Video object segmentation in rainy situations based on difference scheme with object structure and color analysis

A scheme based on a difference scheme using object structures and color analysis is proposed for video object segmentation in rainy situations. Since shadows and color reflections on the wet ground pose problems for conventional video object segmentation, the proposed method combines the background construction-based video object segmentation and the foreground extraction-based video object segmentation where pixels in both the foreground and background from a video sequence are separated using histogram-based change detection from which the background can be constructed and detection of the initial moving object masks based on a frame difference mask and a background subtraction mask can be further used to obtain coarse object regions. Shadow regions and color-reflection regions on the wet ground are removed from the initial moving object masks via a diamond window mask and color analysis of the moving object. Finally, the boundary of the moving object is refined using connected component labeling and morphological operations. Experimental results show that the proposed method performs well for video object segmentation in rainy situations.     

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.     

Detecting moving object from dynamic background video sequences via simulating heat conduction

Moving object detection is one of the essential tasks for surveillance video analysis. The dynamic background often composed by waving trees, rippling water or fountains, etc. in nature scene greatly interferes with the detection of moving objects in the form of noise. In this paper, a method simulating heat conduction is proposed to extract moving objects from dynamic background video sequences. Based on the visual background extractor (ViBe) with an adaptable distance threshold, we design a temperature field relying on the generated mask image to distinguish between the moving objects and the noise caused by dynamic background. In temperature field, a brighter pixel is associated with more energy. It will transfer a certain amount of energy to its neighboring darker pixels. Through multiple steps of energy transfer the noise regions loss more energy so that they become darker than the detected moving objects. After heat conduction, K-Means algorithm with the customized initial clustering centers is utilized to separate the moving objects from background. We test our method on many videos with dynamic background from public datasets. The results show that the proposed method is feasible and effective for moving object detection from dynamic background sequences.     

Joint moving cast shadows segmentation and light source detection in video sequences

This paper proposes a new method which allows a joint estimation of the light source projection on the image plane and the segmentation of moving cast shadows in natural video sequences. It allows improving the segmentation of moving objects by separating clearly cast shadows from moving objects. The method is based on a shadow model which mainly assumes that the cast shadows are projected on plane and Lambertian surfaces, and that the light source is unique. The moving cast shadows, including the penumbra, are detected using a segmentation method based on a comparison between a reference image and the original one. The light source position is estimated using geometrical relations linking the light source, the object and its cast shadow on the 2-D image plane. This is obtained using a robust temporal filtering method. For each image using the current estimation of the light source position and the video object contours, a cast shadow search area is defined. This reduces the risk of false detections during the segmentation process, and thus allows increasing the detection rate and reducing the false alarm one. Experimental results show that good shadow and object contours and light source locations are obtained with the proposed method even if the theoretical assumptions are not fully valid.     

基于YC_bC_r颜色空间的背景建模及运动目标检测

高斯混合模型广泛应用于基于背景建模的运动目标检测中。首先在YCbCr颜色空间采用自适应高斯混合模型对背景的每个像素建模;然后,对输入的当前帧图像的每一像素值与该像素点对应的高斯混合背景模型的各个高斯模型进行比较,将前景运动区域（包括运动目标、投射阴影）从场景中提取出来;最后,采用局部二元图（Lo-cal Binary Pattern,LBP）来提取纹理特征,利用背景在阴影覆盖前后的纹理相似性去除投射阴影,同时结合阴影的空间几何特性优化运动目标检测结果。实验结果表明,该算法能有效地检测出投射阴影和运动目标,具有较高的实际应用价值。     

多信息融合的视频运动目标阴影去除算法

针对传统HSV空间阴影去除模型中阈值难以确定、计算复杂及检测效率较低等问题,在对传统运动目标阴影去除算法进行深入研究的基础上,首先融入一阶梯度信息对传统HSV空间阴影去除模型的不足之处进行针对性改进,然后在此基础上融入反射比不变量提出了一种多信息融合的视频运动目标阴影去除算法.该算法在改进HSV空间阴影去除算法的基础上,进一步引入阴影候选像素及其对应背景区域像素的反射比不变特性来实现阴影区域更为精确的检测,从而有效区分并去除运动目标的阴影像素.实验结果表明,该算法在实际应用中具有较高的有效性和通用性.     

