基于Alpha算法的视频合成

介绍了DirectShow技术,提出基于PC机和DirectShow技术的视频抠像及合成系统;其次对视频合成技术进行了研究,通过分析比较选取Alpha抠像算法.     

After Effects4.1软件使用技巧系列讲座第二讲 动态抠像方法

在上一讲,我们先介绍了抠像的基本概念,并且介绍了一种复杂抠像的实现.本讲我们将首先介绍实现抠像的基本方法,然后详细地介绍动态抠像的实现.下面首先介绍在动态抠像方法 Effects软件中实现动态抠像的基本方法,最后通过动态抠像实例来介绍实现动态抠像的过程.     

遮挡情况下的数字图像修补技术研究

在遮挡情况下,首先将抠像技术应用到目标物的提取过程中,然后使用图像分割的方法实现修补的目的。在基于全自动前景目标提取算法的基础上对抠像技术作了改进,移除遮挡物,提取出背景图像以便于进行修补工作。然后提出了基于模糊C均值聚类结合边缘检测的图像修补方法。实验结果表明,该算法能较好地修补受损区域。     

影视特效制作中抠像技术的应用研究

针对影视特效中常用的抠像技术,从理论到实践分别进行了深入研究.通过对抠像流程每一个环节的技术分析,将影响抠像效果的因素分别进行定位和研究,为实现精细抠像提供了可靠的流程和方法.     

抠像技术研究及其在视频编辑中的应用

本文针对影视特效中的抠像技术,从理论到实践进行了深入研究。通过premiere、Sobey-cy21、Photoshop三种软件对抠像流程每一个环节的技术分析,将影响抠像效果的因素分别进行定位和研究,为实现精细抠像提供了可靠的方法。     

抠像技术研究及在视频编辑中的应用

本文针对特效中的抠像技术,从理论到实践进行了深入研究。通过premiere、Sobey-cy21、Photoshop三种软件对抠像流程每一个环节的技术分析,将影响抠像效果的因素分别进行定位和研究,为实现精细抠像提供了可靠的方法。     

基于FPGA的数字色键器的设计与实现

在数字电视节目制作中,抠像是一种应用广泛的电视特技,它能将人们置身于各种奇妙的环境中,极大丰富了电视节目的表现形式.从抠像的基本原理入手,通过改进抠像算法,设计了一款基于FPGA的数字色键器,并在Quanus Ⅱ中对设计进行了仿真验证,最后通过硬件测试证明了系统设计方法的正确性和可靠性.该色键器能较好地应用于日常的电视节目制作过程中.     

复杂背景下基于分割逼近法的抠像技术研究

论述了复杂背景下人物的抠像问题,提出一种新的抠像方法--分割逼近算法.首先使用背景更新模型建立了差分背景,然后用本文提出的分割逼近法对图像进行粗分和细分两次分割,建立了人物图像边缘连通体,最后运用Laplacian算子对勾勒出的人物轮廓提取边缘,并用替换原图像的方法实现人物的抠像.     

敦煌DX蓝幕抠像的演播室应用

本文主要讲述的是蓝幕抠像的基本步骤与一个实例的讲解。通过本文可以了解到,在前期需要将准备工作做好,减少后期的工作量。成功拍摄出素材后,利用敦煌DX,将抠像的流程融合在＂超级色键＂这个节点里。通过基础色键,将大面积的背景色除去;通过抠像补丁修补色键的取样容差,细化基础色键;通过清理Alpha通道来得出更清晰更合理的Alp...     

Primatte的运算法则

说起Imagica公司鼎鼎大名的数字色键抠像软件Primatte,相信大家都很熟悉,并且为Primatte强大的抠像功能和精准的抠像效果而津津乐道.今天探讨的不是它的操作方法,而是Primatte是如何工作的,也就是它的运算法则是怎样的.     

Unsupervised and reliable image matting based on modified spectral matting

Spectral matting is the state-of-the-art image matting and also a milestone in theoretic matting research. For spectral matting without user intervention, the accuracy of alpha matte is low and the computational cost is high. Therefore, this paper presents a modified version of spectral matting to greatly increase the accuracy of alpha matte and effectively reduce the computational cost. In the proposed modified spectral matting, palette-based component classification is used to obtain reliable foreground and background components. Next, the corresponding matting components are obtained via a linear transformation of the smallest eigenvectors of the matting Laplacian matrix. Finally, the matting components of the foreground and the unknown regions are combined to from the complete alpha matte based on minimizing the matte cost. Moreover, image composition with consistency of color temperature is used to obtain the realistic image composition. Experimental results show that the proposed method outperforms the state-of-the-art methods based on spectral matting.     

