CartoonModes: Cartoon stylization of video objects through modal analysis

We transform the motion and shape of a video object into cartoon style using a set of representative cartooning deformations which we relate to a modal analysis of the object. Results can be obtained at interactive rate and a user survey confirmed their plausibility.     

An adaptive cartoon-like stylization for color video in real time

Multimedia Tools and Applications - The cartoon-like stylization of color video images is one of the major types of non-photorealistic rendering. It has broad applications in the entertainment...     

基于改进的tMHI的实时视频运动目标检测及跟踪

提出了一种对视频图像进行实时目标分割及跟踪的新方法。该方法利用基于时间片的运动历史图像（tMHI）的灰度阶梯轮廓,对存在的子运动区域进行包围划分并予以标记,实现视频图像中运动目标的实时分割,进而将每帧tMHI图像中各个运动区域同场景中运动目标连续关联起来,实现对多运动目标的轨迹跟踪。为了提高分割质量,对tMHI进行了改进处理,去除了大部分噪声干扰,取得了明显的改善效果。实验表明,该方法可以有效地分割并跟踪视频中的多个运动目标,鲁棒性好,检出率较高,并且处理速度较快,达到了实时性的要求,还解决了局部粘连的问题。     

基于粒子视频的高密度人群主流运动检测

采用粒子视频流获得视频序列中的特征点运动轨迹,并对获得的运动轨迹进行提取,然后利用最长共同子序列LCS(Longest Common Subsequence)聚类轨迹,得到运动的主流方向。该算法可以有效检测实际场景中的主流运动方向。     

Free form deformation based face cartoon stylization

A novel free form based face cartoon stylization method is presented in this paper. First, a face cartoon library with marked feature points is constructed. And then select the input image as the target image and an appropriate cartoon image from cartoon library as the reference image, apply the deformation between the corresponding feature points of the images to the target image. Finally, we apply an image stylization process to the result image. As an alternative method, we also choose an appropriate cartoon image as the target image and the input image as the reference image to apply the free form deformation. The experimental results show that our method is straightforward and quick with diversified styles, delivering more infection.     

基于深度学习的图像风格化算法研究综述

随着人工智能时代的到来,深度学习一词也逐渐走进大众的视野,一些基于深度学习神经网络的图像处理方法也随之产生,图像风格化作为其中一个重要的分支也获得了广泛的关注。目前,研究学者提出了很多基于深度学习的图像风格化算法,而且都能较好地完成风格化任务。全面概述了深度学习在图像风格化领域的进展,对比了不同算法之间的优劣,最后探讨了当前基于深度学习的图像风格化研究的局限性及未来的研究方向。     

运动像机下基于粒子滤波的多目标跟踪算法

针对摄像机运动情况下的多目标跟踪问题,提出了基于粒子滤波的跟踪算法.在粒子滤波算法基础上,将二阶自回归过程作为系统状态转移模型,HSV颜色直方图作为观测模型,对视频中多个目标的位置、大小进行跟踪.实验结果表明,该算法能实时正确地跟踪多个目标,并对局部遮挡有较好的鲁棒性,也能在目标短暂消失导致跟踪失败后,在目标重新出现后及时捕获并继续进行跟踪.     

Tracking video objects with feature points based particle filtering

For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.     

Dendritic stylization

Dendritic or branching structures are commonly seen in natural phenomena such as lightning, cracks, and vegetal growth. They are also often used for artistic or decorative purposes. We present a new procedural method for modeling dendritic structures based on a path planning approach. Our method includes the use of a partial non-scalar distance metric that gives us powerful and responsive control over the evolving dendritic structure. We demonstrate the effectiveness of our approach by creating dendritic stylizations of input images. We also show how our approach can be used to model more complex dendritic structures, such as trees; our algorithm allows us to create pareidolia effects, where an image is embedded within the branches of the tree.     

基于滚动时域粒子群优化的视频去雾算法

无人机视频由于拍摄的位置和场景不断移动,环境参数亦不断变化,采用以往针对固定场景的去雾方法不能达到最佳效果.为了使无人机视频去雾算法具有自适应性,提出一种基于滚动时域粒子群优化的视频去雾算法.将基于周期和事件混合驱动的滚动调度策略与粒子群算法(PSO)相结合,对可调去雾参数进行滚动优化调整,当与上次优化的帧间隔数大于阈...     

基于视频粒子流和FTLE场的人群运动分割算法

针对复杂视频监控场景中不同运动行为的人群分割,提出了将视频粒子流和有限时间李雅普诺夫指数(FTLE)场相结合的人群运动分割算法。首先利用视频粒子流来表示长周期的粒子运动估计,通过最小化包含粒子外观匹配一致性和粒子间形变的能量函数,来优化每个粒子的轨迹;接着求解粒子流图的空间梯度,并构造FTLE场;最后利用FTLE场中的拉格朗日相干结构把流图分割成运动特性不同的区域。实验结构表明,算法能从拥挤复杂的视频监控场景中有效地分割出不同运动特性的群体,且具有较好的鲁棒性。     

Interactive time-dependent particle tracing using tetrahedraldecomposition

Streak lines and particle traces are effective visualization techniques for studying unsteady fluid flows. For real time applications, accuracy is often sacrificed to achieve interactive frame rates. Physical space particle tracing algorithms produce the most accurate results although they are usually too expensive for interactive applications. An efficient physical space algorithm is presented which was developed for interactive investigation and visualization of large, unsteady, aeronautical simulations. Performance has been increased by applying tetrahedral decomposition to speed up point location and velocity interpolation in curvilinear grids. Preliminary results from batch computations showed that this approach was up to six times faster than the most common algorithm which uses the Newton-Raphson method and trilinear interpolation. Results presented show that the tetrahedral approach also permits interactive computation and visualization of unsteady particle traces. Statistics are given for frame rates and computation times on single and multiprocessors. The benefits of interactive feature detection in unsteady flows are also demonstrated     

综合颜色和小波纹理特征的免疫粒子滤波视觉跟踪

针对标准粒子滤波视觉跟踪时的缺陷,尤其是其“样贫”和目标特征不明显等问题,提出了结合小波纹理特征的免疫粒子滤波算法.小波纹理特征的加入,使得单纯依靠颜色特征不能很好适应环境变化的情况得到了改善.同时通过加入免疫优化算法,提高了粒子的多样性,尤其是在发生遮挡时减少了“样贫”的影响.通过实验验证了所提算法的有效性和鲁棒性....     

