光学期刊联合编辑部成功参展中国国际光电周

第十一届ILOPE展会于2006年12月5-7日在北京召开,此次展会包含了一个光电展览,四个技术/产业论坛和一个中外贸易交流会.吸引了来自14个国家近400家参展商,展出面积达1万多平米.国内主流的激光、光学和红外厂商悉数到场,同时国外FLIR、光谱物理、相干、镭宝、贰陆、HGH、Senko、美国Photonics、尼康等国际知名企业也携新产品、新技术参与.     

Graph Laplacian Matrix Learning from Smooth Time-Vertex Signal

In this paper,we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as“time-vertex signal”.To realize this,we first represent the signals on a joint graph which is the Cartesian product graph of the time-and vertex-graphs.By assuming the signals follow a Gaussian prior distribution on the joint graph,a meaningful representation that promotes the smoothness property of the joint graph signal is derived.Furthermore,by decoupling the joint graph,the graph learning framework is formulated as a joint optimization problem which includes signal denoising,timeand vertex-graphs learning together.Specifically,two algorithms are proposed to solve the optimization problem,where the discrete second-order difference operator with reversed sign(DSODO)in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm,and the time-graph,as well as the vertex-graph,is estimated by the other algorithm.Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time-and vertex-graphs from noisy and incomplete data.     

8848从顶峰走向衰微的历史见证

人不可能两次迈进同一条河流，而8848却似乎可以。近段时间，因为速达的“收购传闻”，8848再一次成为业界关注的焦点。因为如今的8848内部风雨飘摇，外部官司缠身，它有何被收购的价值?公司总裁吕春维为何早早投怀送抱、急于为收购造势?谁是这一出“收购闹剧”的幕后主使?在诸多疑问背后，很少有人能看清事情本质。老8848的创始人王峻涛知道，但他不说，因为此8848已非当年之彼8848。几年前，盛极一时的老8848就是这样被幕后资本强行拉入分拆和兼并的资本游戏，被胜利冲昏头脑的大股东妄图运用资本力量主导企业发展，对他们来说，更重要的是上市圈钱。但因为牵扯的利益太多，诱惑太多，8848的发展最终陷入极度迷茫中，在资本疯狂追逐最大利益的时候，企业却错过了发展的最佳时机，并从此一步步走向衰微。6年来，8848经历了最辉煌的巅峰时期，同样也经历了无数是是非非。即使当时剩下的钱仍可使8848运营数十年，但是，商人们已经对8848的未来失去了耐心与信心，老8848最终难逃遭清算厄运。后来，所谓“王者归来”的新8848更是虚有其表，连核心业务也惹上纠纷，最后股东终于按捺不住……现在经常有人问王峻涛，如果当时不要上市，如果当时不分拆，如果……但王说：假设没有意义，我懂得了一点：创业者要坚持、坚持、再坚持。张朝阳也说：做自己的，不要听华尔街的。或许这就是本文要寻找的答案。     

中国这样进行专利谈判肯定不行——美国移通半径公司亚太区总裁雷鸣硅谷对话《IT时代周刊》

3G谈判受阻的主要原因是在谈判中，电信研究院作为一家研究机构实力太弱，如果能聘请外国在专利谈判上有丰富经验的律师或律师行主谈，加上中国政府的免费授权政策和运营商有关采购3G设备政策的出台，专利谈判可能是另外一个结果。     

Intelligent Passive Detection of Aerial Target in Space-Air-Ground Integrated Networks

Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.     

CDMA直放站干扰案例分析

2007年11月17日22点35分.我站值班人员接到中国联通梅州分公司的电话投诉.称其在梅州城区的多个CDMA基站收到不明信号的干扰.严重影响CDMA用户的正常使用。接到投诉后.我站迅速组织技术力量查找干扰     

Boston-Power集资发展新一代锂离子电池技术

Boston-Power公司(Boston-Power,Inc.)是创新便携式电源解决方案的供应商,其解决方案可满足当今市场对更安全、更持久锂离子电池的需求.日前,该公司宣布募集了4500万美元的新资金.C系列融资是由Oak Investment Partners主导,并包括现有的蓝筹投资者Venrock Associates、Granite Global Ventures及Gabriel Venture Partners.     

