合成孔径雷达图象结构特征检测的改进算法

本义研究合成孔径雷达（ＳＡＲ）图象结构特征检测。首先讨论了 ＳＡＲ图象结构检测的原理,然后在分析Ｌｏｐｅｓ等人的算法的基础上,提出了一个改进的结构检测算法,给出改进算法的实现流程,并利用模拟和实测ＳＡＲ图象进行试验比较,充分验证了改进方法的优越性和实用性。     

基于Bézier曲线的DR图像平滑算法

分析了DR(Dead Reckoning)递推和平滑的原理及常用算法,提出了一个新的基于Bézier曲线的DR图象平滑算法,讨论了DR算法中阈值、步长和平滑时间三个重要参数的选择问题.将该算法应用于一个分布交互仿真系统-综合仿真系统(Synthetic Si mulation System,SSS)中,取得了良好的图象平滑效果.     

模式识别图象预处理中的目标快速定位法

在模式识别和机器视觉等系统的实际应用中，首先要完成图象预处理的任务，即需要从实时采集的图象中把感兴趣的物体检测出来，以便于后续的识别处理。本文提出了一种基于二值图象形态学腐蚀运算的快速目标测定位法，通过修改腐蚀的运算的算法，构造两个新的形态结构元素可快速有效地对目标进行检测定位，文中给出了基本算法和实验结果。     

噪声图象滤波的稳健混合M滤波器

本文提出一种在空间域根据采集图象局部统计特性和相关信息进行噪声滤波算法─稳健混合M型滤波器．该滤波算法不仅能有效地抑制各种图象噪声分量，而且克服线性滤波算法引起的边缘模糊和细节损失，保持图象边缘和几何结构。选择中值做参考信号有效地改善图象的信噪比和对比度，使滤波后图象具有良好的视觉效果．同CS滤波器、HDMF滤波器、DWMTMF滤波器、自适应L滤波器性能相比较，本文提出的方法在滤波、边缘保持等方面有突出的优点。     

图象目标识别图形的自适应步级检测

建立图象目标识别模型，用形状、灰度和运动特征描述图象目标。基于目标建模，把目标识别、门限及目标图形区域步级检测、虚漏警调节和目标空域条件有机地结合起来，给出牵引式跟踪系统中图象目标识别图形的自适应步级检测算法。该算法用于检测具有识别特征的图象目标图形，并成功地应用到实际的实时跟踪系统中的图象目标识别和跟踪。对实际图象的处理结果和在实时跟踪系统中的实验说明本文研究的技术的有效性和适用性。     

复杂图象序列的自适应目标提取和跟踪方法

本文根据视知觉原理研究了提取和跟踪复杂图象中运动目标的计算模型和算法，分析和检验了改变模型有关参数对图象分割门限的影响。与某些常规算法不同的是，新方法综合考虑了目标一背景条件、视觉非线性、帧间相关性和差异性，目标提取作为一个完整瓣两步过程包括三个准则和一个快速寻优算法，目标跟踪使用二值模板匹配，给出了在可见光图象序列上的实验结果。     

图象跟踪算法仿真环境

图象跟踪系统具有重要的军事价值，所选用的跟踪算法的俦到图象跟踪系统的性能，为了分析各种跟踪算法在不同应用环境下的跟踪能力，开发研究新的跟踪算法，对各种算法某种评价其跟踪状况的准则，建立了一个图象跟踪算法仿真环境。     

融合阴影和结构照明图象以获得三维景物信息

目的是提供融合灰度图象和结构照明图象进行景物分析的几种几何方法。某些三维信息，如面取向，是由优化技术数值方法借助结构照明栅格风线和密闭曲线的边界条件得到的。     

遥感图象边缘检测的松驰方法

松驰法可用于遥感图象的边缘检测,其主要思想是利用图象的相关性及相邻关系,消除图象中边缘信息的二义性,改善检测质量和效果。通常,这类松驰处理是在单一分辨图象上进行的。本文讨论了遥感图象的多分辨率表示方法。提出一种多分辨图象的边缘检测松驰算法。该算法利用多分辨率遥感图象的信息,可以进一步改进检测质量,有效地克服噪声的影响。并可提高算法的效率,减少计算量。     

闭合曲线的斜对称轴线的检测

本文介绍了闭合曲线的斜对称定义，以及新近获得的相关理论分析结果，并在此基础上提出一个基于对称曲线的微分性质，来检测闭合曲线的斜对称性，还设计了一个新算法，实验结果表明，该算法能够快速准确地找出闭合曲线的所有斜对称轴线。     

Salient edge and region aware image retargeting

The purpose of image retargeting is to automatically adapt a given image to fit the size of various displays without introducing severe visual distortions. The seam carving method can effectively achieve this task and it needs to define image importance to detect the salient context of images. In this paper we present a new image importance map and a new seam criterion for image retargeting. We first decompose an image into a cartoon and a texture part. The higher order statistics (HOS) on the cartoon part provide reliable salient edges. We construct a salient object window and a distance dependent weight to modify the HOS. The weighted HOS effectively protects salient objects from distortion by seam carving. We also propose a new seam criterion which tends to spread seam uniformly in nonsallient regions and helps to preserve large scale geometric structures. We call our method salient edge and region aware image retargeting (SERAR). We evaluate our method visually, and compare the results with related methods. Our method performs well in retargeting images with cluttered backgrounds and in preserving large scale structures.     