最小移动二乘抠图

交互式抠图技术在有限的用户交互下抠取图像的前景,被广泛的应用在图像及视频编辑、三维重建等领域中,有极高的应用价值。 近年来的抠图技术中,拉氏矩阵给出alpha图上像素间的线性关系,对alpha图的估计起到了重要作用。本文提出了一种改进拉普拉斯矩阵的方法,使用移动最小二乘法替代最小二乘法,结合最近邻（KNN）方法给出移动拉氏矩阵,并使用移动拉氏矩阵计算alpha图。实验结果证明了移动拉氏矩阵的有效性。     

Gaussian-Hermite moment-based depth estimation from single still image for stereo vision

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues.     

Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.     

单幅雾天图像复原

 本文提出了一种单幅雾天图像分割复原的方法,首先利用黑体原理估计粗糙的大气光传输图,然后使用拉普拉斯修补矩阵对分割之后的传输图进行修补,当场景目标和大气光很类似时,采用置信传播推断算法纠正传输图,经过修补和推断之后的传输图能够准确反映光线通过雾的传输过程,结合雾天图像光学模型,从雾的物理特性上去除雾对图像的影响.实验结果表明,本文提出的雾天图像分割复原算法能够有效恢复出清晰图像,并能获取相应雾天图像深度信息.     

Iterative transductive learning for automatic image segmentation and matting with RGB-D data

In this paper, we propose a fully automatic image segmentation and matting approach with RGB-Depth (RGB-D) data based on iterative transductive learning. The algorithm consists of two key elements: robust hard segmentation for trimap generation, and iterative transductive learning based image matting. The hard segmentation step is formulated as a Maximum A Posterior (MAP) estimation problem, where we iteratively perform depth refinement and bi-layer classification to achieve optimal results. For image matting, we propose a transductive learning algorithm that iteratively adjusts the weights between the objective function and the constraints, overcoming common issues such as over-smoothness in existing methods. In addition, we present a new way to form the Laplacian matrix in transductive learning by ranking similarities of neighboring pixels, which is essential to efficient and accurate matting. Extensive experimental results are reported to demonstrate the state-of-the-art performance of our method both subjectively and quantitatively.     

Volatilization of bismuth in copper matte converting — computer simulation

A computer model has been developed to simulate the behavior of bismuth in copper matte converting at 1100 to 1300 ‡C. The rate equation is integrated numerically by dividing a continuous process of matte converting into a great number of microsteps, in each of which the volatilization of Bi-bearing gases is thermodynamically calculated by assuming a steady state. The bubbles of offgas consisting of SO₂ and N₂ are assumed to be saturated with the vapors of BiS, Bi, BiO, and Bi₂. However, the partial pressures of BiO and Bi₂ are found to remain negligible at all stages of converting. BiS is the most volatile species over the slag-making stage with low grade mattes, but its volatility decreases markedly, becoming negligibly low over white metal. When the copper content of the initial matte is known together with the weight of matte, converting temperature and blowing rate of tuyere air, the present computer model can predict the Bi contents in all the phases involved (gas, slag, matte, copper) at any given time. The predictions by the present computer model are compared with the known commercial data from various smelters around the world. The agreements between the computer predictions and the commercial data are excellent in all cases, so that the present computer model can be used to monitor and optimize the bismuth elimination in the actual industrial operations of copper matte converting. Formerly Associate Professor, Department of Metallurgical Engineering, University of Utah, Salt Lake City, UT 84112     

Activities of CoS and FeS in copper mattes and the behavior of cobalt in copper smelting

The distributions of cobalt and iron between metallic copper and high copper mattes were measured at 1400 and 1500 K. A value of 0.40 ±0.02 was found as the Raoultian activity coefficient of CoS at infinite dilution in the Cu₂S-FeS-CoS mattes. The present activities of FeS in the Cu-saturated Cu₂S-FeS mattes were found to deviate more positively than those reported by Krivsky and Schuhmann at 1623 K, and the positive deviation from the Temkin's ideality was greater at 1400 K than at 1500 K. Using the activity coefficient of CoS, the partitions of cobalt between copper mattes and fayalitic slags were calculated for various conditions of copper smelting. It was found that cobalt exhibits, in the matte-slag equilibria, chemical properties intermediate between nickel and iron, but much closer to iron than to nickel. The overall recovery of cobalt in blister copper depends on matte grade, and is as low as 3 pct at best. When a high cobalt recovery is desired, therefore, a copper concentrate rich in cobalt must not be processed by conventional pyrometallurgical technology in view of the inevitably high loss to slag. Formerly Associate Professor, Department of Metallurgical Engineering, University of Utah, Salt Lake City, Utah