Quality enhancement for scalable video delivery over wireless networks based on particle swarm optimization

In this paper, the main objective is to find an optimal rate allocation strategy that can maximize the total-weighted quality of experience (QoE) associated with multiple video sources over error-prone multi-hop wireless networks based on the particle swarm optimization (PSO) technique which belongs to the family of swarm intelligence algorithms. In video transmission over such wireless networks, many network-based (packet loss, delay etc.) and source-based (encoding quantization level etc.) parameters can impair the perceived video quality. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed based on PSO in which by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized. Second, these optimal rates have been used for differentiated QoE enforcement between multiple competing scalable video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate scalable video encoder such as H.264/MPEG4 AVC. The aforementioned weight parameters are selected based on the importance of each video sequence’s quality and can be associated with some previous service level agreement-based prices. A strong motivation for differentiated quality enforcement is that video sources need to be encoded differently for different resolutions to cater to diverse devices from mobile displays to HDTV displays. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.     

Information tracing model based on PageRank

In social network,original publisher and important nodes in the diffusion process can be traced by analyzing the spreading network of a hot topic.The participated users and spreading network structure of a hot topic build an information tracing model,which mines the source and important diffusion nodes.Firstly,it analyzed the development trend of a hot topic and extracts the users involved.Secondly,it established a user network according to the following relationship of the users involved.Thirdly,the contribution rate of users on the development of the hot topic was initialized,and the PageRank algorithm was used to construct the information tracing model.Finally,the Top k users were selected as the information publisher and important users of the hot topic according to the contribution rate.Experimental results showed that our model can effectively discover the hot topic of the publisher and important users.     

基于上下文表示的知识追踪方法

知识追踪是教育数据挖掘领域中一个十分重要的问题,旨在利用可观测到的学生历史交互数据和习题包含的知识点相关信息来推断学生对知识点的掌握情况。已有方法虽在不同程度上取得了一些进展,但大多忽略了使用知识点表示习题的重要性,并且对使用诸如学习因素之类的上下文表示知识点的研究也不够充分。针对上述问题,提出基于上下文表示的知识追踪方法KTCR。首先,综合考虑影响学生学习过程的因素,并基于学生响应数据设计了知识点上下文表示方法,从而基于Q矩阵表示知识点上下文;其次,为了实现习题向量的降维,利用融合上下文信息的知识点和学生响应数据对习题向量进行重表示;最后,结合学生历史交互数据,使用长短期记忆网络对学生的知识状态进行估计。在4个真实数据集上的实验表明了本文方法对于习题嵌入表示的合理性,并且能够有效地估计学生的知识状态。     

Efficient video encryption scheme based on advanced video coding

A video encryption scheme combining with advanced video coding (AVC) is presented and analyzed in this paper, which is different from the ones used in MPEG1/2 video encryption. In the proposed scheme, the intra-prediction mode and motion vector difference are encrypted with the length-kept encryption algorithm (LKE) in order to keep the format compliance, and the residue data of the macroblocks are encrypted with the residue data encryption algorithm (RDE) in order to keep low cost. Additionally, a key distribution scheme is proposed to keep the robustness to transmission errors, which assigns sub-keys to different frames or slices independently. The encryption scheme’s security, time efficiency and error robustness are analyzed in detail. Experimental results show that the encryption scheme keeps file format unchanged, is secure against replacement attacks, is efficient in computing, and is robust to some transmission errors. These properties make it a suitable choice for real-time applications, such as secure IPTV, secure videoconference or mobile/wireless multimedia, etc.

Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search

The aim of this paper is to propose an evolutionary particle filter based upon improved cuckoo search algorithm which will overcome the sample impoverishment problem of generic particle filter. In our proposed method, improved cuckoo search (ICS) algorithm is embedded into particle filter (PF) framework. Improved cuckoo search algorithm uses levy flight for generating new particles in the solution and introduced randomness in samples by abandoning a fraction of these particles. The second important contribution in this article is introduction of new way for tackling scaling and rotational error in object tracking. Performance of proposed improved cuckoo particle filter is investigated and evaluated on synthetic and standard video sequences and compared with the generic particle filter and particle swarm optimization based particle filter. We show that object tracking using improved cuckoo particle filter provides more reliable and efficient tracking results than generic particle filter and PSO-particle filter. The proposed technique works for real time video objects tracking.     

基于视频的角色动画

现有的计算机动画制作普遍存在投资大、效率不高等缺陷.为此,提出了利用计算机视觉技术中的一种基于特征的跟踪方法-KL(Kanade-Lucas)算法.首先捕获视频中角色的二维运动信息,然后进行三维重建生成满足动画要求的三维角色运动序列,最后将三维角色运动序列重定向到动画模型中去,从而得到逼真的角色动画.实际运行结果表明,从来源广、成本低的视频中完成角色运动提取与合成,从而生成逼真动画的可行性.