基于特征点匹配技术的运动估计及补偿方法

提出一种用于电子稳像技术的全局运动估计和补偿算法。通过特征点匹配,求其局部运动矢量;将其代入变换模型所得的线性力程组,求其全局运动矢量。用均值滤波的方法确定各帧的补偿量,并代入给定的变换模型对当前图像进行变换,实现对视频图像序列的稳定处理。为了减小特征点匹配计算的复杂性,加快匹配速度,还采用了多分辨率图像金字塔匹配策略。实验表明．使用该算法对于提高动态图像的稳定性有较好的效果。     

基于区域协方差的视频显著度局部空时优化模型

显著度检测在计算机视觉中应用非常广泛,图像级的显著度检测研究已较为成熟,但视频显著度因其高度挑战性研究相对较少。该文借鉴图像级显著度算法的思想,提出一种通用的空时特征提取与优化模型来检测视频显著度。首先利用区域协方差矩阵构造视频的空时特征描述子,然后计算对比度得出初始显著图,最后通过联合前后帧的局部空时优化模型得到最终的显著图。在2个公开视频显著性数据集上的实验结果表明,所提算法性能优于目前的主流算法,同时具有良好的扩展性。     

时域积分方程MOT算法的推迟位时间卷积数值积分新方法

通过变量代换平滑三角形上推迟位（标量位函数和矢量位函数）并消除推迟矢量位旋度的奇异性,使得采用数值积分法就能够精确快速地计算任意正则时间基函数与推迟位函数及推迟矢量位旋度之间的时间卷积运算,可用于基于任意类型时间基函数的时域电场、时域磁场及其混合场积分方程时间步进（MOT ）算法。与时间卷积运算的解析法对比分析表明,该时间卷积数值积分方法能够精确快速地计算基于任意类型时间基函数和不同时间步长条件下时域积分方程MOT算法的阻抗矩阵元素;而具体的计算实例也表明,阻抗矩阵的精确计算显著地提升了时域积分方程MOT算法的后时稳定性和求解精度。     

利用小波变换和约束矩阵进行图像压缩编码

本文将小波变换和矢量量化相结合对图像进行压缩编码,分析了图像经小波变换后的数据结构特性以及各小波分量之间的相关性。这种相关性的存在是由于小波变换具有良好的空－频域局部化特性。本文构造了结构约束矩阵来描述这种相关性,并以此为基础对传统的矢量量化算法进行了改进,改进后的算法减小了计算量,降低了码率。     

USB摄像头的条形码识别

以USB摄像头为输入设备,提出一种改进的EAN-13条形码定位和识别算法.首先,视频连续两帧图像进行差分运算,当其绝对值小于一定阈值,捕获当前图像帧,进而用形态学的膨胀和腐蚀算法对捕获到的图像进行条形码区域的定位;然后,用大律法求定位后图像的全局阈值并对其进行二值化;再然后,用所提出的矩形平移的校正方法对倾斜的条形码进行倾斜校正;最后,用平均宽度法计算出条形码条和空的宽度,接着识别条形码并判断是否识别正确.实验结果表明,该算法识别速度快,识别率高.     

基于 SIMD-MCC 的图象块匹配并行算法

图像块匹配操作是图像处理中很多基于窗口任务的典型操作之一。文章提出了一种在SIMD-MCC计算机上实现的全搜索图像块匹配的并行算法,此算法对实时图中的每一个参考块和参考图中搜索区中的候选块进行比较,以确定一个最小的位移矢量。这个位移矢量所对应的位置就是匹配位置。该算法计算复杂度为O(log2N)。     

一种新的JDL-STAP算法的研究

本文提出了一种基于空时导向矢量作为变换阵来实现降维的局域联合处理(JDL)算法.该算法的变换阵由局域化所选取的几列空域、时域导向矢量作直积得到,导向矢量以任意间隔进行选取.这种JDL算法的降维和局域处理方式简单而易实现,相对于基于DFT的JDL算法,它的实现没有任何限制条件,算法性能大约提高4dB.该算法不仅适用于二维空时自适应处理,同样适用于三维空时自适应处理,仿真证明了该算法的可行性和有效性.     

