simulate human visual perception and determine the application feasibility of the synthesized content. In this paper, a no-reference IQA metric based on visual-entropy-guided multi-layer features analysis for 3D synthesized images is proposed. According to the energy entropy, the geometric distortions are divided into two visual attention layers, namely, bottom-up layer and top-down layer. The feature of salient distortion is measured by regional proportion plus transition threshold on a bottom-up layer. In parallel, the key distribution regions of insignificant geometric distortion are extracted by a relative total variation model, and the features of these distortions are measured by the interaction of decentralized attention and concentrated attention on top-down layers. By integrating the features of both bottom-up and top-down layers, a more visually perceptive quality evaluation model is built. Experimental results show that the proposed method is superior to the state-of-the-art in assessing the quality of 3D synthesized images.     

基于相位一致性和Hough变换的多源图像配准方法

由于红外图像与可见光图像对比度不同,常用基于梯度幅值的特征匹配方法难以正确配准。在分析红外图像与可见光图像成像机制的基础上,提出了一种结合相位一致性边缘检测与Hough变换的多源图像配准新方法。该算法首先采用高通滤波和平台直方图均衡方法对红外图像进行预处理以提高红外图像的对比度,再利用具有图像对比度不变性的相位一致性边缘检测法提取两幅图像的边缘,结合Hough变换选取图像空间中最长的线作为特征,采用改进相位相关法作为相似性度量,在对数极坐标域下计算出两幅图像的几何变形参数。仿真实验结果表明,该方法能够以较高查准率实现红外与可见光图像自动配准,并具有较强的鲁棒性。     

Non-local and nonlinear background suppression method controlled by multi-scale clutter metric

To improve the detection performance for non-morphological multi-scale target in IR image containing complex cloud clutter, on basis of cloud scenario self-similarity feature, a non-local and nonlinear background suppression algorithm controlled by multi-scale clutter metric is presented. According to the classical achievements on cloud structure, self-similarity and relativity of cloud clutter on image for target detection is deeply analyzed by classical indicators firstly. Then we establish multi-scale clutter metric method based on LoG operator to describe scenes feature for controlled suppression method. After that, non-local means based on optimal strength similarity metric as non-local processing, and multi-scale median filter and on minimum gradient direction as local processing are set up. Finally linear fusing principle adopting clutter metric for local and non-local processing is put forward. Experimental results by two kinds of infrared imageries show that compared with classical and similar methods, the proposed method solves the existing problems of targets energy attenuation and suppression degradation in strongly evolving regions in previous methods. By evaluating indicators, the proposed method has a superior background suppression performance by increasing the BSF and ISCR 2 times at least.     

基于图像内容视觉感知的图像质量客观评价方法

图像质量客观评价在图像和视频传输、编解码以及服务质量中起着非常重要的作用.然而现有的方法往往没有考虑图像内容特征及其视觉感知,使得其质量客观评价与主观感知结果存在一定的差距.基于此,本文结合图像内容的复杂性特征和人眼的掩蔽特性、对比敏感度特性以及亮度感知的非线性特性,提出了一种基于人眼对图像内容感知的图像质量客观评价方法.该方法首先结合亮度感知的非线性模型将图像进行转换,得到人眼感知强度图;再分别以人眼对比敏感度值和图像局部平均对比度值作为权重因子对强度求和,以求和的数据信息作为人眼感知图像的内容,并构建图像感知模型;最后以此模型分别模拟人眼感知参考图像和失真图像,并计算二者的强度差,以强度差为评价分数的基础构建图像质量客观评价模型.采用LIVE,TID2008和CSIQ三个数据库中的共47幅参考图像和1549幅测试图像进行仿真实验,且与SSIM,VSNR,FSIM和PSNRHVS等典型的图像质量客观评价模型进行对比分析,同时探讨影响图像质量评价的因素.结果表明:所提方法的评价分数与主观评价分数的Pearson线性相关性系数和Spearman秩相关系数值比SSIM的评价结果均有一定程度的提高,提高幅度分别平均为9.5402%和3.2852%,比PSNRHVS和VSNR提高幅度更大.综合以上表明:所提方法是一种有效可行的图像质量客观评价方法;同时,在图像质量客观评价中,考虑人眼对图像内容的感知和复杂度的分析有助于提高图像质量主客观评价的一致性,评价精度可得到进一步的提高.     

