Registration of remote-sensing images using robust weighted kernel principal component analysis

For pre- and post-earthquake remote-sensing images, registration is a challenging task due to the possible deformations of the objects to be registered. To overcome this problem, a registration method based on robust weighted kernel principal component analysis is proposed to precisely register the variform objects. Firstly, a robust weighted kernel principal component analysis (RWKPCA) method is developed to capture the common robust kernel principal components (RKPCs) of the variform objects. Secondly, a registration approach is derived from the projection on RKPCs. Finally, two experiments are conducted on the SAR image registration in Wenchuan earthquake on May 12, 2008, and the results showed that the method is very effective in capturing structure patterns and generalized well for registration.     

Toward robust analysis of satellite images using mapinformation-application to urban area detection

With the rapid development of remote sensing, digital image processing has become an important tool for the quantitative and statistical analysis of remotely sensed images. These images most often contain complex natural scenes. The robust interpretation of such images requires the use of different sources of information about the scenes under consideration. This paper presents an integrated approach to robust analysis of SPOT images with the aid of map information as well as a priori knowledge about the contextual information of images. Markov random field theory and the Bayes formula are used to formulate the image analysis problem as a problem of optimization of an objective function, which in turn permits the application of various existing optimization algorithms to solve the problem. To increase the robustness of the result, several techniques are proposed to effectively use map information and image contextual information. The first one is concerned with the estimation of the parameters in the objective function with the help of these two sources of information. The second one is the integration of map information in Bayes image modeling using a Markov random field. The third one is a new optimization algorithm which takes into account map information and image contextual information by means of a feedback control scheme. The last technique proposed to increase the robustness of the result is concerned with the fusion of several (intermediate) analysis results by again using map knowledge and image contextual information for the estimation of the reliability of these results     

SAR图像中目标配准的稳健加权核主成分分析方法

针对震前震后合成孔径雷达(SAR)图像中发生复 杂形变的目标,提 出了基于稳健的加权核主成分分析(KPCA)的配准方法。首先,提出 一种稳健的 加权KPCA(RWKPCA)方法,不仅能获得震前震后形变目标的共同稳健核主成分(RKPC s),还可 以作为异常值判别准则;其次,利用在共同RKPCs上的投影定义震前震后形变目标特 征的相似性度 量;最后,利用特征的相似性度量精确配准形变目标。对2008年5月12日汶川地震前后的S AR图像进行配准并与现有方法进行比较,结果表明,本文方法能够有效的得到形变目标的 共同RKPCs,并得到很好的配准结果。     

Fast and robust generation of feature maps for region-based visual attention.

Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.     

Content-based image retrieval using local visual attention feature

Content-based image retrieval (CBIR) has been an active research topic in the last decade. As one of the promising approaches, salient point based image retrieval has attracted many researchers. However, the related work is usually very time consuming, and some salient points always may not represent the most interesting subset of points for image indexing. Based on fast and performant salient point detector, and the salient point expansion, a novel content-based image retrieval using local visual attention feature is proposed in this paper. Firstly, the salient image points are extracted by using the fast and performant SURF (Speeded-Up Robust Features) detector. Then, the visually significant image points around salient points can be obtained according to the salient point expansion. Finally, the local visual attention feature of visually significant image points, including the weighted color histogram and spatial distribution entropy, are extracted, and the similarity between color images is computed by using the local visual attention feature. Experimental results, including comparisons with the state-of-the-art retrieval systems, demonstrate the effectiveness of our proposal.     

Blind self-authentication of images for robust watermarking using integer wavelet transform

In this letter a new technique for robust watermarking of images is proposed based on second-generation wavelets (lifting-based integer wavelet transform). The scheme along with its robustness has got the capability of blind self-authentication of the watermarked images. The watermarked images show no perpetual degrading and gives peak signal to noise ratio (PSNR) in excess of 40 dB due to the use of integer-to-integer transform. Simulation results show the superior performance of the proposed technique as compared to a similar existing scheme under different attacks such as filtering, compression and rotation. Further, due to two independent process of watermark sequence extraction proposed in this letter, any alteration in the image results into dissimilar sequences, thereby detecting tampering in the image.     

