触摸调光控制芯片的设计与测试

电力作为一种方便的能源广泛应用于我们生活的各个方面,于是电能的节能变得尤为重要。由于家用照明设计可以从控制方面实现节能,因此人们可以根据自己不同时间的不同需要来调节灯光的亮度来达到节约电能的目的。本文提出了一种应用于家用照明亮度的调节控制芯片的设计方案,首先分析顶层模块的功能,再将其功能分类细化,分配到不同的子模块去实现,利用CMOS电压比较器来实现了过零检测、系统时钟脉冲产生、触摸控制等操作,并通过仿真软件验证了设计模块功能的正确性,最后经流片测试芯片功能满足设计要求。     

基于支持向量回归的立体图像客观质量评价模型

立体图像质量评价是评价立体视频系统性能的有效途径,而如何利用人类视觉特性对立体图像质量进行有效评价是目前的研究难点。该文根据图像奇异值有较强稳定性的特点,结合立体图像的主观视觉特性,提出了一种基于支持向量回归(Support Vector Regression, SVR)的立体图像客观质量评价模型。该模型通过分析立体图像的视觉特性,提取左右图像的奇异值作为立体图像的特征信息,然后根据立体图像的不同失真类型情况对其特征进行融合,通过SVR预测得到立体图像质量的客观评价值。实验结果表明,采用该文提出的客观评价模型对立体数据测试库进行评价,Pearson线性相关系数值在0.93以上,Spearman等级相关系数值在0.94以上,均方根误差值接近6,异常值比率值为0.00%,符合人眼视觉特性,能够很好地预测人眼对立体图像的主观感知。     

Characterizing pairwise contact patterns in human contact networks

We use a counting process representation of the pairwise contact process to analyze pairwise contact patterns. Studying two real-world traces, we find that the pairwise contact patterns have three characteristics. First, human contact patterns are influenced by daily and weekly cycles of activity. Second short time intervals with intensive contact event (bursts) are separated by long periods with few contact events. Third, the pairwise contact process exhibits long range dependence. We introduce a Markov modulated Poisson process (MMPP) as a flexible model for pairwise contact process exhibiting both regular structure and irregular bursts of activity. Using standard statistical techniques, we demonstrate that the proposed model is consistent with the empirical data. Our work has significant implication for mobility modeling and performance analysis in human contact networks.     

图像语义提取方法研究

为解决从图像的低层视觉特征到高层语义特征的＂语义鸿沟＂问题,对当前的语义提取方法进行研究,简单介绍了图像语义层次模型,并根据语义信息的来源不同,归纳总结了图像语义中基于处理范围的方法,基于机器学习的方法,基于人机交互的方法和基于外部信息源的提取方法,这些工作为图像语义提取和图像语义检索等研究提供有益参考。     

Image annotation using high order statistics in non-Euclidean spaces

Automatic image annotation is a promising way to achieve more effective image retrieval and image analysis by using keywords associated to the image content. Due to the semantic gap between low-level visual features and high-level semantic concepts of an image, however, the performances of many existing algorithms are not so satisfactory. In this paper, a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP), is proposed to deal with the issue of image annotation. To bridge the gap between human judgment and machine intelligence, the proposed scheme first constructs a dissimilarity representation for each image in a non-Euclidean space; then, the information of dissimilarity diffusion distribution for each image is achieved with respect to the high-order statistics of a triplet of nearest neighbor images; finally, a maximum a posteriori algorithm with the information of Gaussian Mixture Model and dissimilarity diffusion distribution is adopted to estimate the relevance between each annotation and an input un-annotated image. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed automatic image annotation scheme.     

Rethinking PASCAL-VOC and MS-COCO dataset for small object detection

The data and the algorithm are critical to deep learning-based small object detectors. In this paper, we rethink the PASCAL-VOC and MS-COCO dataset for small object detection. By visual analysis of the original annotations, we find that there are different labeling errors in these two datasets. To solve these problems, we build specific datasets, including SDOD, Mini6K, Mini2022 and Mini6KClean. The experimental results of several typical algorithms (e.g. SSD, YOLOv5, Faster RCNN and Deformable DETR) on the datasets show that data labeling errors (such as missing labels, category label errors, inappropriate labels) are another factor that affects the detection performance of small objects.     

Human action recognition toward massive-scale sport sceneries based on deep multi-model feature fusion

In sport sceneries, automatically recognizing human actions is a useful technique that can be popularly applied in may domains, such as human body tracking and athlete behavior analysis Most state-of-the-art deep architectures have achieved competitive performance in recognizing human action. However, it is still a challenging task due to the unavoidable occlusion, camera angle changes, and varied human posture. In this paper, we propose a novel deep multimodal feature fusion algorithm for human action recognition. The key technique is a multi-model feature fusion scheme. More specifically, we fuse visual feature, skeleton posture, probability maps and audio signal into a hybrid feature, which is utilized to represent human action. Then these feature channels are optimally combined using a deep model, wherein the weights of multiple feature channels can be predicted intelligently. Finally, the optimally fused feature are fed into a multi-class SVM for conducting human action recognition. Extensive comparative results and parameter analysis have shown the effectiveness of our proposed method.     

Human-computer interaction based on face feature localization

Human-computer interaction is the way in which humans and machines communicate information. With the rapid development of deep learning technology, the technology of human-computer interaction has also made a corresponding breakthrough. In the past, the way human-computer interaction was mostly relied on hardware devices. Through the coordinated work of multiple sensors, people and machines can realize information interaction. However, as theoretical technology continues to mature, algorithms for human-computer interaction are also being enriched. The popularity of convolutional neural networks has made image processing problems easier to solve. Therefore, real-time human-computer interaction can be performed by using image processing, and intelligent of human-computer interaction can be realized. The main idea of this paper is to use the real-time capture of face images and video information to image the face image information. We perform feature point positioning based on the feature points of the face image. We perform expression recognition based on the feature points that are located. At the same time, we perform ray tracing for the identified human eye area. The feature points of the face and the corresponding expressions and implementation movements represent the user's use appeal. Therefore, we can analyze the user's use appeal by locating the face feature area. We define the corresponding action information for specific user face features. We extract the user's corresponding information according to the user's face features, and perform human-computer interaction according to the user's information.     

基于学习的模糊人脸图像重构算法研究

人脸研究一直是计算机视觉、模式识别和计算机图形学领域中的热点研究问题之一。现今,通过监视器得到的人脸图像分辨率不高,以至于给人脸识别和跟踪等后续应用带来很大的难度。本文提出一种基于学习的超分辨率重构算法来得到清晰的人脸图像,采用对图像块搜索操作进行位置限制和检查图像分块间重叠区域水平兼容性的思想,降低了搜索的复杂度,提高了匹配相关性。