一种改进的基于纹理和颜色的运动阴影检测

精确地消除活动阴影对运动目标的影响是智能视频监控的核心任务之一,针对当前运动阴影检测中采用的纹理信息过于粗糙、阈值选取需要人工干涉等问题,通过对NCC(归一化互相关)纹理算法进行改进,并结合亮度和归一化颜色特性,提出一种自适应的运动阴影检测方法。以混合高斯模型得到的前景像素为基础,通过阴影在亮度和归一化颜色的特性筛选出候选的阴影区域,结合改进的纹理算法进一步缩小阴影区域范围,最后利用空间后处理得到真实阴影。实验结果表明,该算法在有效降低噪声干扰的情况下能够较好区分局部纹理不明显的运动目标和阴影。     

Adaptive Pixel Interpolation for Spatial Error Concealment

Error concealment techniques are widely used as efficient ways to recover the lost information at the decoder. This paper proposes an adaptive pixel interpolation technique for spatial error concealment in the block based coding system. For a missing pixel in a corrupted block, its value is derived from four neighborhoods of the block through interpolation using multiple prediction strategy. The weighting rules of these four neighborhood blocks are carefully designed with regard to three factors, the distance to the missing pixels within the given corrupted block, the percentage of uncorrupted pixels, and the similarity to the given corrupted block. The proposed method works effectively in consecutive block loss situation, which is common in real applications of video transmission. Experimental results show the proposed technique gains more accurate recovery of the missing pixels than the existing schemes.     

Layered moving-object segmentation for stereoscopic video using motion and depth information

A novel layered stereoscopic moving-object segmentation method is proposed in this paper by exploiting both motion information and depth information to extract moving objects for each depth layer with high accuracy on their shape boundary. By taking a higher-order statistics on two frame-difference fields across three adjacent frames, the computed motion information are used to conduct change detection and generate one motion mask that consists of all the moving objects from all the depth layers involved at each view. It would be highly desirable, and challenging, to further differentiate them according to their residing depth layer to achieve layered segmentation. For that, multiple depth-layer masks are generated using our proposed disparity estimation method, one for each depth layer. By intersecting the motion mask and one depth-layer mask at any given layer-of-interest, the moving objects associated with the corresponding layer are then extracted. All the above-mentioned processes are repeatedly performed along the video sequence with a sliding window of three frames at a time. For demonstration, only the foreground and the background layers are considered in this paper, while the proposed method is generic and can be straightforwardly extended to more layers, once the corresponding depth-layer masks are made available. Experimental results have shown that the proposed layered moving-object segmentation method is able to segment the foreground and background moving objects separately, with high accuracy on their shape boundary. In addition, the required computational load is considered fairly inexpensive, since our design methodology is to generate masks and perform intersections for extracting the moving objects for each depth layer.     

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods.     

一种新型可重复利用荫罩掩膜技术

利用化学气相沉积、光刻和反应离子刻蚀等微细加工技术,可以制作柔性、低成本、结实耐用、生物科技和半导体技术兼容、可重复使用的聚对二甲苯-C荫罩掩膜。利用这种透明的惰性材料掩膜,可以制作出传统平面微加工技术难以实现的曲面微图形,同时在多次使用场合,可以实现其它柔性荫罩掩膜材料难以达到的微米级特征尺寸,并能保持很好的图形复制特性。     

A focus mask for color picture tubes using dipolar and quadrupolar lensing

Post-deflection focus masks can increase the electron-beam transmission in a color picture tube over that achievable by conventional shadow masks. Various focus mask structures are considered here and most attention is given to one in which the electrostatic fields in each mask aperture consist of a dipole and a quadrupole component. A theory of the combination of dipolar deflection and quadrupolar focusing is given. Experiments on small masks are described and their quantitative behavior is evaluated. A demountable focus-mask color tube is described which has a 10-in diagonal flat mask. This has been used to display video and the 50-percent transmission of the focus mask has allowed high brightness operation with screen voltage of only 10 kV.     

An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land

An operational scheme for masking cloud-contaminated pixels in Advanced Very High Resolution Radiometer (AVHRR) daytime data over land is developed, evaluated, and presented. Dynamic thresholding is used with channel 1 reflectance data, channel 3 minus channel 4 temperature difference data, and channel 4 minus channel 5 temperature difference data to automatically create a cloud mask for a single image. The dynamic thresholds can be applied in two different ways: to each pixel individually and to classes of pixels determined by an unsupervised minimum Euclidian distance classifier. The dynamic threshold cloud-masking (DTCM) algorithm presented in this study is used to produce cloud masks based on three different configurations: two channels and individual pixels, three channels and individual pixels, and three channels and classes of pixels. These cloud masks are compared with control masks that were created by visual inspection. The results from the clouds from AVHRR (CLAVR) algorithm and the cloud and surface parameter retrieval (CASPR) algorithm are also compared with the control masks. The results of the comparisons indicate that DTCM, applied on a pixel-by-pixel basis, correctly identifies more clear pixels than CASPR or CLAVR while correctly identifying a comparable or higher number of cloud-contaminated pixels.     

