A general non-local denoising model using multi-kernel-induced measures

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.     

挤出机各段温度对开孔型橡塑共混材料性能的影响

研究开孔型橡塑共混材料在不同挤出加工工艺中结构形态及力学性能的变化规律。结果表明，挤出机的机头温度应稍高于发泡剂的分解温度，均化段温度要设在发泡剂分解温度区的低温段，在挤出加工工艺中，要特别注意挤出机均化段温度及机头温度的设定，温度设定不好会使发泡剂无法发挥作用。     

塑料配方设计的一些要点

介绍了塑料配方设计时应考虑的因素,如树脂的选择,塑料助剂的选择,助剂的形态及加入量,助剂间的相互影响,卫生与环保要求,价格及来源等,并举出了一些具有实用性的例子。     

湿法成型中稳定浆料的制备

为了满足各工业领域对陶瓷材料的需求,人们开发和研制多种适合成型复杂形状的湿法成型技术,湿法成型中最关键的一步是稳定浆料的制备。本文从稳定浆料的制备条件,形成稳定浆料的理论依据以及稳定浆料所具备的流变特性等几个方面,对湿法成型中浆料的制备进行了综述。     

填充母料及其他常用塑料母料

塑料母料对推动塑料工业的迅猛发展起了很大作用，其主要特点是：可以简化生产工艺过程：原料混合方便，混炼质量均匀，提高生产效率及制品性能指标；减少粉尘飞扬及对设备的磨损；降低制品在换色时清洗螺杆的用料量；延长原料储存的保质期等。     

Polyline‐sourced Geodesic Voronoi Diagrams on Triangle Meshes

This paper studies the Voronoi diagrams on 2‐manifold meshes based on geodesic metric (a.k.a. geodesic Voronoi diagrams or GVDs), which have polyline generators. We show that our general setting leads to situations more complicated than conventional 2D Euclidean Voronoi diagrams as well as point‐source based GVDs, since a typical bisector contains line segments, hyperbolic segments and parabolic segments. To tackle this challenge, we introduce a new concept, called local Voronoi diagram (LVD), which is a combination of additively weighted Voronoi diagram and line‐segment Voronoi diagram on a mesh triangle. We show that when restricting on a single mesh triangle, the GVD is a subset of the LVD and only two types of mesh triangles can contain GVD edges. Based on these results, we propose an efficient algorithm for constructing the GVD with polyline generators. Our algorithm runs in O(nNlogN) time and takes O(nN) space on an n‐face mesh with m generators, where N = max{m, n}. Computational results on real‐world models demonstrate the efficiency of our algorithm.