基于秩序与非秩序理论的文创产品卡通形象设计研究

目的 从秩序与非秩序理论出发,研究文创产品卡通形象的设计方法.方法 对秩序与非秩序理论的视觉机制和创制理论进行梳理,结合实例,阐述了其与文创产品卡通形象设计之间的联系,分析文创产品卡通形象中的秩序与非秩序的表现形式,提出基于秩序与非秩序理论文创产品卡通形象设计的主要原则,归纳出一套基于秩序与非秩序理论的文创产品卡通形象设计流程与方法,指导设计实践,并验证这些理论方法在设计实践中有效性与可行性.结论 通过对秩序与非秩序理论的研究,文创产品卡通形象设计时应注意提高辨识度、亲和力和便于衍生设计的原则.探索基于秩序与非秩序理论的设计流程与方法,按照界定主题、主体形象设计、主体形象拓展、衍生品应用四个环节开展整体方案设计,为文创产品卡通形象设计提供新的思路与方法.     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.     

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation. Then, the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted–-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.     

A 360-Degree Panoramic Image Inpainting Network Using a Cube Map

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms.     

基于改进网格运动统计特征的图像匹配算法

为了解决尺度不变特征变换（SIFT）算法在图像匹配中匹配正确率低、耗时长等问题，提出一种基于改进网格运动统计特征RANSAC-GMS的图像匹配算法。首先，利用快速旋转不变性特征（ORB）算法对图像进行预匹配，对预匹配的特征点采用网格运动统计（GMS）来支持估计量以实现正确匹配点与错误匹配点的区分；然后，采用改进的随机抽样一致性（RANSAC）算法通过匹配点间的距离相似性对特征点进行筛选，并采用评价函数对筛选后的新数据集进行重新整理，进而实现对误匹配点的剔除。采用Oxford标准图库和现实中拍摄的图像对图像匹配算法进行测试对比，实验结果表明，所提算法在图像匹配中的平均匹配正确率达到91%以上；与GMS、SIFT、ORB等算法相比，该改进算法的近景匹配正确率和远景匹配正确率分别最少提高了16.15个百分点和3.56个百分点，说明它能有效剔除误匹配点，进一步提高图像匹配精度。     

基于流形学习的光学遥感图像分类

随着光学遥感图像技术的快速发展与广泛应用,对光学遥感图像的准确分类具有深远的研究意义。传统特征提取方式提取的高维特征中夹杂着许多冗余信息,分类过程可能导致过拟合现象,针对传统的线性降维算法不足以保持原始数据的内部结构,容易造成数据失真这一问题,提出基于流形学习的光学遥感图像分类算法。该算法首先提取出图像的SIFT特征,然后将流形学习运用于特征降维,最后结合支持向量机进行训练和识别。实验结果表明,在Satellite、NWPU和UCMerced实验数据中,冰川、建筑群和海滩分类精度得到了有效提高,达到85%左右;针对沙漠、岩石、水域等特殊环境遥感图像,分类精度提高了10%左右。总而言之,基于流形学习的分类算法对通过降维之后的数据能够保持在原高维空间中的拓扑结构,相似特征点能得到有效聚合,预防了"维数灾难",减少了计算量,保证了分类精度。     

Controllable Image‐Based Transfer of Flow Phenomena

Modelling flow phenomena and their related weathering effects is often cumbersome due their dependence on the environment, materials and geometric properties of objects in the scene. Example‐based modelling provides many advantages for reproducing real textures, but little effort has been devoted to reproducing and transferring complex phenomena. In order to produce realistic flow effects, it is possible to take advantage of the widespread availability of flow images on the Internet, which can be used to gather key information about the flow. In this paper, we present a technique that allows the transfer of flow phenomena between photographs, adapting the flow to the target image and giving the user flexibility and control through specifically tailored parameters. This is done through two types of control curves: a fitted theoretical curve to control the mass of deposited material, and an extended colour map for properly adapting to the target appearance. In addition, our method filters and warps the input flow in order to account for the geometric details of the target surface. This leads to a fast and intuitive approach to easily transfer phenomena between images, providing a set of simple and intuitive parameters to control the process.     

水泥工厂视频监控智能预警平台研究及规划

视频监控系统作为智能化工厂体系架构的子模块，在水泥工厂得到了广泛的部署和应用，为安全、生产流程等管理需求提供了远程可视化的技术支撑手段。为进一步提升工厂智能化水平，提高管理效率，本文提出了基于视频图像分析技术的智能监控预警平台的路线规划，实施步骤和方法，并对该平台的初步应用效果进行了量化评估，为后续平台建设提供科学决策依据。     

卷积神经网络用于关节角度识别与姿势评估

为了快速准确地输出各种工作姿势风险评估结果，提出采用Kinect v2与卷积神经网络识别人体各关节角度，并输出标准姿势风险的评估得分。首先使用亚像素角点提取的棋盘标定算法标定Kinect两个摄像头，其次使用改进后的双边滤波对深度图像去噪，使用卷积神经网络识别人体关节二维位置，结合深度信息获取实际三维坐标并计算人体关节角度，最后输出姿势风险评估得分。通过两种实验分别验证了提出的Kinect角度识别与姿势评估的准确性，表明该方法关节角度识别与姿势风险评估的准确率均较高，是一种低成本、高可靠性的姿势评价方法，具有一定的科学意义和工程应用价值。