A real-time visual postprocessor for MPEG-coded video sequences

A novel content-adaptive enhancement filter is described, which aims at reducing compression artifacts in MPEG-coded video streams. The filter locally selects the most appropriate kernel among a set of pre-defined masks, based upon a classification of the pixels to be processed. The features used in the classification phase take into account the distribution of transform coefficients and the presence of nearby contour pixels, previously detected by an edge extractor. An important aspect of the proposed approach is the low computational complexity, very appealing in the scenario of a typical low-cost consumer application (video communication over the Internet, set-top-box DVB receiver, etc.). Experimental results show that the proposed algorithm outperforms existing approaches with similar level of complexity.     

一种基于纹理的车辆阴影消除新算法

基于对智能交通系统(ITS,Intelligent Transport Systems)中视频检测的研究和分析,特别针对其中关键步骤之一的阴影消除展开深入探讨,分析了阴影产生的原理和特点,阐述了现有的阴影去除算法,在现有算法的基础上提出了一种基于区域聚类的阴影消除算法.实践证明,该方法能够较好的去除运动车辆的阴影,保留...     

融合局部三值数量和色度信息的均值漂移跟踪

该文提出了局部三值数量(Local Ternary Number, LTN)这一新的局部显著性纹理算子,并将其与色度信息相结合得到一种新的目标跟踪方法。该方法充分利用目标像素与其八邻域像素灰度值的大小关系,将局部显著性算子 (Local Similarity Number, LSN)加以拓展,设计了局部三值数量这一新的局部显著性纹理算子,该算子能区分目标像素在同一局部显著度下的不同纹理结构;LTN掩膜提取边缘、线和角点上关键像素以提高纹理特征的区分能力,同时能够较完整地保留目标信息;在此基础上,将掩膜内目标像素的LTN特征与色度信息融合生成一种新的目标模型,并嵌入到均值漂移(Mean Shift, MS)框架完成目标的跟踪。实验结果表明,该文提出的目标跟踪方法在场景中存在相似颜色和光照变化干扰的情况下,仍能持续准确地实现目标的定位,提高了传统均值漂移跟踪算法的性能。     

A fast white balance algorithm based on pixel greyness

The goal of automatic white balance (AWB) is to maintain colour constancy of an image by removing colour cast caused by un-canonical illuminant. In this paper, we address two limitations associated with a class of AWB algorithms and propose a technique to estimate the illuminant which takes into consideration the internal illumination and all pixels of the image. The estimate is calculated by a weighted average of all pixels. The weight for a pixel is determined from the greyness. The greyness of a pixel is measured from its chroma Cb and Cr in the YCbCr colour space. The experimental results demonstrate that performance of the proposed technique is competitive with that of state-of-the-art AWB algorithms. The proposed algorithm can be implemented in real-time applications, such as in consumer digital cameras due to its low computational complexity.     

Contour extraction in medical images using initial boundary pixel selection and segmental contour following

In today??s health care, an imaging system plays an important role throughout the entire clinical process from diagnosis and treatment planning to surgical procedures and follow-up studies of disease. Boundary detection is a technique used to segment an object within a region of interest in the medical image for further clinical applications. Contour extraction is one of the most important boundary detection methods. In this paper, an object contour extraction for gray-level medical images using automatic initial boundary pixel selection and tracing of a segmental contour based on the boundary pixels obtained by the initial boundary pixel selection is proposed. Experimental results on artificial images of convex and deep concave objects, and real CT and MRI images show that, in comparing with other existing methods, a more detailed and accurate contour can be obtained using the proposed object contour extraction method. This has low computational complexity, which will benefit applications to clinical diagnosis, treatment, surgery, and follow up studies.     

基于改进单模高斯模型的运动目标检测算法

在视频交通车辆目标检测中,阴影问题是影响其检测准确性的关键问题之一.为了解决这个问题,提出了一种结合单模高斯模型和背景差法的运动目标阴影检测方法.首先针对传统单模高斯模型提出了一种自适应学习率和选择性差值更新背景相结合的方法,加快了背景模型的初始化速度,同时结合背景差法对阴影部分进行检测与去除.实验结果表明,该方法能够较好地去除车辆的阴影,提高了检测的准确性.     

以多目标优化的轮廓定位分割视频对象

该文提出一种用于半自动分割视频对象的对象跟踪方法,它以方块均方色差和图像像素边缘强度构造目标函数,把对象轮廓点的运动估值和补偿表达为用带容错的分层排序法实现多目标优化,从而确定与参考帧对象轮廓点对应的当前帧像素为轮廓点;以边缘强度最大为目标将这些轮廓点连接成闭合曲线,从而得到当前帧的对象分割掩膜,用这种方法处理多个视频测试序列得到满意结果。