相比起西方发达国家较为成熟的征信业,我国的互联网金融起步较晚但发展迅速,伴随我国的大数据、云计算、人工智能以及"互联网+"等互联网技术的发展,伴随而来的个人征信问题也越来越无法忽视.

在当前信息技术迅速发展的新时代,各行各业开始广泛应用计算机信息,其给人们的生活与工作带来了极大的便利,尤其是在人们日常办公过程中提供的帮助很大,同时丰富了办公的形式与内容.本文从计算机信息技术在办公自动化在的作用出发,探究了公办自动化中计算机信息处理技术的应用,从而为更好的发挥计算机信息处理技术的优势奠定基础.

A common assumption in stereo matching is that the corresponding pixels in stereo images have similar pixel values. Unfortunately, such an assumption may not be true due to radiometric variations in different views, leading to severely degraded matching results. In this letter, we propose a radiometrically invariant stereo matching algorithm called Optimal Local Adaptive Radiometric Compensation (LARAC). In LARAC, we approximate the spatially varying Pixel Value Correspondence Function (PVCF) between a corresponding pixel pair as a locally consistent polynomial within an optimal local adaptive window. The optimal polynomial coefficients are obtained for each candidate disparity value and are used to compute the matching cost. Meanwhile, a self-correction property is achieved by the proposed LARAC, leading to reduced matching errors for the outlier pixels. Experimental results suggest that the proposed LARAC outperforms other state-of-the-art stereo matching algorithms.

随着经济的不断发展，WEB使用挖掘已经是实现个性化推荐系统的有效途径。通过对网站日志数据进行挖掘发现频繁访问模式，再结合活动用户的访问页面序列来实现个性化的在线推荐。本文将就基于WEB的数据挖掘技术进行相关探讨。

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.

Recently a new information display technique named temporal psychovisual modulation (TPVM) was developed to extend the utilities of optoelectronic displays. A typical usage of TPVM is the backward-compatible stereoscopic display, where a stereoscopic view is perceived with 3-D glasses while a 2-D view of the same scene is concurrently available for naked-eye viewers. The most challenging part of this task is to display a clean 2-D image without ghosting artifacts. In order to reduce the ghosting artifacts, we present an improved TPVM system for backward-compatible stereoscopic display. Different from previous approaches, we rebuild the TPVM system by investigating the light perception process of human eyes, and reformulate the problem as a minimization of the light intensity difference between the target and perceived views. In order to derive the light intensities, we model it as a monotonie increasing function of the pixel intensity consisting of a scaling plus an offset, and incorporate this model into our system. By solving the optimization problem, ghosting artifacts are significantly reduced in the 2-D view. Moreover, a closed-form solution is provided for real-time applications. Experimental results demonstrate that the proposed method is superior to previous approaches in terms of the 2-D view quality, while the quality of the 3-D view remains unchanged.

We study the problem of formulating the discrete dense stereo matching using continuous convex optimization. One of the previous work derived a relaxed convex formulation by establishing the relationship between the disparity vector and a warping matrix. However it suffers from high computational complexity. In this paper, the previous convex formulation is translated into an equivalent quadratic programming (QP). Then redundant variables and constraints are eliminated by exploiting the internal sparse property of the warping matrix. The resulting QP can be efficiently tackled using interior point solvers. Moreover, enhanced smoothness term and effective post-processing procedures are also incorporated to further improve the disparity accuracy. Experimental results show that the proposed method is much faster and better than the previous convex formulation, and provides competitive results against existing convex approaches.

In this paper, we propose a virtual 3D Eyeglasses Try-on (3DET) system, with efficient, realistic and real-time augmented performance. The 3DET system captures user's performance with the help of depth camera and renders the glasses properly on the video stream immediately after user's selection. The virtual eye-glasses follow with the motion (movement or rotation) of user's head simultaneously. The high efficiency of our 3DET system is achieved by simplifying the eyeglasses matching procedure, making use of the active appearance model (AAM) based face tracking algorithm. This is completely different from existing methods, which usually relies on eye detection. In addition, due to the exploiting of a generic 3D face model during tracking and displaying, the 3DET system can handle occlusion problem easily and render realistic glasses in videos effectively. Experimental results demonstrate that the proposed 3DET system is able to produce superior natural and smooth visual results with virtual glasses fitted on the users face at 30 fps with a high level of accuracy on common hardware.

In this paper, we present a novel view synthesis method named Visto, which uses a reference input view to generate synthesized views in nearby viewpoints. We formulate the problem as a joint optimization of inter-view texture and depth map similarity, a framework that is significantly different from other traditional approaches. As such, Visto tends to implicitly inherit the image characteristics from the reference view without the explicit use of image priors or texture modeling. Visto assumes that each patch is available in both the synthesized and reference views and thus can be applied to the common area between the two views but not the out-of-region area at the border of the synthesized view. Visto uses a Gauss-Seidel-like iterative approach to minimize the energy function. Simulation results suggest that Visto can generate seamless virtual views and outperform other state-of-the-art methods.

Cost aggregation is the most essential step for dense stereo correspondence searching, which measures the similarity between pixels in the stereo images. In this paper, based on the analysis of the optimal adaptive weight, we propose a novel support aggregation strategy by adaptive weighting selection. The proposed method calculates the aggregation cost by the joint optimization of both left and right matching cost. By assigning more reasonable weighting coefficients, we exclude the occlusion pixels while preserving sufficient support region for accurate matching. The proposed optimal strategy can be integrated by any other adaptive weighting based cost aggregation method to generate more reasonable similarity measurement. Experimental results show that, compare with traditional methods, our algorithm can reduce the foreground fatten phenomenon while increasing the accuracy in the high texture regions.

Ray-space interpolation is one of the key technologies to generate virtual viewpoint images, typically in epipolar plane images (EPI), to realize free viewpoint television (FTV). In this paper, a novel Radon transform based ray-space interpolation algorithm (RTI) is proposed to generate virtual viewpoint images in the EPI. In the proposed RTI, feature points of each EPI are first extracted to form the corresponding feature epipolar plane image (FEPI). Radon transform is then applied to each FEPI to detect the candidate interpolation direction set. Then corresponding pixels in neighboring real view rows for each pixel to be interpolated are found by some block matching based interpolation method, in which the smoothness property of the disparity field and the correlation among neighboring EPIs are explored. Possible occlusion regions are processed by the proposed one-sided interpolation. Finally, to solve the problem that the cameras are not equally spaced, a novel ray-space based spacing correction algorithm is proposed to correct the EPI such that pixels corresponding to the same scene point will form a straight line. Experimental results suggest that the proposed RTI and the spacing correction algorithm can achieve good performance. Moreover, compare with traditional methods, our proposed algorithm can generate good interpolation result even if apriori depth range is not known.

Multimedia streaming service providers such as YouTube often need to design caching strategy based on predicted future media content propagation pattern. It is commonly observed that social network behavior tends to have great correlation with the content propagation pattern. In this paper, we propose an Advanced Independent Cascade Model (AICM) which is an extension of the existing ICM method to predict the future propagation pattern of multimedia (YouTube) content based on scraped social network (Facebook) user profiles in terms of their “talkativeness” and “influential power”. Simulation results suggest that the proposed AICM is reasonable and consistent.

In this paper, a novel ‘co-superpixel’ generation method is proposed via the graph matching. The co-superpixel can capture the common semantic information in coupled images. Therefore, it is significant for various applications in visual pattern recognition. Specifically, we first introduce a superpixel correspondence method based on the graph matching. The main property is that it has the ability to capture the consistent intermediate-level semantic information in coupled images, which can represent the region-based similarity rather than the conventional similarity based on low-level vision features. Second, a new co-superpixel generation method is proposed by the superpixel-merging incorporated with the graph matching cost and the adjacent superpixel appearance similarity in coupled images simultaneously. Furthermore, we extend the proposed co-superpixel method to tackle the object matching problem. The experimental results show that the object matching can be effectively addressed by the co-superpixel. The proposed method is effective for challenging cases in which object appearance changes, deformation and background clutter.

Halftone image watermarking has been explored and developed rapidly over the past decade. However, there are still issues to be studied. This paper presents a data hiding method called Data Hiding by Dual Color Conjugate Error Diffusion (DHDCCED) to hide a binary secret pattern into two error diffused color halftone images, such that when the two color halftone images are overlaid, the secret pattern will be revealed. The experimental results show that DHDCCED can significantly improve the performances when comparing both the correct decoding rate and the visual quality of the revealed secret pattern to the existing method Color Conjugate Error Diffusion (CCED).

Given two pictures of the same scene captured using the same camera and the same lens, the one captured with a large aperture will appear partially blurred while the other captured with a small aperture will appear totally sharp. This paper investigates two possible ways of inferring depth of the scene from such an image pair with the constraint that both pictures are focused on the closest point of the scene. Our first method uses a series of Gaussian kernels to blur the image pair, and in the second method, the image pair will be shrunk to a series of smaller dimensions. In both methods, sharp areas in both images will always stay similar to each other, whereas the areas that appear sharp in one image but blurred in the other will not be similar until they are blurred using a large Gaussian kernel or shrunk to small dimensions. This observation enables us to roughly tell which objects in the scene are closer to us and which ones are farther away. At the end of this paper, we will discuss the limitations of our proposed approaches and some of the directions for future work.

3D technologies such like three-dimensional television and free viewpoint television have caught enormous attentions in the consumer market recently. However, because of the inaccurate camera configuration and environmental constraint, there are errors in the assumed equally-spaced camera intervals. In this paper, we propose a novel camera spacing correction algorithm to detect the spacing errors among the multiple cameras by making the corresponding points co-linear in the epipolar plane images. Experimental results show that the proposed algorithms are robust and can achieve good performance even if the corresponding pixels are not well detected. Meanwhile, our algorithm can be solved by convex optimization with an extremely low complexity.

In depth map coding for 3D video coding systems, coding errors in edges can severely affect the synthesis quality. Edge errors mainly compose of two parts: one is blurring and ringing artifact around sharp edge and the other is fake edge caused by blocking artifact. In this paper, we propose an adaptive depth map filter to remove blocking artifacts while preserving depth edges. The proposed filter is designed based on bilateral filter, in which the range kernel parameter is changed adaptively considering the strength of edges and blocking artifacts. Experimental results demonstrate that the proposed depth map filter can achieve up to 0.41 dB gain on the synthesis quality compared to the deblocking filter in MVC at low bit rate.

Subpixel-based downsampling is a new downsampling technique which utilizes the fact that each pixel in LCD is composed of three individually addressable subpixels. Subpixel-based downsampling can provide higher apparent resolution than pixel-based downsampling. In this paper we study the inverse problem of subpixel-based downsampling. We found that conventional pixel-based super resolution algorithms are not suitable for subpixel-based downsampled images due to the special downsampling pattern. In this paper we propose a super resolution algorithm specially for subpixel-based downsampled images, which use piecewise autoregressive model to model spatial correlation of neighboring pixels, and incorporate the special data degradation term corresponding to the subpixel downsampling pattern. We formulate the super resolution problem as a constrained least square problem and solve it using Gauss-Seidel iteration. Experiment results demonstrate the effectiveness of the proposed algorithm.

行为引导型教学法以学生为中心,以培养学生行为能力为目标,在教师的行为引导下,通过多种不定型的活动形式,激发学生的学习热忱和兴趣,有效地提高学生的综合素质,特别是能力素质,使学生学会学习、学会应用、学会创新,从而提高教学质量。