Tongue Image Feature Extraction in TCM

In this paper, digital image processing and computer vision techniques are applied to study tongue images for feature extraction with VC and Matlab. Extraction and analysis of the tongue surface features are based on shape, color, edge, and texture. The developed software has various functions and good user interface and is easy to use. Feature data for tongue image pattern recognition is provided, which form a sound basis for the future tongue image recognition.     

RECOGNITION OF WEAR PARTICLES IN LUBRICATING OIL USING LVQ NEURAL CLASSIFIER

A technique for wear particle identification using computer vision system is described. The computer vision system employs LVQ Neural Networks as classifier to recognize the surface texture of wear particles in lubricating oil and determine the conditions of machines. The recognition process includes four stages:(1) capturing image from ferrographies containing wear particles;(2) digitising the image and extracting features;(3) learning the training data selected from the feature data set;(4) identifying the wear particles and generating the result report of machine condition classification. To verify the technique proposed here, the recognition results of several typical classes of wear particles generated at the sliding and rolling surfaces in a diesel engine are presented.     

A robust face recognition method using multiple features fusion and linear regression

This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.     

Current progress of information fusion in China

Information fusion is a challenging task to extract the required information for high-level applications from various homogeneous or heterogeneous sensors. This paper summarizes some improvements in recent five years in the presented structure of information fusion, including estimation fusion, image fusion and others. Some technologies of information fusion and their variants are discussed. Although the state-of-the-art of the algorithms for information fusion have been proposed, there still remains some fundamental challenges with regard to exploiting the emerging multi-sensors＇ characteristics and their special structures. Finally, some potential prospects of estimation fusion and image fusion are discussed.     

A novel adaptive temporal-spatial information fusion model based on Dempster-Shafer evidence theory

In the field of target recognition based on the temporal-spatial information fusion, evidence theory has received extensive attention. To achieve accurate and efficient target recognition by the evidence theory, an adaptive temporal-spatial information fusion model is proposed. Firstly, an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy, which realizes the credibility discrimination and adaptive correction of the spatial evidence. Secondly, the c...     

Information engineering at Oxford

Information engineering mainly focus on application, uncertainty and information for its utility. This lecture discussed several aspects of information engineering research in Oxford, included the areas of mobile robotics, signal processing, realtime computer vision for object tracking, 3D reconstruction of space, medical image analysis and artificial intelligence. Then what information engineering really means was discussed and the possibilities for the future of this field was prospected finally.     

Dual relations in physical and cyber space

1 Introduction The speedy progress of embedded computing and the wireless communication technology promotes the fusion of computer, communication and sensing tech- nology. Verity of information appliances with the em- bedded processor can be integrated wi…     

Micro-expression recognition algorithm based on graph convolutional network and Transformer model

Micro-expressions are spontaneous, unconscious movements that reveal true emotions.Accurate facial movement information and network training learning methods are crucial for micro-expression recognition.However, most existing micro-expression recognition technologies so far focus on modeling the single category of micro-expression images and neural network structure.Aiming at the problems of low recognition rate and weak model generalization ability in micro-expression recognition, a micro-expre...     

Efficient and Robust Feature Model for Visual Tracking

Long duration visual tracking of targets is quite challenging for computer vision,because the environments may be cluttered and distracting.Illumination variations and partial occlusions are two main difficulties in real world visual tracking.Existing methods based on hostile appearance information cannot solve these problems effectively.This paper proposes a feature-based dynamic tracking approach that can track objects with partial occlusions and varying illumination.The method represents the tracked o...     

Shadow free texture acquisition of a large scale scene for city modeling

Texture acquisition of a large scale scene is one of the critical research areas in computer vision and can be used in other application areas such as computer graphics (CG),the intelligent transportation system (ITS) and the 3D geographic information system (GIS). Moreover,to acquire texture without noise (e. g.,a shadow,an obstacle body) is vital for such work. Although obstacles can be removed by using 3D geometric data,shadow elimination is still a difficult problem and strongly required for the CG and ITS community,especially for city modeling and simulation purposes. In this paper,we propose an automatic multiple image fusion technique and an efficient and simple shadow removing technique to retrieve high quality texture images of an urban area. The image fusion can be efficiently achieved by epipolar plane image (EPI) analysis,and the shadow elimination can be successfully carried out by an illumination-independent color clustering technique. The strength of this algorithm is that we can successfully fuse multiple images and eliminate shadows from the fused single image, especially in low dynamic range images,which have proven difficult using previous techniques.     

基于有限内存Broyden-Fletcher-Goldfarb-Shanno优化算法的图像非刚性配准方法

图像配准可以分为刚性配准和非刚性配准两类,图像非刚性配准是图像处理研究的热点和难点,其中的参数模型往往转化为无约束优化问题的求解,当参数数目较大时求解比较费时,提高此类算法效率的关键之一是减少迭代算法中矩阵的计算量和存储量.本文研究了一种非刚性配准方法,该方法应用Basic样条自由变形模型,此模型改变控制点只影响其附近...     

