单幅图像阴影检测与去除算法的研究

阴影检测和阴影去除在图像中扮演着至关重要的角色。尽管目前已经有很多算法在不同的环境和假设前题下被提出,但检测自然场景中的阴影并将其去除仍然是一项具有挑战性的任务。文中阐述了单幅图像阴影检测和去除的典型算法,并进一步分析了各自的原理以及优缺点。为了更直观地对比各种算法的处理效果,文中给出了不同算法对同一幅图像进行检测及去除的实验结果。最后对这些算法的优缺点进行总结,并对今后阴影检测与去除算法的发展应用进行了展望。     

基于二维道路图像理解算法的阴影检测

提出了一种新的自动检测二维彩色道路场景图像阴影区域的方法。该方法首先从RGB色彩空间中提取出亮度信息,并进行图像分割,再结合从色差图像中提取的阴影信息,检测并消除可能存在的阴影区域,减小在直方图分割中由于阴影的影响而引起的分割误差,最终达到正确的图像分割结果。实验结果表明,该方法简便且有效,并能适应各种不同的二维道路场景需求,具有一定的鲁棒性。     

基于序列图像的阴影检测研究

针对由于入射光的照射,目标阴影与背景有明显的区别,而被当做目标的问题进行了研究。分析了单幅图像的阴影检测方法,提出了序列图像阴影检测方法,建立了序列图像高度测量模型;采用相邻序列图像对应点匹配的约束条件,降低了匹配的复杂度;研究了序列图像的特征点匹配,获得平面目标的高度信息表示;基于阴影的二维表面属性,设计了利用空间三维信息中的高度信息检测阴影的算法,可以对任意形状的阴影进行检测。通过试验表明,该方法简单,适用面广,可以有效地检测阴影。     

基于阴影检测的单幅高分辨SAR图像动目标检测方法

本文根据合成孔径雷达(SAR)运动目标的成像特点,提出了一种基于单幅高分辨率SAR图像的动目标检测方法。运动目标所成SAR图像是散焦的,同时偏离其真实位置,并在其真实位置处留下与目标大小近似的"阴影"区域。通过该阴影区域来实现运动目标的检测,没有盲速的限制,这种独立于回波的检测方法,使检测过程简单化。再结合道路辅助信息,可以完成运动目标的检测,精确定位以及参数的估计。     

基于分数阶微分的Kinect传感器深度图像阴影检测方法

深度图像作为Kinect传感器的重要组成部分,其获得的深度图像往往伴随着不可避免和无法预知的阴影噪声,这也极大地影响并制约其在三维可视化等方面的应用及研究。因此,针对深度图像提出了一种基于分数阶微分的阴影检测方法。在研究分数阶微分定义的Tiansi模板基础上,设计并实现了一种非线性拉伸算子。该算子在0.6阶次可以增强阴影区域边界信息的同时实现阴影的有效检测。通过分析比较发现,该方法在F测度的评价体系中可以达到0.971,而其他传统的检测方法均小于0.7。实验结果证明文中提出方法可以有效实现深度图像的阴影检测。     

基于度量除图像平坦度阴影检测与消除的研究

通过对交通监控场景图像观察,可以发现运动目标常常会在背对光源的方向投下阴影.而且运动目标阴影的大小由于时间的变化、太阳的运动而引发变化,阴影部分还和目标一样显著区别于背景,和目标有相同的运动规律,使得阴影检测成为一个有待解决的难题.本文先对目前常见阴影消除方法分析,随后在运动目标初步检测完成后,通过一种度量除图像平坦度...     

基于阴影向量扩展的阴影检测方法

图像处理中阴影检测是一个重要的主题。本文提出了一种基于阴影向量扩展的阴影检测新方法。该方法依据阴影区域的光学特性,先找出前景中能够表达阴影区域主要特征的阴影向量,再根据阴影向量扩展出整个阴影区域。测试图库的实验结果表明该方法能有效的检测出前景中的阴影,并且改善了传统方法在强阴影识别方面的缺陷,以及常常把目标局部误识别为阴影的问题。     

基于似圆阴影的光学遥感图像油罐检测

针对光学遥感图像中受阴影干扰的油罐目标识别率低的问题,该文提出一种将改进的视觉显著模型与似圆阴影区域特征检测相结合的由粗到精的油罐目标检测方法。首先建立改进的视觉显著模型,将油罐从复杂背景中粗分离。然后对分离结果中由油罐产生的似圆阴影区域进行精检测,得到疑似油罐目标。再去除阴影,获得油罐目标的初步检测结果。最后基于图搜索策略及先验知识,确定油罐目标并定位油库区域。实验结果表明,该方法对检测光学遥感图像中存在似圆阴影的油罐目标具有较高的鲁棒性和准确率。同时,在不同环境的光学遥感图像中使用该方法可快速准确地定位油库区域。     

基于PCNN的图像阴影处理新方法

该文提出了如何用脉冲耦合神经网络(PCNN)进行图像阴影处理的新方法。计算机仿真表明,采用参数优选的PCNN图像阴影处理算法,可有效地处理图像中的阴影,从而使得图像中的目标易于被发现。该文工作拓展了有明确生物学背景的PCNN在图像处理方面的功能。     

视频图像中的车辆检测及阴影去除方法研究

视频监控系统成为近年来的研究热点。文中针对交通智能视频检测系统的车辆检测问题,采用基于高斯混合模型方法来检测视频中的运动车辆,结合车辆阴影纹理不变的特性分割阴影,实现了运动车辆的检查识别和阴影去除。仿真实验达到了预期效果。     