新起点 新机遇——BIRTV2008吹响广播影视新技术的集结号

2008年7月11日.BIRTV2008筹备工作通报会在北京梅地亚中心召开,广电总局科技司王效杰司长、中央电视台何宗就副台长、中央人民广播电台钱岳林总工、广电总局广播科学研究院马炬院长、广播电视规划院姜文波院长、中央电视台技管办黄平刚主任以及中广国际总公司李金荣总经理等领导出席会议,与120多位参展厂家的代表和媒体界的朋友们在会上进行了座谈.     

信息产业部苟仲文副部长亲临一线指导江西抗寒救灾

2月4日,信息产业部苟仲文副部长深入省内通信运营企业现场查看了机房、通信基站和野外倒塌的电线杆,并召开座谈会,详细了解全省通信行业的受灾情况,指导江西通信业抗灾保通信畅通工作。苟仲文高度评价了江西省通信行业抗灾保通信畅通工作,对全省通信行业广大干部员工在抗雪灾行动中的无私奉献表示感谢,并勉励全省通信行业要继续战斗,做好后期的抗灾工作。     

农村电子商务的5个问题和建议

目前我国农业管理部门已经重视到电子商务对农村经济发展的重要性,农村信息化建设步伐加快,进一步促进农村电子商务发展.从对中国互联网认识和使用的群体来看,绝大部分都集中于受过高等教育的人口,虽然会有分布于农业行业的部分,但总体所占比率很低.全国各种涉农网站为实现农业增效和农民增收,每天都发布农产品供求信息.无论从交易规模、数量、还是经济效益来看,中国农村电子商务都滞后于其他领域电子商务的发展.……     

基于最小位移可视差的连续Seam Carving算法在图像缩放中的研究

图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。近年来,Seam Carving及其改进算法得到了广泛的关注和研究。由于采用了离散式最小能量线迭代搜索策略,缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。该文针对上述问题提出最小位移可视差(JND)检测算法,能够有效地检测每一次迭代中出现的潜在扭曲信息。能量权重\begin{document}$ {E}_{w} $\end{document}能够将JND信息累加传递给后续的迭代过程,从而抑制缩放过程中的边缘扭曲现象。通过JND算法和能量权重,该文首次将离散的Seam Carving模型转变为连续缩放模型。最后,在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验,验证了所提方法的有效性和先进性。     

图像自适应中基于形变控制的细缝剪裁方法

为了降低图像自适应过程中图像内容缺失和图像扭曲变形,文章中提出了一种基于形变控制的图像自适应细缝裁剪方法,将融合局部和全局显著性的显著图作为细缝裁剪参考的能量图,对需要剪裁的细缝按照这个能量图进行排序,以更好地保护图像中感兴趣区域（ROI）,保留图像的主要内容信息;同时,利用SIFT流矢量场来衡量剪裁图像的形变程度,每移除一定数量的细缝就计算剪裁图像与原始图像之间的形变,一旦形变达到某一阈值,就停止细缝裁剪,转换为均匀缩放使图像到达目标尺寸。实验结果表明,文章中提出的方法更好地平衡了均匀缩放和非均匀剪裁,更有利于保留图像主要内容和避免主要内容的变形。      

A Distortion-Sensitive Seam Carving Algorithm for Content-Aware Image Resizing

As displays become less expensive and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed (Avidan and Shamir, ACM Trans Graphics 26(3), 2007; Guo et al., IEEE Trans Multimedia 11(5):856–867, 2009; Shamir and Avidan, Commun ACM 52(1), 2009; Simakov et al. 2008; Wolf et al. 2007), which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose a distortion-sensitive seam carving algorithm for content-aware image resizing that improves edge preservation and decreases aliasing artifacts. In the proposed approach, we use local gradient information along with a thresholding technique to guide the seam selection process and provide a mechanism to halt seam carving when further processing would introduce unacceptable visual distortion in the resized image. Furthermore, anti-aliasing filter is used to reduce the aliasing artifacts caused by seam removal. Experiments have demonstrated superior performance over the current seam carving methods.     

