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IMAGE RECONSTRUCTION AND object classification IN CT IMAGING SYSTEM

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Nuclear Science and Techniques 1995年第2期6卷 108-112页

作者：张晓明蒋大真卢宋林 InstituteofNuclearResearch theChineseAcademyofSciencesShanghai201800

By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection *** the gray and spstial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms.

关键词： Filter function Backprojection Image reconstruction Fuzzy clustering object classification

High efficient moving object extraction and classification in traffic video surveillance

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Journal of Systems Engineering and Electronics 2009年第4期20卷 858-868页

作者： Li Zhihua Zhou Fan Tian Xiang Chen Yaowu Inst. of Advanced Digital Technology and Instrument Zhejiang Univ. Hangzhou 310027 P. R. China

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.

关键词： background model nonparametric model adaptive single Gaussian model object classification

Two Stream Neural Networks with Traditional CNN and Gabor CNN for object classification

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Two Stream Neural Networks with Traditional CNN and Gabor CN...

Entropy Based Feature Fusion Using Deep Learning for Waste object Detection and classification Model

第37届中国控制会议

作者： Jiakun Li Tian Wang Ming Gao Aichun Zhu Guangcun Shan Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Key Laboratory of Spectral Imaging technology of Chinese Academy of Sciences Beijing Institute of Electronic System Engineering School of Computer Science and Technology Nanjing University of Technology School of Instrumentation Science and Opto-electronics Engineering Beihang University University of Technology of Troyes Institute Charles Delaunay-LM2S-UMR STMR 6279 CNRS

Image classification is still a hot and challenging task in the field of computer vision. With the combination of traditional CNN and Gabor CNN, we designed color-texture convolutional neural networks. We believe that color and texture is the most important feather to describe an object. So the original image, which main consist of color information, and the image processed by Gabor, which contain the texture information, input two stream networks separately is a natural idea. And merging the output of two stream at the end of network to complete the task of classification. We design a simple but effective network structure and test it on Cifar-10, STL-10 and NWPU-RESISC45 dataset. The result shows that the network achieve a well accuracy and proves that our idea is feasible.

关键词： two stream Gabor texture object classification

来源： cnki会议评论

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Computer Systems Science & Engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

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

关键词： object detection object classification waste management deep learning feature fusion

VFM： Visual Feedback Model for Robust object Recognition

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Journal of Computer Science & Technology 2015年第2期30卷 325-339页

作者：王冲黄凯奇 National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

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.

关键词： object recognition object classification object detection visual feedback

Development of vehicle-recognition method on water surfaces using LiDAR data:SPD^(2)(spherically stratified point projection with diameter and distance)

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Defence Technology（防务技术） 2024年第6期36卷 95-104页

作者： Eon-ho Lee Hyeon Jun Jeon Jinwoo Choi Hyun-Taek Choi Sejin Lee The Department of Mechanical Engineering Kongju National University1223-24 Cheonan-daeroCheonan 31080Republic of Korea Ocean System Engineering Research Division Korea Research Institute of Ships and Ocean Engineering(KRISO)Daejeon 34103Republic of Korea The Division of Mechanical&Automotive Engineering Kongju National University1223-24 Cheonan-daeroCheonan 31080Republic of Korea

Swarm robot systems are an important application of autonomous unmanned surface vehicles on water *** monitoring natural environments and conducting security activities within a certain range using a surface vehicle,the swarm robot system is more efficient than the operation of a single object as the former can reduce cost and save *** is necessary to detect adjacent surface obstacles robustly to operate a cluster of unmanned surface *** this purpose,a LiDAR(light detection and ranging)sensor is used as it can simultaneously obtain 3D information for all directions,relatively robustly and accurately,irrespective of the surrounding environmental *** the GPS(global-positioning-system)error range exists,obtaining measurements of the surface-vessel position can still ensure stability during platoon *** this study,a three-layer convolutional neural network is applied to classify types of surface *** aim of this approach is to redefine the sparse 3D point cloud data as 2D image data with a connotative meaning and subsequently utilize this transformed data for object classification ***,we have proposed a descriptor that converts the 3D point cloud data into 2D image *** use this descriptor effectively,it is necessary to perform a clustering operation that separates the point clouds for each *** developed voxel-based clustering for the point cloud ***,using the descriptor,3D point cloud data can be converted into a 2D feature image,and the converted 2D image is provided as an input value to the *** intend to verify the validity of the proposed 3D point cloud feature descriptor by using experimental data in the ***,we explore the feasibility of real-time object classification within this framework.

关键词： object classification Clustering 3D point cloud data LiDAR(light detection and ranging) Surface vehicle

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

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1177-1187页

作者： A.A.M.Muzahid Wanggen Wan Ferdous Sohel Lianyao Wu Li Hou School of Communications and Information Engineering Institute of Smart CityShanghai UniversityShanghai 200444China Discipline of Information Technology Murdoch UniversityMurdoch WA 6150Australia School of Information Engineering Huangshan UniversityHuangshan 245041China

In computer vision fields,3D object recognition is one of the most important tasks for many real-world ***-dimensional convolutional neural networks(CNNs)have demonstrated their advantages in 3D object *** 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 *** framework,namely CurveNet,learns perceptually relevant salient features and predicts object class *** directions incorporate complex surface information of a 3D object,which helps our framework to produce more precise and discriminative features for object *** 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 *** results show that our proposed framework using curvature vectors performs better than voxels as an input for 3D object *** 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 *** evaluated our methods using three publicly available datasets and achieved competitive performance in the 3D object recognition task.

关键词： 3D shape analysis convolutional neural network DNNs object classification volumetric CNN

Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** 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 *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** 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 *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** 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.

关键词： Ship detection deep learning data augmentation object location object classification