An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system

To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposed an improved algorithm based on the contrast mechanism of human visual system (HVS) for infrared small target detection in an image with complicated background. According to the contrast mechanism of HVS, Laplacian of Gaussian (LoG) filter is exploited to deal with the input image, which can not only suppress the background noise and clutter but also enhances the target intensity significantly. As a result it increases the contrast ratio between target and background. To further eliminate residual clutter, we process the filtered image with morphological method in all directions. True target is finally obtained by applying local thresholding segmentation to the pre-processed image. Experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate.     

基于波原子变换的红外复杂背景杂波抑制算法

针对红外图像弱小目标检测技术中复杂背景杂波干扰问题,提出了一种基于波原子变换的红外图像背景抑制算法。首先,采用波原子变换对图像进行多尺度和多方向分解,获得原始图像的多尺度和多方向细节特征;然后,根据目标和背景杂波信号的差异,通过频域变换设计的系数调整函数修正经波原子变换后各子带系数,再经波原子逆变换重构得到估计的背景图像;最后,将其与原始图像相减获得背景杂波抑制后的图像。用真实的红外图像序列进行实验,结果显示,与最大中值和小波变换两种算法相比,该算法能有效地抑制红外弱小目标复杂背景杂波,突出目标信号,提高信杂比,具有良好的背景抑制性能。     

A categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference features

The fusion of infrared polarization and intensity image can significantly improve the detection performance of target, and the fused image is more suitable for human visual perception and further image-processing tasks. In this paper, a new categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference feature is proposed. Firstly, the difference feature between two kinds of image and the characteristics of different fusion algorithms are analyzed and summarized. Second, an evaluation vector of fusion algorithm for difference feature transform ability is constructed. Thirdly, the transfer ability of fusion algorithm for difference feature is estimated by the evaluation vector, and the degree of transfer ability of fusion algorithm for difference feature is analyzed. Finally the fusion algorithms are classified by the degree of transfer ability of fusion algorithm for difference feature. The results shows that the proposed fusion algorithm categorization method helps select fusion algorithms in actual scene.     

Infrared dim small target enhancement using toggle contrast operator

A new infrared dim small target enhancement algorithm based on toggle contrast operator is proposed. Toggle contrast operator is modified and used to construct operators using the image features derived from dilation and erosion operators. Then, based on the constructed operators, the operators which could be used to estimate the clutter background of the original infrared dim small target image are proposed using the same strategy as the definition of opening. Finally, the infrared dim small target is well enhanced through subtracting the estimated background from the original image. Experimental results on infrared images with different types of targets verified that the proposed method could effectively enhance infrared dim small target, which would be very useful for infrared dim small target detection and tracking.     

基于多分辨率双边滤波的红外场景杂波抑制

背景杂波是影响红外搜索跟踪系统探测性能的主要因素,针对这一问题,根据红外场景中目标和背景特性,提出了一种基于多分辨率双边滤波的红外场景杂波抑制新方法.首先采用非下采样轮廓波对红外场景图像进行多尺度、多方向分解,提取红外原始场景图像在不同尺度和方向上的细节特征,然后,根据目标和背景信号子带分布特性之差异,通过应用双边滤波调整分解后的各子带系数,最后重构各子带就可将红外场景中目标信号和背景杂波分离,可有效地将背景杂波剔除掉.将本文提出的方法应用于实际的红外场景,实验结果显示,与经典的二维最小均方误差方法相比较,该方法具有更好的杂波抑制能力.     

Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images

Current medical ultrasonic scanning instrumentation permits the display of fine image detail (speckle) which does not transfer useful information but degrades the apparent low contrast resolution in the image. An adaptive two-dimensional filter has been developed which uses local features of image texture to recognize and maximally low-pass filter those parts of the image which correspond to fully developed speckle, while substantially preserving information associated with resolved-object structure. A first implementation of the filter is described which uses the ratio of the local variance and the local mean as the speckle recognition feature. Preliminary results of applying this form of display processing to medical ultrasound images are very encouraging; it appears that the visual perception of features such as small discrete structures, subtle fluctuations in mean echo level and changes in image texture may be enhanced relative to that for unprocessed images.     