Sparse Seam-Carving for Structure Preserving Image Retargeting

The seam carving technique is one of the most popular techniques for content-aware image retargeting. There have been many attempts to improve existing methods of seam carving to provide more aesthetic retargeting results. However, most previous methods focus on developing an energy map for preserving important image regions while retargeting. Commonly, the existing seam carving based techniques tends to produce discontinuity artifacts when retargeting straight structures. We observe that it occurs when a bunch of seams are assigned across straight structures (i.e., lines or edges). In this paper, we propose a modified seam carving method not only preserving important parts, but also maintaining the prominent structure of the image. To this end, we put a constraint that seams are sparsely assigned each other. Experimental results show that the proposed sparse seam carving yields more plausible retargeting results than previous retargeting approaches and it is very robust to images containing prominent lines or edges.     

Salient edge and region aware image retargeting

The purpose of image retargeting is to automatically adapt a given image to fit the size of various displays without introducing severe visual distortions. The seam carving method can effectively achieve this task and it needs to define image importance to detect the salient context of images. In this paper we present a new image importance map and a new seam criterion for image retargeting. We first decompose an image into a cartoon and a texture part. The higher order statistics (HOS) on the cartoon part provide reliable salient edges. We construct a salient object window and a distance dependent weight to modify the HOS. The weighted HOS effectively protects salient objects from distortion by seam carving. We also propose a new seam criterion which tends to spread seam uniformly in nonsallient regions and helps to preserve large scale geometric structures. We call our method salient edge and region aware image retargeting (SERAR). We evaluate our method visually, and compare the results with related methods. Our method performs well in retargeting images with cluttered backgrounds and in preserving large scale structures.     

Optimal bi-directional seam carving for compressibility-aware image retargeting

A novel compressibility-aware image retargeting method based on seam carving is introduced in this paper. We propose a new significance detection method, with both the edge information and visual saliency taken into consideration. A wall-seam model is constructed to evaluate the image compressibility and assign the right number of seams for each direction. By repeatedly carving out or inserting seams we can retarget the image to a new size while preserving the important content. Finally, our algorithm is completed with the supplement of uniformly scaling, the stretched image is resized to the target size with the least structure distortion brought. Experimental results on images show that those improvements are effective and our approach can preserve image content better compared to several state-of-the-art image retargeting methods.     

Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents (e.g., uniform scaling) cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content-aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.     

Detecting image seam carving with low scaling ratio using multi-scale spatial and spectral entropies

Seam carving is the most popular content-aware image retargeting technique. However, it may also be used to correct poor photo composition in photography competition or to remove object from image for malicious purpose. A blind detection approach is presented for seam carved image with low scaling ratio (LSR). It exploits spatial and spectral entropies (SSE) on multi-scale images (candidate image and its down-sampled versions). We observe that when a few seams are deleted from an original image, its SSE distribution is greatly changed. Forty-two features are designed to unveil the statistical properties of SSE in terms of centralized tendency, dispersion tendency and distribution tendency. They are combined with the local binary pattern (LBP)-based energy features to form ninety-six features. Finally, support vector machine (SVM) is exploited as classifier to determine whether an image is original or suffered from seam carving. Experimental results show that the proposed approach achieves superior detection accuracy over the state-of-the-art works, especially for resized image by seam carving with LSRs. Moreover, it is robust against JPEG compression and seam insertion.     

基于梯度矢量方向性分析的线裁剪算法

在传统线裁剪(seam carving)算法中梯度矢量的方向性是一个被忽略的因素,该文提出了一种新的基于梯度矢量方向性分析的线裁剪算法。首先利用随机纹理区域内梯度矢量方向散乱的特点,对局部区域的图像梯度矢量进行低通滤波,使算法能够提取更加合理的像素线路;接着又根据像素线路的走向不同,定义两个不同的像素能量函数,给予像素梯度矢量的x, y分量以不同的权值。实验结果图像显示,文中算法不仅可以更好地保护图像边缘等细节,还可以在整体上达到与原图像更加近似的视觉效果。定量分析结果也显示,相比其它算法该文算法在完整性距离和一致性距离两方面都取得了更好结果。     

Saliency detection in the compressed domain for adaptive image retargeting

Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments.