Performance of Object Classification Using Zernike Moment

Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems.

Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution is one significant environmental problem.The prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher efficiency.Waste object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or images.This technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental pollution.Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data andmetrics.Therefore,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)algorithm.The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects.In the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object classification.For object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network.In addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are used.Finally,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste objects.The performance validation of the EFFDL-WODC approach was validated on the benchmark database.The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

Automatic object classification using motion blob based local feature fusion for traffic scene surveillance

Automatic object classification in traffic scene videos is an important issue for intelligent visual surveillance with great potential for all kinds of security applications. However, this problem is very challenging for the following reasons. Firstly, regions of interest in videos are of low res- olution and limited size due to the capacity of conventional surveillance cameras. Secondly, the intra-class variations are very large due to changes of view angles, lighting conditions, and environments. Thirdly, real-time performance of algo- rithms is always required for real applications. In this paper, we evaluate the performance of local feature descriptors for automatic object classification in traffic scenes. Image inten- sity or gradient information is directly used to construct ef- fective feature vectors from regions of interest extracted via motion detection. This strategy has great advantages of ef- ficiency compared to various complicated texture features. We not only analyze and evaluate the performance of differ- ent feature descriptors, but also fuse different scales and fea- tures to achieve better performance. Numerous experiments are conducted and experimental results demonstrate the ef- ficiency and effectiveness of this strategy with robustness to noise, variance of view angles, lighting conditions, and environments.

High efficient moving object extraction and classification in traffic video surveillance

Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.

Objective Synoptic Weather Classification on Air Pollution during Winter Seasons in Hangzhou

Using the 2015-2018 Hangzhou city PM2.5,PM10,SO2,CO,NO2 and O3 mass concentration data,ERA5 reanalysis data and ground observation data,through the PCT classification method,the objective analysis of the winter air pollution weather situation in Hangzhou was obtained.The results showed that the winter air quality concentration in Hangzhou continued to be high from 2015 to 2018,and the air pollution was the most significant.Through objective classification,it is concluded that the main weather conditions affecting the region in winter are divided into 6 types,namely high pressure control,high pressure bottom control equalizing field,L-shaped high pressure control,high pressure front control equalizing field,low pressure control,low pressure front control Equalizing field.Among them,when high pressure control,high pressure bottom control equalizing field,L high pressure control,low pressure control are affected by local sources,the impact of external sources has a greater impact on the air quality in Hangzhou,and air pollution is prone to occur;before low pressure When the pressure equalization field is controlled by the Ministry and the pressure equalization field is controlled by the high pressure front,the local wind and precipitation in Hangzhou are relatively high,which is not conducive to the accumulation of air pollutants.The probability of occurrence of air pollution is small,and air pollution is not easy to occur.

Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

CurveNet:Curvature-Based Multitask Learning Deep Networks for 3D Object Recognition

In computer vision fields,3D object recognition is one of the most important tasks for many real-world applications.Three-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3D object recognition.In this paper,we propose to use the principal curvature directions of 3D objects(using a CAD model)to represent the geometric features as inputs for the 3D CNN.Our framework,namely CurveNet,learns perceptually relevant salient features and predicts object class labels.Curvature directions incorporate complex surface information of a 3D object,which helps our framework to produce more precise and discriminative features for object recognition.Multitask learning is inspired by sharing features between two related tasks,where we consider pose classification as an auxiliary task to enable our CurveNet to better generalize object label classification.Experimental results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object classification.We further improved the performance of CurveNet by combining two networks with both curvature direction and voxels of a 3D object as the inputs.A Cross-Stitch module was adopted to learn effective shared features across multiple representations.We evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Circulations and Thermodynamic Characteristics of Different Patterns of Rainstorm Processes in the Eastern Foot of Helan Mountain

Based on the observational hourly precipitation data and the European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA5)products from 2006 to 2020,22 rainstorm processes in the eastern foot of Helan Mountain are objectively classified by using the hierarchical clustering method,and the circulation characteristics of different patterns are comparatively analyzed in this study.The results show that the occurrences of rainstorm processes in the eastern foot of Helan Mountain are most closely related to three circulation patterns.PatternsⅠandⅢmainly occur in July and August,with similar zonal circulations in synoptic backgrounds.Specifically,the South Asia high and the western Pacific subtropical high are stronger and more northward than those in normal years.The frontal systems in westerlies are inactive,while the water vapor from the ocean surface in the south is mainly transported to the rainstorm area by the southerly jet stream at 700 h Pa.The dynamic lifting anomalies are relatively weak,the instability of atmospheric stratification is anomalously strong,and thus the localized severe convective rainstorm is more significant.Comparatively,rainstorm processes of patternⅠare accompanied by stronger and deeper ascending motions,and the warm-sector rainstorm is more extreme.PatternⅢshows a stronger and deeper convective instability,accompanied by larger low-level moisture.Rainstorm processes of patternⅡmainly occur in early summer and early autumn,presenting a meridional circulation pattern of high in the east and low in the west in terms of geopotential height.Moreover,the two low-level jets transporting the water vapor northward from the ocean on the east of China encounter with the frontal systems in westerlies,which makes the ascending motion in patternⅡanomalously strong and deep.The relatively weak instability of atmospheric stratification causes weak convection and long-lasting precipitation formed by the confluence of cold air and warm air.This study may help improve rainstorm forecasting in arid regions.