航空图像中阴影区域的检测

航空图像中的阴影区域可以提供大量的关于目标形状、相对位置、表面特性、高度的相关信息,本文首先对阴影的特征进行了研究,认为阴影对图像的影响是使所投射地表的表面光照强度降低,但不改变原有地表,目标表面的光照特征,如纹理特征及光照强度方向统计特征等。阴影区域内的灰度方差一般小于其他非阴影区域,不同阴影区域之间的灰度值具有较强的一致性。然后,本文利用基于二维直方图的最大熵门限化方法求得边缘图像,并对边缘图像进行了细化和跟踪,删除那些对比很弱、或者虽然对比度较强,但是不满足光照条件的边缘,得到阴影边缘。在门限化过程中使用了邻域函数和过渡区域,既保留了弱边缘,又最大程度地抑制了噪声对边缘的干扰。根据阴影边缘构出假想的阴影区域,并统计这些区域的灰度直方图,求得阴影区域的灰度区间。根据该灰度区间可以得到阴影区域,这些区域具有对比度较高、至少有一侧比较规则、光学性质类似的特点。     

Robust blind motion deblurring using near-infrared flash image

In light-limited situations, camera motion blur is one of the prime causes for poor image quality. Recovering the blur kernel and latent image from the blurred observation is an inherently ill-posed problem. In this paper, we introduce a hand-held multispectral camera to capture a pair of blurred image and Near-InfraRed (NIR) flash image simultaneously and analyze the correlation between the pair of images. To utilize the high-frequency details of the scene captured by the NIR-flash image, we exploit the NIR gradient constraint as a new type of image regularization, and integrate it into a Maximum-A-Posteriori (MAP) problem to iteratively perform the kernel estimation and image restoration. We demonstrate our method on the synthetic and real images with both spatially invariant and spatially varying blur. The experiments strongly support the effectiveness of our method to provide both accurate kernel estimation and superior latent image with more details and fewer ringing artifacts.     

Moving cast shadow detection using online sub-scene shadow modeling and object inner-edges analysis

In this paper, we propose an adaptive and accurate moving cast shadow detection method employing online sub-scene shadow modeling and object inner-edges analysis for applications of static-camera video surveillance. To describe shadow appearance more accurately, the proposed method builds adaptive online shadow models for sub-scenes with different conditions of irradiance and reflectance. The online shadow models are learned by utilizing Gaussian functions to fit the significant peaks of accumulating histograms, which are calculated from Hue, Saturation and Intensity (HSI) difference of moving objects between background and foreground. Additionally, object inner-edges analysis is adopted to reject camouflages, which are misclassified foreground regions that are highly similar to shadows. Finally, the main shadow regions are expanded to recycle the misclassified shadow pixels based on local color constancy. The proposed algorithm can adaptively handle the shadow appearance changes and camouflages without prior information about illuminations and scenarios. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.     

Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.     

适于空间图像闪存阵列的非与闪存控制器

提出一种适于空间应用的非与(NAND,not and)闪存控制器。首先,分析了空间相机存储图像的要求,说明了闪存控制器结构的特点。接着,分析了闪存数据存储差错的机理,针对闪存结构组织特点提出了一种基于BCH(Bose-Chaudhuri-Hocquenghem,2108,2048,5)码的闪存纠错算法。然后,对传统BCH编码器进行了改进,提出了一种8bit并行蝶形阵列处理机制。最后,使用地面检测设备对闪存控制器进行了试验验证。结果表明,闪存控制器能快速稳定、可靠地工作,在闪存单页2Kbt/page下可以纠正40bit错误,在相机正常工作行频为2.5kHz下拍摄图像时4级流水线闪存连续写入速度达到133Mbit/s,可以满足空间相机图像存储系统的应用。     

Object-based change detection method for high-resolution remote sensing image combining shadow compensation and multi-scale fusion

As an interpreting symbol of remote sensing images,shadow,however,brings about “pseudo changes”,which is one of the main sources leading to error detection in high-resolution remote sensing image change detection.For this issue,an object-based high-resolution remote sensing image change detection method was proposed combining with shadow compensation and multi-scale fusion.In the object orientation detection framework,the shadows in the remote sensing images were extracted.Then multi-scale change detection was conducted with shadow compensation.In the process,an objective function was constructed of mutual scale information minimization to realize the adaptive extraction of scale parameters.Based on this,combined with the shadow compensation factor,a multi-scale decision-level fusion strategy built on D-S theory of evidence was designed,and the levels of change intensity were further divided.The experiments show that the method is effective in solving the error detection problem caused by shadow,significantly improving the precision of change detection.     

Correspondence estimation in image pairs

This article provides an overview of current techniques for dense geometric correspondence estimation. We first formally define geometric correspondence and investigate the different types of image pairs. Then, we briefly look at the classic approaches to correspondence estimation, at their feasibility and flaws for simultaneous dense estimation. We focus on the Bayesian approach, which is very well suited for this task, and for which several promising algorithms have previously been developed     

Optical FSK heterodyne detection using image rejection mixer

A new optical FSK heterodyne detection scheme is proposed and demonstrated. This scheme uses an optical image rejection mixer. The intermediate frequency bandwidth is the same as ASK heterodyne detection while equalling dual filter FSK detection receiver sensitivity.<>