A robust past algorithm for subspace tracking in impulsive noise

The PAST algorithm is an effective and low complexity method for adaptive subspace tracking. However, due to the use of the recursive least squares (RLS) algorithm in estimating the conventional correlation matrix, like other RLS algorithms, it is very sensitive to impulsive noise and the performance can be degraded substantially. To overcome this problem, a new robust correlation matrix estimate, based on robust statistics concept, is proposed in this paper. It is derived from the maximum-likelihood (ML) estimate of a multivariate Gaussian process in contaminated Gaussian noise (CG) similar to the M-estimates in robust statistics. This new estimator is incorporated into the PAST algorithm for robust subspace tracking in impulsive noise. Furthermore, a new restoring mechanism is proposed to combat the hostile effect of long burst of impulses, which sporadically occur in communications systems. The convergence of this new algorithm is analyzed by extending a previous ordinary differential equation (ODE)-based method for PAST. Both theoretical and simulation results show that the proposed algorithm offers improved robustness against impulsive noise over the PAST algorithm. The performance of the new algorithm in nominal Gaussian noise is very close to that of the PAST algorithm.     

Building robust wavelet estimators for multicomponent images using Stein's principle.

Multichannel imaging systems provide several observations of the same scene which are often corrupted by noise. In this paper, we are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein's principle to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise. Simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques.     

浅析基于空间信息的视觉注意模型

近几年,基于频谱变化的视觉显著性检测模型逐渐成为研究热点,大多数频域视觉注意模型的主要思想通常是利用离散傅里叶变换或离散余弦变换等方法把图像从空间域变换到频谱域,然后通过对频域内的相位谱、幅度谱等信息进行分析和处理,提取显著信息,最后再通过逆变换到空间域并进行后续处理得到最终的显著图。     

Detection of moving objects in video using a robust motionsimilarity measure

This correspondence deals with the segmentation of a video clip into independently moving visual objects. This is an important step in structuring video data for storage in digital libraries. The method follows a bottom-up approach. The major contribution is a new well-founded measure for motion similarity leading to a robust method for merging regions. The improvements with respect to existing methods have been confirmed by experimental results.     

Saliency detection in human crowd images of different density levels using attention mechanism

The human visual system has the ability to rapidly identify and redirect attention to important visual information in high complexity scenes such as the human crowd. Saliency prediction in the human crowd scene is the process using computer vision techniques to imitate the human visual system, predicting which areas in a human crowd scene may attract human attention. However, it is a challenging task to identify which factors may attract human attention due to the high complexity of the human crowd scene. In this work, we propose Multiscale DenseNet — Dilated and Attention (MSDense-DAt), a convolutional neural network (CNN) using self-attention to integrate the result of knowledge-driven gaze in the human visual system to identify salient areas in the human crowd scene. Our method combines various state-of-the-art deep learning architectures to deal with the high complexity in human crowd image, such as multiscale DenseNet for multiscale deep features extraction, self-attention, and dilated convolution. Then the effectiveness of each component in our CNN architecture is evaluated by comparing different components combinations. Finally, the proposed method is further evaluated in different crowd density levels to appraise the effect of crowd density on model performance.     

Exploring visual attention using random walks based eye tracking protocols

Identifying visual attention plays an important role in understanding human behavior and optimizing relevant multimedia applications. In this paper, we propose a visual attention identification method based on random walks. In the proposed method, fixations recorded by the eye tracker are partitioned into clusters where each cluster presents a particular area of interest (AOI). In each cluster, we estimate the transition probabilities of the fixations based on their point-to-point adjacency in their spatial positions. We obtain the initial coefficients for the fixations according to their density. We utilizing random walks to iteratively update the coefficients until their convergency. Finally, the center of the AOI is calculated according to the convergent coefficients of the fixations. Experimental results demonstrate that our proposed method which combines the fixations’ spatial and temporal relations, highlights the fixations of higher densities and eliminates the errors inside the cluster. It is more robust and accurate than traditional methods.     

