基于阴影图的阴影生成算法研究现状

阴影效果在计算机图形的真实感显示中非常重要,而阴影图算法又是阴影算法的主流.文中对阴影图算法展开了系统的研究,特别是对该领域近几年的发展进行综述,从阴影图数量、参数化和内容三个方面总结归纳了各类阴影图算法的基本思想、特点及其应用环境.综合考虑以上三个方面后对特定的场景选择合适的阴影图算法,除了选择场景外,还包括光源类型、内存消耗、实时性要求、硬件支持及特殊阴影要求等.最后结合当前的技术发展和应用需求展望了阴影图领域未来的研究方向.     

融合显著性与运动信息的相关滤波跟踪算法

主流的目标跟踪算法以矩形模板的形式建立被跟踪物体的视觉表征, 无法有效区分目标与背景像素, 在背景复杂、目标非刚体形变、复杂运动等挑战性因素影响下容易出现模型偏移的问题, 导致跟踪失败. 与此同时, 像素级的显著性信息与运动先验信息作为人类视觉系统有效区分目标与背景、识别运动物体的重要信号, 并没有在主流目标跟踪算法中得到有效的集成利用. 针对上述问题, 提出目标的像素级概率性表征模型, 并且建立与之对应的像素级目标概率推断方法, 能够有效利用像素级的显著性与运动观测信息, 实现与主流的相关滤波跟踪算法的融合; 提出基于显著性的观测模型, 通过背景先验与提出的背景距离模型, 能够在背景复杂的情况下得到高辨识度的像素级图像观测; 利用目标与相机运动的连续性来计算目标和背景的运动模式, 并以此为基础建立基于运动估计的图像观测模型. 实验结果表明, 提出的目标表征模型与融合方法能够有效集成上述像素级图像观测信息, 提出的跟踪方法总体跟踪精度优于多种当下最先进的跟踪器, 对跟踪场景中的背景复杂、目标形变、平面内旋转等挑战性因素具有更好的鲁棒性.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

低空突防用数字地形数据压缩研究

地形数据是低空突防系统最主要的信息来源之一,由于机载条件对电子设备的重量和体积有很大的限制,因此地形数据必须按低空防的要求进行压缩以减小其存储空间,本文探讨并比较了采用。ＬＰＣ,ＤＣＴ和ＢＴＣ三种压缩算法运行低空防用地形数据压缩的特点,文中首先设计了一个三阶预测器对地形数据进行ＬＰＣ压缩,其次给出了ＤＣＴ编码的恢复误差的估计式,并根据该估计式按低空防要求选取量化矩阵以压缩地形数据,最后提出了一种块间自适应的ＢＴＣ算法,仿真结果表明,块间自适应ＢＴＣ算法的综合压缩的性能较合乎低空突防的要求。     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

实现曲面刻字的调和方法

曲面刻字的首要问题在于如何选择刀位点，使得文字笔划角度在曲面上与平面时相同。本文的解决方法是将文字图像调和映射到曲面上确定刀位点，由调和映射的保角性质保证角度不变。描述了平面到曲面调和映射的求解过程，并以球面印字为例，进行了数值试验。     

基于视觉显著性和超像素融合的物体定位方法

针对选择性搜索算法所需定位窗口数量过多的问题,提出了一种基于视觉显著性和超像素融合的改进方法.首先,利用视觉显著性图像粗略估计物体的位置;然后,从这些初始位置开始,根据图像的表观特征融合相邻超像素,并引入一种背景分析方法以避免过度融合;最后,利用贪心算法将融合后的区域再进行组合,并生成最终的定位窗口.在Pascal VOC 2007数据集上的实验结果表明,与选择性搜索方法相比,在同样的检测标准下(查全率为0.91),改进后的方法所使用的窗口数量减少了20%,而重叠率达到了0.77.该方法由粗到细地进行物体定位,在定位窗口数量较少的情况下仍能保持较高的重叠率和查全率.     

自适应融合局部和全局稀疏表示的图像显著性检测

针对基于局部或全局稀疏表示的图像显著性检测方法频繁出现提取对象不完整、边界不光滑及噪声消除不干净等问题,提出自适应融合局部和全局稀疏表示的图像显著性检测方法。首先,对原始图像进行分块处理,利用图像块代替像素操作,降低算法复杂度;其次,对分块后的图像进行局部稀疏表示,即：针对每一个图像块,选取其周围的若干图像块生成过完备字典,基于该字典对图像块进行稀疏重构,得到原始图像的初始局部显著图,该显著图能够有效提取显著性目标的边缘信息;接着,对分块后的图像进行全局稀疏表示,与局部稀疏表示过程类似,不同的是针对每一个图像块所生成的字典来源于图像四周边界处的图像块,这样可以得到能有效检测出显著性目标内部区域的初始全局显著图;最后,将初始局部和全局显著图进行自适应融合,生成最终显著图。实验结果表明,提出算法在查准率（precision）、查全率（recall）及F-measure等指标上优于几种经典的图像显著性检测方法。     

Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation

This study presents an image segmentation system that automatically segments and labels T1-weighted brain magnetic resonance (MR) images. The method is based on a combination of unsupervised learning algorithm of the self-organizing maps (SOM) and supervised learning vector quantization (LVQ) methods. Stationary wavelet transform (SWT) is applied to the images to obtain multiresolution information for distinguishing different tissues. Statistical information of the different tissues is extracted by applying spatial filtering to the coefficients of SWT. A multidimensional feature vector is formed by combining SWT coefficients and their statistical features. This feature vector is used as input to the SOM. SOM is used to segment images in a competitive unsupervised approach and an LVQ system is used for fine-tuning. Results are evaluated using Tanimoto similarity index and are compared with manually segmented images. Quantitative comparisons of our system with the other methods on real brain MR images using Tanimoto similarity index demonstrate that our system shows better segmentation performance for the gray matter while it gives average results for white matter.     

Anomaly Detection Using Real-Valued Negative Selection

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.