基于神经网络的旗语识别系统

视觉是人类从外界获取信息的主要途径，人们希望计算机视觉在人机交互技术中发挥重要的作用。介绍一个基于视觉的动作实时识别系统，该系统用于在门桥模拟训练中对旗语动作进行识别。与以往工作不同的是，该系统仅用一个摄像机来实现多个动作的识别与跟踪，避免了多目视觉的特征匹配问题，进而提高了系统的稳定性和识别速度。     

基于Spline金字塔的图像融合方法

图像融合作为信息融合的一个重要领域已广泛应用于遥感、医学、计算机视觉、军事目标探测和识别等多方面。文章在传统的多分辨金字塔结构—Laplace金字塔的基础上提出Spline金字塔融合算法,有一定的探索性和创新性。在融合过程中着重讨论了塔形结构的分解和重构及融合规则。由实验结果可以判断Spline金字塔算法是一种有效的图像融合算法。     

基于RCBAM和SSD的特征融合低照度环境目标检测算法研究

针对低光照、雨雾等恶劣场景对智能驾驶视觉系统检测能力的影响,提出了一种雷达与相机特征融合的网络模型. 基于毫米波雷达信息和注意力模型构建了雷达注意力机制特征模块,该模块可以为特征融合网络提供一个先验信息和增加算法在目标候选区域权重. 测试结果表明,引入雷达注意力机制模块后,特征融合网络的目标检测性能要比仅依赖计算机视觉的检测性能有了明显的提升,并且在复杂场景下的目标检测鲁棒性更强.      

基于注意力机制多尺度网络的自然场景情绪识别

情绪识别作为计算机视觉的一项基本课题已经取得很大进展,然而在无约束自然场景中的情绪识别仍具挑战性.现有方法主要是利用人脸、姿态以及场景信息识别情绪,但是忽略了人物个体在场景中的不确定性,以及不能很好地挖掘场景中的情绪线索.针对现有研究存在的问题,提出了基于人物与场景线索的双分支网络结构,两个分支独立学习,通过早期融合得到情绪分类结果.对于人物在场景中的不确定性,引入身体注意力机制预判人物情绪置信度进而获得人体的特征表示,场景中引入空间注意力机制和特征金字塔以便充分获得场景中不同粒度的情绪线索.实验结果表明,此方法有效融合人物与场景信息,在EMOTIC数据集下能够明显提高情绪识别率.     

基于视觉的多机器人协作SLAM 问题

 视觉SLAM 仅采用图像作为外部信息,用于估计机器人位置的同时构建环境地图。SLAM 是机器人自主性的基本前提,如今在小动态环境采用激光或者声呐传感器构建2D 地图得到较好地解决。然而动态、复杂和大范围下的SLAM 仍存在问题,使用视觉作为基本的外部传感器是解决问题的一个新颖热门的研究方法。在视觉SLAM 中使用计算机视觉技术,如特征检测、特征描述和特征匹配,图像识别和恢复,还存在很多改善的空间。本文在视觉SLAM 领域的最新技术的基础上,对基于视觉的多机器人协作SLAM 领域的前沿技术进行综述。     

基于小波网络的主被动毫米波数据特征层融合

针对毫米波主被动数据决策层融合预处理代价高、丢失目标信息的缺点,提出了一种基于小波网络的毫米波主被动数据特征层融合方法。该方法从主被动数据中提取特征值,将特征值作为小波神经网络的输入,在小波网络中实现主被动数据的特征层融合,对目标进行识别。实验和计算机仿真表明,对与毫米波主被动数据融合,基于小波神经网络的特征层融合的识别率比基于D-S证据理论的决策层融合的识别率高。     

基于小波的图像融合算法的实现

图像融合技术以其综合多传感器信息的优越性日益受到越来越多的重视，广泛地应用在医学、遥感、计算机视觉以及其它领域，其中有效实用的融合算法是图像融合的关键．本文着重讨论了基于小波变换的融合方法(基于像素的融合规则)及它的实现方式，经仿真，取得了很好的效果．     

基于B样条梯度的融合图像算法

图像融合在目标识别、计算机视觉、医学、遥感图像处理等领域应用广泛.利用B样条梯度融合图像算法来解决图像融合,将融合的图像分成3×3子块,计算每个子块的B样条梯度.把待融合图像对应的子块梯度模值累加求和,计算每个子块梯度模值的权值并与每个子块相乘并求和,得到融合后的图像子块.模拟实验结果表明该方法是有效的,优于传统的主分量分析、小波变换和Laplacian金字塔等方法.     

土地资源调查的像元级遥感影像融合方法研究

文章总结了IHS彩色空间变换融合法、Brovey法、主成分分析法(PCA)等像元级影像融合的方法及评价标准,对研究区域SPOT影像的全色波段和多光谱波段分别进行IHS、PCA及Brovey方法的融合实验,并从信息熵、灰度均值和标准差、相关系数和视觉效果四个方面,对三种方法的融合效果进行定性与定量评价.分析结果表明,相对于其他两种方法而言,PCA方法在整体图像上清晰,色调协调,保留了较多的空间信息,细节特征明显,质量较好.