基于SIMD—MCC的图象块匹配并行算法

图像块匹配操作是图像处理中很多基于窗口任务的曲型操作之一。文章提出了一种在ＳＩＭＤ－ＭＣＣ计算机上实现的全扫帚图像块匹配的并行算法。此算法对实时图中的每一个参数图中搜索区中的修选块并行比较,以确定一个最小的位移矢量。这个位移矢量所对应的位置就是匹配位置。该算法计算复杂度为Ｏ（ｌｏｇＮ）。     

一种低复杂度立体视频错误隐藏新方法

针对立体视频流传输中右视 点整帧丢失,提出 了一种低复杂度的错误隐藏算法。首先,为了高效地感知立体视频的时域质量和视点间质量 ,定义了时域相似尺度(TSM)、 视间相似尺度(ISM)的概念;将前一时刻右视点图像进行时域和视点间匹配,分别求取 其以像素为单位的TSM和 ISM映射图;然后,计算前一时刻右视点图像当前宏块的TSM和ISM值,通过比较得 到当前宏块的预测模式;最后,根据视频序列的时域一致性,将前一时刻右视点图像宏块 的预测模式作为丢失图像宏 块的预测模式,从而使用运动补偿预测(MCP)或者视差补偿预测(DCP )的方法恢复丢失信息。研究结果表明,与已有错误隐藏 算法相比,本文算法获得更好主客观视觉效果;同时与基于图像结构相似度(SSIM)的错误隐藏算法相比,在保持主观视觉质量情况下,错误隐藏时间节省20%左右。     

一种三角函数权重的图像拼接算法

针对传统加权平均融合算法和渐入渐出融合算法仍然存在相对明显的拼接痕迹,提出一种三角函数权重的图像拼接算法。首先,对参考图计算图像重叠区域从左边界开始每一列像素所占重叠区的比例,将其用相应的角度表示;然后计算角度对应余弦值的平方,将此结果作为参考图的权重信息;对于目标图,计算靠近右边界的每一列所占重叠区的比例,并用角度表示,然后计算该角度对应正弦值的平方,将此结果作为目标图的权重;最后用计算得到的2个非线性的权重对两幅图进行图像拼接。实验结果表明无论摄像机是否在曝光差异较大情况下进行拍摄,改进的图像融合算法效果更好。     

Compressed domain video saliency detection using global and local spatiotemporal features

A compressed domain video saliency detection algorithm, which employs global and local spatiotemporal (GLST) features, is proposed in this work. We first conduct partial decoding of a compressed video bitstream to obtain motion vectors and DCT coefficients, from which GLST features are extracted. More specifically, we extract the spatial features of rarity, compactness, and center prior from DC coefficients by investigating the global color distribution in a frame. We also extract the spatial feature of texture contrast from AC coefficients to identify regions, whose local textures are distinct from those of neighboring regions. Moreover, we use the temporal features of motion intensity and motion contrast to detect visually important motions. Then, we generate spatial and temporal saliency maps, respectively, by linearly combining the spatial features and the temporal features. Finally, we fuse the two saliency maps into a spatiotemporal saliency map adaptively by comparing the robustness of the spatial features with that of the temporal features. Experimental results demonstrate that the proposed algorithm provides excellent saliency detection performance, while requiring low complexity and thus performing the detection in real-time.     

Spatiotemporal saliency detection and salient region determination for H.264 videos

In this study, a spatiotemporal saliency detection and salient region determination approach for H.264 videos is proposed. After Gaussian filtering in Lab color space, the phase spectrum of Fourier transform is used to generate the spatial saliency map of each video frame. On the other hand, the motion vector fields from each H.264 compressed video bitstream are backward accumulated. After normalization and global motion compensation, the phase spectrum of Fourier transform for the moving parts is used to generate the temporal saliency map of each video frame. Then, the spatial and temporal saliency maps of each video frame are combined to obtain its spatiotemporal saliency map using adaptive fusion. Finally, a modified salient region determination scheme is used to determine salient regions (SRs) of each video frame. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of two comparison approaches.     

Video saliency detection incorporating temporal information in compressed domain

Saliency detection is widely used to pick out relevant parts of a scene as visual attention regions for various image/video applications. Since video is increasingly being captured, moved and stored in compressed form, there is a need for detecting video saliency directly in compressed domain. In this study, a compressed video saliency detection algorithm is proposed based on discrete cosine transformation (DCT) coefficients and motion information within a visual window. Firstly, DCT coefficients and motion information are extracted from H.264 video bitstream without full decoding. Due to a high quantization parameter setting in encoder, skip/intra is easily chosen as the best prediction mode, resulting in a large number of blocks with zero motion vector and no residual existing in video bitstream. To address these problems, the motion vectors of skip/intra coded blocks are calculated by interpolating its surroundings. In addition, a visual window is constructed to enhance the contrast of features and to avoid being affected by encoder. Secondly, after spatial and temporal saliency maps being generated by the normalized entropy, a motion importance factor is imposed to refine the temporal saliency map. Finally, a variance-like fusion method is proposed to dynamically combine these maps to yield the final video saliency map. Experimental results show that the proposed approach significantly outperforms other state-of-the-art video saliency detection models.     

Hedging static saliency models to predict dynamic saliency

In recent years, many computational models for saliency prediction have been introduced. For dynamic scenes, the existing models typically combine different feature maps extracted from spatial and temporal domains either by following generic integration strategies such as averaging or winners take all or using machine learning techniques to set each feature’s importance. Rather than resorting to these fixed feature integration schemes, in this paper, we propose a novel weakly supervised dynamic saliency model called HedgeSal, which is based on a decision-theoretic online learning scheme. Our framework uses two pretrained deep static saliency models as experts to extract individual saliency maps from appearance and motion streams, and then generates the final saliency map by weighted decisions of all these models. As visual characteristics of dynamic scenes constantly vary, the models providing consistently good predictions in the past are automatically assigned higher weights, allowing each expert to adjust itself to the current conditions. We demonstrate the effectiveness of our model on the CRCNS, UCFSports and CITIUS datasets.     

Visual saliency guided video compression algorithm

Recently Saliency maps from input images are used to detect interesting regions in images/videos and focus on processing these salient regions. This paper introduces a novel, macroblock level visual saliency guided video compression algorithm. This is modelled as a 2 step process viz. salient region detection and frame foveation. Visual saliency is modelled as a combination of low level, as well as high level features which become important at the higher-level visual cortex. A relevance vector machine is trained over 3 dimensional feature vectors pertaining to global, local and rarity measures of conspicuity, to yield probabilistic values which form the saliency map. These saliency values are used for non-uniform bit-allocation over video frames. To achieve these goals, we also propose a novel video compression architecture, incorporating saliency, to save tremendous amount of computation. This architecture is based on thresholding of mutual information between successive frames for flagging frames requiring re-computation of saliency, and use of motion vectors for propagation of saliency values.     

Salient object detection using local,global and high contrast graphs

In this paper, we propose a novel multi-graph-based method for salient object detection in natural images. Starting from image decomposition via a superpixel generation algorithm, we utilize color, spatial and background label to calculate edge weight matrix of the graphs. By considering superpixels as the nodes and region similarities as the edge weights, local, global and high contrast graphs are created. Then, an integration technique is applied to form the saliency maps using degree vectors of the graphs. Extensive experiments on three challenging datasets show that the proposed unsupervised method outperforms the several different state-of-the-art unsupervised methods.     

Spatiotemporal segmentation for compact video representation

In this paper, a novel hierarchical object-oriented video segmentation and representation algorithm is proposed. The local variance contrast and the frame difference contrast are jointly exploited for structural spatiotemporal video segmentation because these two visual features can indicate the spatial homogeneity of the grey levels and the temporal coherence of the motion fields efficiently, where the two-dimensional (2D) spatiotemporal entropic technique is further selected for generating the 2D thresholding vectors adaptively according to the variations of the video components. After the region growing and edge simplification procedures, the accurate boundaries among the different video components are further exploited by an intra-block edge extraction procedure. Moreover, the relationships of the video components among frames are exploited by a temporal tracking procedure. This proposed object-oriented spatiotemporal video segmentation algorithm may be useful for MPEG-4 system generating the video object plane (VOP) automatically.     

Saliency-based dense trajectories for action recognition using low-rank matrix decomposition

Dense trajectory methods have recently been proved to be successful in recognizing actions in realistic videos. However, their performance is still limited due to the uniform dense sampling, which does not discriminate between action-related areas and background. This paper proposes to improve the dense trajectories for recognizing actions captured in realistic scenes, especially in the presence of camera motion. Firstly, based on the observation that the motion in action-related areas is usually much more irregular than the camera motion in background, we recover the salient regions in a video by implementing low-rank matrix decomposition on the motion information and use the saliency maps to indicate action-related areas. Considering action-related regions are changeable but continuous with time, we temporally split a video into subvideos and compute the salient regions subvideo by subvideo. In addition, to ensure spatial continuity, we spatially divide a subvideo into patches and arrange the vectorized optical flow of all the spatial patches to collect the motion information for salient region detection. Then, after the saliency maps of all subvideos in a video are obtained, we incorporate them into dense tracking to extract saliency-based dense trajectories to describe actions. To evaluate the performance of the proposed method, we conduct experiments on four benchmark datasets, namely, Hollywood2, YouTube, HMDB51 and UCF101, and show that the performance of our method is competitive with the state of the art.     

Synergizing spatial and temporal texture

Temporal texture accounts for a large proportion of motion commonly experienced in the visual world. Current temporal texture techniques extract primarily motion-based features for recognition. We propose a representation where both the spatial and the temporal aspects of texture are coupled together. Such a representation has the advantages of improving efficiency as well as retaining both spatial and temporal semantics. Flow measurements form the basis of our representation. The magnitudes and directions of the normal flow are mapped as spatiotemporal textures. These textures are then aggregated over time and are subsequently analyzed by classical texture analysis tools. Such aggregation traces the history of a motion which can be useful in the understanding of motion types. By providing a spatiotemporal analysis, our approach gains several advantages over previous implementations. The strength of our approach was demonstrated in a series of experiments, including classification and comparisons with other algorithms.