城南的区域价值潜藏巨大商机——专访四川新创意实业有限责任公司董事长 卢小华

生活不仅仅是一种体验,它更是一种品味,一种享受,一种境界。每一次选择,就是一种体验,一种享受,生活如此,工作亦如此。正如卢小华选择新创意,新创意选择城南。     

总裁声音

对于消费者来说,品牌是一种标准,是一种信任,是一种追求,也是一种时尚。对于企业而言,品牌是一种信念、一种价值。所谓信念是消费者对产品的认可;所谓价值,我认为是品牌的资产。     

总裁声音

对于消费者来说,品牌是一种标准,是一种信任,是一种追求,也是一种时尚。对于企业而言,品牌是一种信念、一种价值。所谓信念是消费者对产品的认可;所谓价值,我认为是品牌的资产。     

第五届世界显示协会年度奖

1999年,一种低温多晶硅AMLCD获得了第五届世界显示协会(SID)年度奖的金奖.其它获奖项目还有一种新型轻便HMD,一种液晶电视,一种液晶监视器,一种集成的图像处理器,以及一种数字影像接口协议.     

水煮2005

人生或者企业，总有浮沉。《水煮三国》里说“成败与否，在乎一心”，自然是不错的了。教育心理学的那句经典名言。播下一种思想，收获一种行为：播下一种行为收获一种习惯；播下一种习惯，收获一种性格；播下一种性格，收获一种命运。”经成忆君这么一推广，路人皆知。至于书中暗含的厚黑学，则无需刻意理会。     

沟通·设计·表现——《读书·风华燕赵系列》节目宣传片创作体会

设计是一种变化、一种感觉、一种积累、一种概括,更是一种锦上添花的升华.在电视节目后期制作中,设计是画龙点睛的核心步骤.它可以化平庸为亮丽,化单调为丰富,甚至化腐朽为神奇.     

介绍几种自动调焦系统

自动调焦系统有几种方式,一种是焦点检测方式,一种是三角测距方式,在照相机里还有一种是超声波测速器。本文主要介绍前两种方式。     

纯粹之美 尊贵之美——香武仕PALACE皇宫系列音频精显技术

纯粹,是一种境界。比如说,“唯美”就是一种纯粹,是一种抽象、形而上的完美意境。一台器材、一件事物如果能将一种东西(感觉)毫无瑕疵地表现出来,达到了一种完美的境界,那它也就是纯粹的,有一种纯粹之美。丹麦香武仕音响最新推出的皇宫系列就是如此。它以独到的     

行IT天下，创银色未来

过去凡有水井处必有柳词．如今凡有企业处必有责任。“绿色之道”对企业而言,不再只是一种口号,一种牺牲．而是一种生存使命．一种竞争优势．一份社会责任。     

光纤带宽微机测试系统

一、引言目前,多模光导纤维的基频传输带宽的测量普遍采用国际IEC文件建议的两种方法。一种是扫频法,即对光纤直接进行扫频测量;一种是利用付立叶变换的脉冲法。我们根据后一种方法研制了一种带微计算机的光纤带宽测试系统。     

基于改进的Hopfield离散神经网络的模式识别

本文介绍基于改进的Ｈｏｐｆｉｅｌｄ离散神经网络（ＩＨＤＮ）的模式识别。当一个要求存贮在网络中的模式输入时，我们计算它与其它模式的类似测试，并把类似测试在网络综合中加以考虑。当输入任一矢量时，由于网络的联想能力，该矢量将进化到与它距离最近的一个存贮模式中。理论分析和实验仿真结果表明，ＩＨＤＮ比原Ｈｏｐｆｉｅｌｄ离散神经网络（ＨＤＮ）有更大的存贮容量和更强的联想能力。     

Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image

Application of convolutional neural networks (CNNs) for image additive white Gaussian noise (AWGN) removal has attracted considerable attentions with the rapid development of deep learning in recent years. However, the work of image multiplicative speckle noise removal is rarely done. Moreover, most of the existing speckle noise removal algorithms are based on traditional methods with human priori knowledge, which means that the parameters of the algorithms need to be set manually. Nowadays, deep learning methods show clear advantages on image feature extraction. Multiplicative speckle noise is very common in real life images, especially in medical images. In this paper, a novel neural network structure is proposed to recover noisy images with speckle noise. Our proposed method mainly consists of three subnetworks. One network is rough clean image estimate subnetwork. Another is subnetwork of noise estimation. The last one is an information fusion network based on U-Net and several convolutional layers. Different from the existing speckle denoising model based on the statistics of images, the proposed network model can handle speckle denoising of different noise levels with an end-to-end trainable model. Extensive experimental results on several test datasets clearly demonstrate the superior performance of our proposed network over state-of-the-arts in terms of quantitative metrics and visual quality.     

CSM neural network for degraded printed character optical recognition

In pattern recognition applications, the classification power of a system can be improved by combining several classifiers. Obviously performance of the system cannot be improved if the individual classifiers make all the same mistakes, thus it is important to use different features and different structures in the individual classifiers. In this context, we propose a two subnets neural network called CSM net. The first subnet, or similarity layer, is operating as a similarity measure neural network; it is based on the complementary similarity measure method (CSM). The second subnet is a competitive neural network (CNN) based on the winner takes all algorithm (WTA) that is used for the classification. In the proposed neural architecture, the statistical CSM method is analyzed, and implemented in the form of a feed forward neural network, it is named “similarity measure neural network” (SMNN). We show that the resulting SMNN synaptic weights are modified versions of the model patterns used in the training set, and that they can be considered as a memory network. We introduce a relative distance data calculated from the SMNN output, and we use it as a quality measurement tool of the degraded characters, what makes the SMNN classifier very powerful, and very well-suited for features rejections. This relative distance is used by the SMNN and compared to a first rejection threshold to accept, or reject, the incoming characters. In order to guarantee a higher recognition and reliability rates for the cascaded method, the SMNN is combined with a second subnet based on the WTA for classification using a second specific rejection threshold. These two submits combination (CSM net) boost the performance of the SMNN classifier. This is resulting in a robust multiple classifiers that can be used for setting the entire rejection threshold. The experimental results that we introduce are related to the proposed method, but the tests are introduced with various impulse noise levels, as well as the tests with broken and manually corrupted characters, and characters with various levels of additive Gaussian noise. The experiments show the effective ability of the model to yield relevant and robust recognition on poor quality printed checks, and show that the CSM net outperforms the previous works, both in efficiency and accuracy.     

Similarity measure for superquadrics

Superquadrics with parametric deformations are suitable models for use as solid primitives for describing a complicated 3-D object. Some different methods for the recovery of superquadric primitives from range data have been proposed, but there is still no effective similarity measure for the matching task between two superquadrics in a 3-D object recognition system. The authors propose a similarity measure to evaluate the degree of shape similarity between two superquadric-based objects. This similarity measure is defined as the volume of regions bounded by the surfaces of two 3-D objects. The proposed measure has been proved to be a metric. The metric value is computed by the Monte Carlo integration method. The experimental results illustrate that the proposed similarity measure is effective in matching a recovered superquadric with a set of superquadrics in the model database     

Multi-granularities capture interaction information for text matching accuracy enhancement

Modeling and Matching texts is a critical issue in natural language processing (NLP) tasks. In order to improve the accuracy of text matching, multi-granularities capture matching features (MG-CMF) model was proposed. The proposed model used convolution operations to construct the representation of text under multiple granularities, used max-pooling operations to filter more reasonable text representations and built a matching matrix at different granularities. Then, the convolution neural network (CNN) was used to capture the matching information in each granularity. Finally, the captured matching features were input into the fully connected neural network to obtain the matching similarity. By making some experiments, the results indicate that the MG-CMF model not only gets multiple granularity representations of sentences but also can obtain matching information from multiple granularities of sentences better than the other text matching models.     