A shape-based image retrieval method using salient edges

Content-based image retrieval is emerging as an important research area with applications in digital libraries and multimedia databases. In this paper, we present a novel five-stage image retrieval method based on salient edges. In the first stage, the Canny operator is performed to detect edge points. Then, the Water-Filling algorithm is employed to extract edge curves. In the third stage, salient edges are selected and the shape features in terms of the salient edges are yielded. In the fourth stage, a similarity measure, namely the integrated salient edge matching, that integrates properties of all the salient edges, is introduced, and used to compare the similarity of the query image with the images in the database. Finally, the best matches are returned in similarity order. The presented approach is easy to implement and can be efficiently applied to retrieve images with clear edges. Preliminary experimental results on a database containing 6500 images are very promising.     

Boundary enhancement and speckle reduction for ultrasound images via salient structure extraction

In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.     

Using hidden scale for salient object detection.

This paper describes a method for detecting salient regions in remote-sensed images, based on scale and contrast interaction. We consider the focus on salient structures as the first stage of an object detection/recognition algorithm, where the salient regions are those likely to contain objects of interest. Salient objects are modeled as spatially localized and contrasted structures with any kind of shape or size. Their detection exploits a probabilistic mixture model that takes two series of multiscale features as input, one that is more sensitive to contrast information, and one that is able to select scale. The model combines them to classify each pixel in salient/nonsalient class, giving a binary segmentation of the image. The few parameters are learned with an EM-type algorithm.     

Visual privacy-preserving level evaluation for multilayer compressed sensing model using contrast and salient structural features

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.     

Registration of Images With Outliers Using Joint Saliency Map

Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint histogram. However, MI is sensitive to the “outlier” objects that appear in one image but not the other, and also suffers from local and biased maxima. We propose a novel joint saliency map (JSM) to highlight the corresponding salient structures in the two images, and emphatically group those salient structures into the smoothed compact clusters in the weighted joint histogram. This strategy could solve both the outlier and the local maxima problems. Experimental results show that the JSM-MI based algorithm is not only accurate but also robust for registration of challenging image pairs with outliers.      

An Overview of Potential Factors for Effective Telemedicine Transfer to Sub-Saharan Africa

This paper gives an overview of core factors mitigating effective transfer of TeleMedicine to Sub-Saharan Africa (SSA) as a capability for improving the extremely poor state of healthcare delivery systems in that region of the world. Using specific examples of TeleMedicine applications, such as in TeleRadiology and health education, the paper highlights the importance of TeleMedicine in SSA. It then presents the salient factors that influence TeleMedicine technology transfer in the form of a conceptual framework. In explaining the framework, the paper offers opinions and supportive arguments on the importance and significance of the identified factors in effective TeleMedicine ldquouptakerdquo within the SSA. We believe the framework provides a grounded theoretical basis that information and communications technologies (ICT) or technology transfer researchers can use for empirical investigation in order to understand the efficacy of TeleMedicine adoption within developing countries at large.     

Incremental refinement of image salient-point detection

Low-level image analysis systems typically detect "points of interest", i.e., areas of natural images that contain corners or edges. Most of the robust and computationally efficient detectors proposed for this task use the autocorrelation matrix of the localized image derivatives. Although the performance of such detectors and their suitability for particular applications has been studied in relevant literature, their behavior under limited input source (image) precision or limited computational or energy resources is largely unknown. All existing frameworks assume that the input image is readily available for processing and that sufficient computational and energy resources exist for the completion of the result. Nevertheless, recent advances in incremental image sensors or compressed sensing, as well as the demand for low-complexity scene analysis in sensor networks now challenge these assumptions. In this paper, we investigate an approach to compute salient points of images incrementally, i.e., the salient point detector can operate with a coarsely quantized input image representation and successively refine the result (the derived salient points) as the image precision is successively refined by the sensor. This has the advantage that the image sensing and the salient point detection can be terminated at any input image precision (e.g., bound set by the sensory equipment or by computation, or by the salient point accuracy required by the application) and the obtained salient points under this precision are readily available. We focus on the popular detector proposed by Harris and Stephens and demonstrate how such an approach can operate when the image samples are refined in a bitwise manner, i.e., the image bitplanes are received one-by-one from the image sensor. We estimate the required energy for image sensing as well as the computation required for the salient point detection based on stochastic source modeling. The computation and energy required by the proposed incremental refinement approach is compared against the conventional salient-point detector realization that operates directly on each source precision and cannot refine the result. Our experiments demonstrate the feasibility of incremental approaches for salient point detection in various classes of natural images. In addition, a first comparison between the results obtained by the intermediate detectors is presented and a novel application for adaptive low-energy image sensing based on points of saliency is presented.     

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.     

Kernel estimation from salient structure for robust motion deblurring

Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the kernel estimation, especially when the blur kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the kernel estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of kernel estimation. Third, we proposed a novel kernel estimation method, which is capable of removing noise and preserving the continuity in the kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.