Evaluation of Tactile Comfort of Underwear Fabrics

From the perspective of the tactile comfort of underwear fabrics, 179 kinds of underwear fabrics were selected to test tactile related performance indices using the fabric touch tester(FTT), and the relationship between physical indicators and tactile sensation of different fiber types of underwear fabrics was studied to establish a digital regression model by a stepwise regression method. The experimental results show that fabric fiber composition, compression characteristics, surface friction coefficient, surface roughness amplitude, bending characteristics, and maximum thermal conductivity significantly affect the level of tactile comfort of underwear fabrics, the composition of underwear fabrics has a significant effect on soft touch, and the clustering method and the grading method can effectively rate the level of tactile comfort of underwear fabrics.

How to classify bibliometrics indicators?A thorough investigation of objective classification and its application

Classification of bibliometric indicators is a fundamental issue in information science.Traditionally,the classification is based on subjective classification.This article presents an empirical study on the mathematics journals listed in JCR 2019 by using objective classification methods including cluster analysis,factor analysis,and principal component analysis to classify bibliometric indicators.Different classification results are compared and further interpreted,major finding are:the classification results of objective classification methods share similarities;objective classification helps better comprehend bibliometric indicators;objective classification should be used in combination with subjective classification;cluster analysis performs better in classifying bibliometric indicators than factor analysis and principal component analysis;not all the results of objective classification are meaningful;cluster of indicators has sufficient influence on subsequent evaluation and regression analysis.This study provides a new paradigm for journal classification and indicator analysis.

VFM： Visual Feedback Model for Robust Object Recognition

Object recognition, which consists of classification and detection, has two important attributes for robustness： 1） closeness： detection windows should be as close to object locations as possible, and 2） adaptiveness： object matching should be adaptive to object variations within an object class. It is difficult to satisfy both attributes using traditional methods which consider classification and detection separately; thus recent studies propose to combine them based on confidence contextualization and foreground modeling. However, these combinations neglect feature saliency and object structure, and biological evidence suggests that the feature saliency and object structure can be important in guiding the recognition from low level to high level. In fact, object recognition originates in the mechanism of ＂what＂ and ＂where＂ pathways in human visual systems. More importantly, these pathways have feedback to each other and exchange useful information, which may improve closeness and adaptiveness. Inspired by the visual feedback, we propose a robust object recognition framework by designing a computational visual feedback model （VFM） between classification and detection. In the ＂what＂ feedback, the feature saliency from classification is exploited to rectify detection windows for better closeness; while in the ＂where＂ feedback, object parts from detection are used to match object structure for better adaptiveness. Experimental results show that the ＂what＂ and ＂where＂ feedback is effective to improve closeness and adaptiveness for object recognition, and encouraging improvements are obtained on the challenging PASCAL VOC 2007 dataset.

Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.

Influence of circulation types on temporal and spatial variations of ozone in Beijing

This study analyzes the impact of circulation types(CTs)on ozone(O_(3))pollution in Beijing.The easterly high-pressure(SWW)circulation occurred most frequently(30%;276 day),followed by northwesterly high-pressure(AN)circulation(24.3%;224 day).The SWW type had the highest O_(3) anomaly of+17.28μg/m^(3),which was caused by excellent photochemical reactions,poor diffusion ability and regional transport.Due to the higher humidity and precipitation in the low-pressure type(C),the O_(3) increase(+8.02μg/m^(3))was less than that in the SWW type.Good diffusion/wet deposition and weak formation ability contributed to O_(3) decrease in AN(-12.54μg/m^(3))and northerly high-pressure(ESN)CTs(-12.26μg/m^(3)).The intra-area transport of O_(3) was significant in polluted circulations(SWW-and C-CTs).In addition,higher temperature,radiation and less rainfall also contributed to higher O_(3) in northern Beijing under the SWW type.For the clean CTs(AN and ESN CTs),precursor amount and intra-area transport played a dominant role in O_(3) distribution.Under the northeasterly low-pressure CT,better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O_(3) values in the south than in the north.The higher O_(3) in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O_(3) concentration in previous day.Annual variation in the CTs contributed up to 86.1%of the annual variation in O_(3).About 78%-83%of the diurnal variation in O_(3) resulted from local meteorological factors.

Weight-Edge Convolution Neural Network for Point Clouds Learning

As a kind of flexible three-dimensional geometric data, point clouds can accomplish many challenging tasks so long as the rich information in the geometric topology architecture can be deeply analyzed. On account of that point cloud data is sparse, disordered and rotation-invariant, the success of convolutional neural network in 2 D image cannot be directly reproduced on point cloud. In this paper, we propose WECNN, namely, Weight-Edge Convolution Neural Network, which has an excellent ability to utilize local structural features. As the core of WECNN, a novel convolution operator called WEConv tries to capture structural features by constructing a fixed number of directed graphs and extracting the edge information of the graph to further analyze the local regions of point cloud. Moreover, a weight function is designed for different tasks to assign weights to the edges, so that feature extractions on the edges can be more fine-grained and robust. WECNN gets overall accuracy of 93.8% and mean class accuracy of 91.6% on Model Net40 dataset. At the same time, it gets a mean Io U of 85.5% on Shape Net Part dataset. Results of extensive experiments show that our WECNN outperforms other classification and segmentation approaches on challenging benchmarks.