基于图像退化模型的红外与可见光图像融合方法

基于深度学习的红外与可见光图像融合算法依赖人工设计的相似度函数衡量输入与输出的相似度,这种无监督学习方式不能有效利用神经网络提取深层特征的能力,导致融合结果不理想。针对该问题,该文首先提出一种新的红外与可见光图像融合退化模型,把红外和可见光图像视为理想融合图像通过不同退化过程后产生的退化图像。其次,提出模拟图像退化的数据增强方案,采用高清数据集生成大量模拟退化图像供训练网络。最后,基于提出的退化模型设计了简单高效的端到端网络模型及其网络训练框架。实验结果表明,该文所提方法不仅拥有良好视觉效果和性能指标,还能有效地抑制光照、烟雾和噪声等干扰。     

基于图匹配网络的小样本违禁物品分割算法

自动化安检技术是维护公共安全、提升安检效率的一项有效措施。在实际场景中很难获得充足的违禁品标注样本用于神经网络的训练,并且在不同场景和安全级别下违禁品的类别也有所不同。为解决基于神经网络的违禁品检测方法所面临的样本不均衡问题,以及避免模型在分割新的违禁品类别时需重新训练的现象,文中提出一种基于图匹配网络的小样本违禁物品分割算法。文中模型将测试图像与参考图像并行输入到图匹配网络中,并根据匹配结果从测试图像中分割出违禁品。所设计的图匹配模块不仅从图间节点的相似性考虑匹配问题,并利用DeepEMD算法建立全局概念,进一步提高测试图和参考图的匹配结果。在SIXray数据集和Xray-PI数据集上的实验表明:本模型在单样本分割任务中得到36.4%和51.2%的类平均交并比,分别比目前先进的单样本分割方法提高2.5%和2.3%。由此表明所设计的算法能有效提升小样本X光图像分割算法的精确度。     

一种基于像素差值编码度量不确定SAR图像相似性的新方法

在SAR图像目标识别、图像匹配等应用中,定义合理可靠的相似度尤为重要。该文在分析现有SAR图像相似度基础上,提出一种基于像素差值编码的相似度准则。首先将SAR图像按照相邻像素灰度差异生成编码图像,然后以编码图像之间的一致性作为相似度。该文从理论上证明了该相似度对SAR图像中相干斑噪声、部分遮挡和模糊等因素的鲁棒性和适应性,还讨论了将该准则应用于SAR图像匹配时,如何针对不确定性,给出一定置信水平下所有合理的匹配位置。理论和实验结果表明该文提出的相似度准则对SAR图像上相干斑噪声、部分遮挡以及模糊不敏感,能有效应用于不确定SAR图像的匹配。     

Winner-take-all neural network and its application to minimax optimisation problems

A new and simple winner-take-all neural subnetwork suitable for VLSI CMOS implementation is proposed. The subnetwork is used for the real-time solving of minimax optimisation problems. In particular, the Letter shows how to solve, in real time, an over-determined system of linear equations by using the Chebyshev L/sub infinity / norm criterion and a neural network approach. The validity and performance of the network have been checked by extensive computer simulation of different minimax optimisation problems.<>     

基于高阶残差和参数共享反馈卷积神经网络的农作物病害识别

当前,大部分农作物病害图像识别方法主要关注于精度而忽略了鲁棒性.在面向实际环境时,由于噪声干扰和环境因素影响导致识别精度不高.为此提出了一种高阶残差和参数共享反馈的卷积神经网络模型以应用于实际环境农作物病害识别.其中,高阶残差子网络为病害表观提供丰富细致的特征表达,以提高模型识别精度;参数共享反馈子网络用来进一步抑制原深层特征中的背景噪声,以提高模型的鲁棒性.实验结果表明,当面向实际环境农作物病害识别时,本文方法在识别精度和鲁棒性上均优于其他方法.