Detection of urban structures in SAR images by robust fuzzyclustering algorithms: the example of street tracking

The authors present a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images of urban environments. In particular, they want to show how to implement structure extraction algorithms based on fuzzy clustering unsupervised approaches. To this aim, the idea is to segment first the sensed data and recognize very basic urban classes (vegetation, roads, and built areas). Then, from these classes, we extract structures and infrastructures of interest. The initial clustering step is obtained by means of fuzzy logic concepts and the successive analyses are able to exploit the corresponding fuzzy partition. As a possible complete procedure for urban SAR images, they focus on the street tracking and extraction problem. Three road extraction algorithms available in literature (namely, the connectivity weighted Hough transform (CWHT), the rotation Hough transform, and the shortest path extraction) have been modified to be consistent with the previously computed fuzzy clustering results. Their different capabilities are applied for the characterization of streets with different width and shape. The whole approach is validated by the analysis of AIRSAR images of Los Angeles, CA     

Detection of blood vessels in retinal images using two-dimensional matched filters

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.     

Automatic foveation for video compression using a neurobiological model of visual attention

We evaluate the applicability of a biologically-motivated algorithm to select visually-salient regions of interest in video streams for multiply-foveated video compression. Regions are selected based on a nonlinear integration of low-level visual cues, mimicking processing in primate occipital, and posterior parietal cortex. A dynamic foveation filter then blurs every frame, increasingly with distance from salient locations. Sixty-three variants of the algorithm (varying number and shape of virtual foveas, maximum blur, and saliency competition) are evaluated against an outdoor video scene, using MPEG-1 and constant-quality MPEG-4 (DivX) encoding. Additional compression radios of 1.1 to 8.5 are achieved by foveation. Two variants of the algorithm are validated against eye fixations recorded from four to six human observers on a heterogeneous collection of 50 video clips (over 45 000 frames in total). Significantly higher overlap than expected by chance is found between human and algorithmic foveations. With both variants, foveated clips are, on average, approximately half the size of unfoveated clips, for both MPEG-1 and MPEG-4. These results suggest a general-purpose usefulness of the algorithm in improving compression ratios of unconstrained video.     

Wavelet based watermarking method for digital images using thehuman visual system

A wavelet based multiresolution watermarking method using the human visual system (HVS) is proposed, with the number of watermarks embedded proportional to the energy contained in each band. Experiments show that the proposed three-level wavelet based watermarking method is robust to some attacks such as, for example, joint photographic experts group (JPEG) compression, smoothing, cropping and collusion     

视觉注意驱动的基于混沌分析的运动检测方法

提出了视觉注意驱动的基于混沌分析的运动检测方法(MDSA)。MDSA首先基于视觉注意机制提取图像的显著区域,而后对显著区域进行混沌分析以检测运动目标。算法技术路线为:首先根据场景图像提取多种视觉敏感的底层图像特征;然后根据特征综合理论将这些特征融合起来得到一幅反映场景图像中各个位置视觉显著性的显著图;而后对显著性水平最高的图像位置所在的显著区域运用混沌分析的方法进行运动检测;根据邻近优先和返回抑制原则提取下一最显著区域并进行运动检测,直至遍历所有的显著区域。本文对传统的显著区域提取方法进行了改进以减少计算量:以邻域标准差代替center-surround算子评估图像各位置的局部显著度,采用显著点聚类的方法代替尺度显著性准则提取显著区域;混沌分析首先判断各显著区域的联合直方图（JH）是否呈现混沌特征,而后依据分维数以一固定阈值对存在混沌的JH中各散点进行分类,最后将分类结果对应到显著区域从而实现运动分割。MDSA具有较好的运动分割效果和抗噪性能,对比实验和算法开销分析证明MDSA优于基于马塞克的运动检测方法（MDM）。      

Nonlinear filtering of multivariate images under robust errorcriterion

A class of nonlinear filters for multivariate data is introduced. A robust error criterion is minimized. Approximate algorithms for computing the filter output are developed. A polynomial signal model is used in applications where the signal amplitude has to be retained with high fidelity. Simulated data and RGB color image data are used in experiments.     

A general study of maximal robust stability regions

In the study of robust stability, the largest coefficient region of a given stable polynomial that guarantees stability preservation under perturbation of coefficients is to be determined. A general consideration including both Hurwitz and Schur polynomials is treated in this paper. For this purpose, the notion ofperturbation constant is introduced. As a consequence of our results, we also introduce a general Kharitonov-type stability test which is based on testing the stability and perturbation constant of a single polynomial.The work of C. K. Chui was supported by SDIO/1ST managed by ARO under Contract No. DAAL 03-87-K-0025, and that of H. N. Mhaskar was supported in part by the Center for Approximation Theory, Texas A&M University.