Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms.Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost,lowpower solution compared to LiDAR solutions in the field of autonomous driving.However,this technique has some problems,i.e.,(1)the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and(2)the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks.Therefore,we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection.We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence.Then,we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud.Finally,our detection framework is based on Point-Voxel-RCNN(PV-RCNN)with high-quality Pseudo-LiDAR and enriched geometric features as input.From the experimental results,our method achieves satisfactory results in monocular 3D object detection.

Cancellable Multi-Biometric Feature Veins Template Generation Based on SHA-3 Hashing

In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is proposed.The MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric shape.MBFH is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies(retina,palm print,Hand Dorsum,fingerprint).A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical sources.This may raise worries about their utilization and security when these spread out designs are subverted as everybody is acknowledged for another biometric attribute.The proposed structure comprises of four sections:input multi-biometric acquisition,feature extraction,Multi-Exposure Fusion(MEF)and secure hashing calculation(SHA-3).Multimodal biometrics systems that are more powerful and precise in human-unmistakable evidence require various configurations to store a comparative customer that can be contrasted with biometric wellsprings of people.Disparate top words,biometrics graphs can’t be denied and change to another request for positive Identifications(IDs)while settling.Cancellable biometrics is may be the special procedure used to recognize this issue.

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Feature extraction is the most critical step in classification of multispectral image.The classification accuracy is mainly influenced by the feature sets that are selected to classify the image.In the past,handcrafted feature sets are used which are not adaptive for different image domains.To overcome this,an evolu-tionary learning method is developed to automatically learn the spatial-spectral features for classification.A modified Firefly Algorithm(FA)which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose.For extracting the most effi-cient features from the data set,we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions.For selecting spatial and spectral features we have studied three different approaches namely overlapping window(OW-3DFS),non-overlapping window(NW-3DFS)adaptive window cube(AW-3DFS)and Pixel based technique.Fivefold Multiclass Support Vector Machine(MSVM)is used for classification purpose.Experiments con-ducted on Madurai LISS IV multispectral image exploited that the adaptive win-dow approach is used to increase the classification accuracy.

Extraction of Feature Points for Non-Uniform Rational B-Splines(NURBS)-Based Modeling of Human Legs

Methods of digital human modeling have been developed and utilized to reflect human shape features.However,most of published works focused on dynamic visualization or fashion design,instead of high-accuracy modeling,which was strongly demanded by medical or rehabilitation scenarios.Prior to a high-accuracy modeling of human legs based on non-uniform rational B-splines(NURBS),the method of extracting the required quasi-grid network of feature points for human legs is presented in this work.Given the 3 D scanned human body,the leg is firstly segmented and put in standardized position.Then re-sampling of the leg is conducted via a set of equidistant cross sections.Through analysis of leg circumferences and circumferential curvature,the characteristic sections of the leg as well as the characteristic points on the sections are then identified according to the human anatomy and shape features.The obtained collection can be arranged to form a grid of data points for knots calculation and high-accuracy shape reconstruction in future work.

A Novel Airborne 3D Laser Point Cloud Hole Repair Algorithm Considering Topographic Features

Hole repair processing is an important part of point cloud data processing in airborne 3-dimensional(3D)laser scanning technology.Due to the fragmentation and irregularity of the surface morphology,when applying the 3D laser scanning technology to mountain mapping,the conventional mathematical cloud-based point cloud hole repair method is not ideal in practical applications.In order to solve this problem,we propose to repair the valley and ridge line first,and then repair the point cloud hole.The main technical steps of the method include the following points:First,the valley and ridge feature lines are extracted by the GIS slope analysis method;Then,the valley and ridge line missing from the hole are repaired by the mathematical interpolation method,and the repaired results are edited and inserted to the original point cloud;Finally,the traditional repair method is used to repair the point cloud hole whose valley line and ridge line have been repaired.Three experiments were designed and implemented in the east bank of the Xiaobaini River to test the performance of the proposed method.The results showed that compared with the direct point cloud hole repair method in Geomagic Studio software,the average repair accuracy of the proposed method,in the 16 m buffer zone of valley line and ridge line,is increased from 56.31 cm to 31.49 cm.The repair performance is significantly improved.

快速3D-CNN结合深度可分离卷积对高光谱图像分类

针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成分分析(IPCA)对输入的数据进行降维预处理;其次将输入模型的像素分割成小的重叠的三维小卷积块,在分割的小块上基于中心像素形成地面标签,利用三维核函数进行卷积处理,形成连续的三维特征图,保留空谱特征。用3D-CNN同时提取空谱特征,然后在三维卷积中加入深度可分离卷积对空间特征再次提取,丰富空谱特征的同时减少参数量,从而减少计算时间,分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证,并且同其他经典的分类方法进行比较。实验结果表明,该方法不仅能大幅度节省可学习的参数,降低模型复杂度,而且表现出较好的分类性能,其中总体精度(OA)、平均分类精度(AA)和Kappa系数均可达99%以上。

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Today,fatalities,physical injuries,and significant economic losses occur due to car accidents.Among the leading causes of car accidents is drowsiness behind the wheel,which can affect any driver.Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents.This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos.This model depends on integrating a 3D convolutional neural network(3D-CNN)and long short-term memory(LSTM).The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames.The learned features are then used as the input of the LSTM component for modeling high-level temporal features.In addition,we investigate how the training of the proposed model can be affected by changing the position of the batch normalization(BN)layers in the 3D-CNN units.The BN layer is examined in two different placement settings:before the non-linear activation function and after the non-linear activation function.The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD.3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers.We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other.As a result,the model achieves a test detection accuracy of 96%,93%,and 90%on YawDD,Side-3MDAD,and Front-3MDAD,respectively.

3DMKDR:3D Multiscale Kernels CNN Model for Depression Recognition Based on EEG

Depression has become a major health threat around the world,especially for older people,so the effective detection method for depression is a great public health challenge.Electroencephalogram(EEG)can be used as a biomarker to effectively explore depression recognition.Motivated by the studies that multiple smaller scale kernels could increase nonlinear expression compared to a larger kernel,this article proposes a model named the three-dimensional multiscale kernels convolutional neural network model for the depression disorder recognition(3DMKDR),which is a three-dimensional convolutional neural network model with multiscale convolutional kernels for depression recognition based on EEG signals.A three-dimensional structure of the EEG is built by extending one-dimensional feature sequences into a two-dimensional electrode matrix to excavate the related spatiotemporal information among electrodes and the collected electrode matrix.By the major depressive disorder(MDD)and the multi-modal open dataset for mental-disorder analysis(MODMA)datasets,the experiment shows that the accuracies of depression recognition are up to99.86%and 98.01%in the subject-dependent experiment,and 95.80%and 82.27%in the subjectindependent experiment,which are higher than alternative competitive methods.The experimental results demonstrate that the proposed 3DMKDR is potentially useful for depression recognition in older persons in the future.

水下目标识别的1/3倍频程掩蔽谱方法

在对现有水下目标噪声信号分布研究的基础上,基于声学分析中的1/3倍频程分析和人耳的听觉掩蔽效应,提出了1/3倍频程掩蔽谱特征提取方法,并对水下目标辐射噪声进行了特征提取和特征分析。结果表明,不同类的目标,其主频范围有其固定的区域。I类目标的主频一般在100Hz附近,II类目标的主频一般在100Hz和200Hz附近,III类目标的主频一般在450Hz附近。针对从属于三大类目标的29种目标中提取出的1107个样本进行了分类识别实验,识别正确率大于86%,验证了所提出的方法的有效性。

基于Candide-3模型的姿态表情人脸识别研究

针对姿态表情严重影响人脸识别准确率的问题,基于Candide-3模型的简化,提出了形状表情关键点拟合的人脸几何结构重建和基于三角网格模型的纹理映射的方法,该方法确定关键特征点,根据人脸的几何结构信息确定姿态角,提取Candide-3模型形状表情对应点,调整模型参数,进行几何结构重建;对几何结构中每个三角网格模型进行纹理影射,得到逼真的特定人脸模型。实验结果表明,该方法提高了人脸重建速度,达到减弱姿态表情对人脸识别影响的目的。

用变形雅可比(p=4,q=3)-傅立叶矩描述红花粉末的显微图像

本文用变形雅可比(p=4,q=3)-傅立叶矩,对红花粉末花粉粒的几个显微特征点图像进行重建实验,当N=M=13时,各种图像的重建图像能够恢复原始图像的主要特征,可以很清楚地分辨相同图像的不同变形体。这说明PJFM's的图像特征抽取性能非常强。随着矩数量的增加图像重建质量提高,并且N=M=23以后,重建图像和原始图像基本相同。

基于GF-3全极化SAR影像多特征优选的水产养殖塘提取

高分三号作为中国首颗民用高分辨率多极化合成孔径雷达卫星,为水产养殖用地监测提供了重要的数据源。为了充分利用GF-3全极化SAR影像,该研究提出了一种基于特征优选的全极化SAR影像养殖塘提取方法。首先通过极化分解和灰度共生矩阵方法共获取了55维特征;然后对影像进行多尺度分割,并利用随机森林-递归特征消除(Random Forest-Recursive Feature Elimination,RF-RFE)算法进行特征优选;最后基于最优特征集进行随机森林分类提取了养殖塘。以南京固城湖和东台近海两个典型区为研究区,利用GF-3全极化数据进行养殖塘提取试验,结果表明,与单一极化分解方法相比,综合利用多种极化特征在一定程度上提高了总体分类精度,但仍然难以区分养殖水体和非养殖水;经过特征优选,香农熵SE及其强度分量SE_I对于养殖塘识别是很好的极化参数,而纹理特征Variance的引入有效减少了养殖水体和非养殖水体的错分;该研究方法与最大似然和支持向量机相比,总体精度最高,固城湖区域和东台近海区域分别为96.85%和94.60%,研究结果可为GF-3卫星在水产养殖塘提取方面的应用提供参考。

3D Model Retrieval Method Based on Affinity Propagation Clustering

In order to improve the accuracy and efficiency of 3D model retrieval,the method based on affinity propagation clustering algorithm is proposed. Firstly,projection ray-based method is proposed to improve the feature extraction efficiency of 3D models. Based on the relationship between model and its projection,the intersection in 3D space is transformed into intersection in 2D space,which reduces the number of intersection and improves the efficiency of the extraction algorithm. In feature extraction,multi-layer spheres method is analyzed. The two-layer spheres method makes the feature vector more accurate and improves retrieval precision. Secondly,Semi-supervised Affinity Propagation ( S-AP) clustering is utilized because it can be applied to different cluster structures. The S-AP algorithm is adopted to find the center models and then the center model collection is built. During retrieval process,the collection is utilized to classify the query model into corresponding model base and then the most similar model is retrieved in the model base. Finally,75 sample models from Princeton library are selected to do the experiment and then 36 models are used for retrieval test. The results validate that the proposed method outperforms the original method and the retrieval precision and recall ratios are improved effectively.

Multi-level spherical moments based 3D model retrieval

In this paper a novel 3D model retrieval method that employs multi-level spherical moment analysis and relies on voxelization and spherical mapping of the 3D models is proposed. For a given polygon-soup 3D model, first a pose normalization step is done to align the model into a canonical coordinate frame so as to define the shape representation with respect to this orientation. Afterward we rasterize its exterior surface into cubical voxel grids, then a series of homocentric spheres with their center superposing the center of the voxel grids cut the voxel grids into several spherical images. Finally moments belonging to each sphere are computed and the moments of all spheres constitute the descriptor of the model. Experiments showed that Euclidean distance based on this kind of feature vector can distinguish different 3D models well and that the 3D model retrieval system based on this arithmetic yields satisfactory performance.

Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

The three-dimensional(3D)reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages.Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise,a lightweight stripe image feature extraction algorithm based on You Only Look Once v4(YOLOv4)network is proposed.First,Mobilenetv3 is used as the backbone network to effectively extract features,and then the Mish activation function and Complete Intersection over Union(CIoU)loss function are used to calculate the improved target frame regression loss,which effectively improves the accuracy and real-time performance of feature detection.Simulation experiment results show that the model size after the improved algorithm is only 52 MB,the mean average accuracy(mAP)of fringe image data reconstruction reaches 82.11%,and the 3D point cloud restoration rate reaches 90.1%.Compared with the existing model,it has obvious advantages and can satisfy the accuracy and real-time requirements of reconstruction tasks in resource-constrained equipment.

Study on threshold segmentation of multi-resolution 3D human brain CT image

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

Distributed Cluster Based 3D Model Retrieval with Map-Reduce

View-based 3D model retrieval methods are attracted intensive research attentions due to the high expression and stable features. In the paper, the bag-of-words (BOW) standardization based SIFT feature were extracted from three projection views of a 3D model, and then the distributed K-means cluster algorithm based on a Hadoop platform was employed to compute feature vectors and cluster 3D models. In order to get precise initial cluster center, the maximum and minimum principle based Canopy algorithm was also presented. The similarity of models was determined by the distance between the query model and each cluster center, and the cluster which nearest to the query model will be return as retrieval results. The simulations indicated that the proposed method had good results in terms of 3D model retrieval accuracy and retrieval time efficiency.

Visualization Analysis for 3D Big Data Modeling

This paper describes an automatic system for 3D big data of face modeling using front and side view images taken by an ordinary digital camera, whose directions are orthogonal. The paper consists of four keys in 3D visualization. Firstly we study the 3D big data of face modeling including feature facial extraction from 2D images. The second part is to represent the technical from Computer Vision, Image Processing and my new method for extract information from images and create 3D model. Thirdly, 3D face modeling based on 2D image software is implemented by C# language, EMGU CV library and XNA framework. Finally, we design experiment, test and record results for measure performance of our method.

结合ZY-3高分影像和OSM数据的城市建筑物提取与分类

近年来,城市化进程不断加快,城镇建设规模的不断扩张给城市规划管理带来了越来越多的挑战。充分利用高分辨率遥感影像进行城市建筑物的自动快速提取具有非常广泛的现实意义。以江西省南昌市中心城市的建筑提取为例,结合ZY-3号高分辨率遥感影像和OSM道路网矢量数据,分别从光谱指数决策规则、面向对象空间决策规则两方面出发,采用分层决策的方法完成城市不同屋面类型建筑的提取,包括蓝色建筑、红色建筑和灰色建筑。最后,对不同类别建筑物的提取精度进行检验,研究结果显示蓝色建筑物区分效果较好,红色建筑其次,水泥屋面的灰色建筑物提取精度相对较低。