基于双层局部对比度的红外弱小目标检测方法

针对传统基于人类视觉系统的检测方法在复杂背景下容易造成检测虚警的问题,提出一种基于双层局部对比度的红外弱小目标检测方法.首先,通过双层对角灰度差对比度分析机制,充分利用小目标局部对比度的先验信息,提高目标对比度的同时抑制背景杂波及噪声;之后,利用自适应阈值分割法获取待检测的真实目标.实验结果表明,与主流基于人类视觉系统的检测方法相比,所提方法的背景抑制因子平均提高9.3倍以上,信杂比率增益平均提高7.8倍以上,在不同的复杂场景下均具有更好的检测性能.     

Pedestrian detection from thermal images: A sparse representation based approach

Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects’ emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.     

尺度不变特征与几何特征融合的人耳识别方法

要提高人耳的识别率,关键是特征的提取与表达.尺度不变特征变换(SIFT)技术是局部点特征提取算法,在尺度空间寻找极值点,提取对图像的尺度和旋转变化具有不变性,对光照变化和图像变形具有较强的适应性的特征向量.尝试用SIFT技术来提取外耳图像的结构特征点以形成稳定的特征描述子,为了克服一幅图像中有多个局部描述子相似的问题,在SIFT特征描述子中融入一个耳廓几何特征.最后采用特征向量的欧氏距离作为两幅图像相似性度量标准进行人耳识别.在耳图像库七进行实验.结果表明,该方法不仅可以有效地提取人耳特征,通过少量特征可获得较高的识别率,而且对耳图像刚体变化具有较强的稳健性.     

Improvement in appearance of colors for elderly persons using lightness transform

The color of an object appears to be different for elderly and young persons. This is because human visual features vary with on age. In general, the spectral transmittance of the crystalline lens of an elderly person is lower than that of a young person. The low spectral transmittance decreases the brightness of the visual field. This phenomenon affects the color appearance of an image in human visual perception. In this paper, a lightness transform method is proposed to improve the color appearance of images for elderly persons. In the proposed method, colors that are difficult for an elderly person to see are transformed into colors that are easy to see by adding lightness contrast. The performance of the proposed method is confirmed in experiments using digital images.     

一种新的红外成像末制导目标跟踪方法

为了稳定跟踪导弹末制导阶段的红外目标,提出了一种基于尺度不变特征变换的红外目标跟踪算法.尺度不变性特征变换所提取的图像纹理特征具有尺度和旋转不变性,跟踪算法分别提取目标模板和待跟踪图像的尺度不变特征变换特征.根据最小欧氏距离准则提取目标模板与待跟踪图像间相匹配的尺度不变特征变换特征点对,利用该特征点对拟合反映两图像间映射关系的仿射模型,并据此估计目标中心位置及调整目标模板尺寸.仿真结果表明,跟踪算法能够较好地实现在导弹末制导阶段对红外地面杂波背景下目标的稳定跟踪,其跟踪准确度和稳定度优于传统方法.
关键词:末制导跟踪|尺度不变性特征变换|特征匹配|仿射模型     

结合视觉注意力机制和图像锐度的无参图像质量评价方法

针对模糊图像的质量评价,提出一种新的无参图像质量评价方法,该方法结合了自底向上的视觉注意力机制和自顶向下的图像锐度评价标准。根据人眼视觉注意力机制模型,分别计算颜色、亮度和方向显著度图像,通过竞争机制得到人眼优先关注的区域; 利用无参图像锐度评价方法分别对优先关注的区域及背景区域进行评价,综合2个区域的评价结果得到最终的图像质量评价指标。利用该方法分别对相向运动过程中所产生的模糊图像和图像质量评价Live数据库中的高斯模糊图像进行了评价,结果表明:针对两类图像的评价结果与主观评价结果的相关系数均较高,其中,针对相向运动模糊图像的主客观评价结果的相关系数达到0.98。该方法能够胜任对模糊图像的客观质量评价。