Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current frameworks for oriented detection modules are co...Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment inconsistencies.To overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object detection.The ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter efficiency.These improvements empower the model to accurately detect objects across varying scales.The ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box coherence.This approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection accuracy.Comprehensive evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection models.These findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.展开更多
Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging du...Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.展开更多
In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, an...In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.展开更多
The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficien...The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.展开更多
An object oriented data modelling in computer aided design (CAD) databases is focused. Starting with the discussion of data modelling requirements for CAD applications, appropriate data modelling features are introdu...An object oriented data modelling in computer aided design (CAD) databases is focused. Starting with the discussion of data modelling requirements for CAD applications, appropriate data modelling features are introduced herewith. A feasible approach to select the “best” data model for an application is to analyze the data which has to be stored in the database. A data model is appropriate for modelling a given task if the information of the application environment can be easily mapped to the data model. Thus, the involved data are analyzed and then object oriented data model appropriate for CAD applications are derived. Based on the reviewed object oriented techniques applied in CAD, object oriented data modelling in CAD is addressed in details. At last 3D geometrical data models and implementation of their data model using the object oriented method are presented.展开更多
This paper discusses the design concept and method about window based and object oriented Graphic User Interface(GUI),and describes the definition of each class in detail. It is developed with Watcom C ++ in...This paper discusses the design concept and method about window based and object oriented Graphic User Interface(GUI),and describes the definition of each class in detail. It is developed with Watcom C ++ in DOS environment.The GUI can be redeveloped conveniently and effectively by users.It consists of window,popup menu,icon,button and other components.展开更多
An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, ...An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.展开更多
In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentati...In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.展开更多
The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set u...The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set up adaptive C2 teams. In this paper, the relational problems about distributed C2 organizational structure adaptation are discussed, and the methodology for team decision making design based on the object oriented technique is studied.展开更多
Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of obje...Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.展开更多
Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to ...Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.展开更多
This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regio...This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.展开更多
While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In t...While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.展开更多
Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize th...Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.展开更多
A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2...A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.展开更多
This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small ...This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.展开更多
Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a sof...Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a software development framework acting as compiler, cross-project linker and data fetcher, which allow hot-swaps in order to compare various versions of software under test. The flexibility fostered by IRIS allowed modular exchange of software libraries among developers, making it a powerful development tool. The IRIS platform used input data ROOT-ntuples [3];however a new data model is sought, in line with the facilities offered by IRIS. The schematic of a possible new data structuring—as a user implemented object oriented data base, is presented.展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
Recently automotive nets are adopted to solve increasing problems in automotive electronic systems.Technologies of automotive local area network from CAN and LIN can solve the problems of the increasing of wire bunch ...Recently automotive nets are adopted to solve increasing problems in automotive electronic systems.Technologies of automotive local area network from CAN and LIN can solve the problems of the increasing of wire bunch weight and lack in module installation space.However,the multilayer automotive nets software becomes more and more complex,and the development expense is difficult to predict and to keep in check.In this paper,the modeling method of hierarchical automotive nets and the substitution operation based on object-oriented colored Petri net(OOCPN) are proposed.The OOCPN model which analyzes the software structure and validates the collision mechanism of CAN/LIN bus can speed the automobile system development.First,the subsystems are divided and modeled by object-oriented Petri net(OOPN).According to the sets of message sharing relations,the message ports among them are set and the communication gate transitions are defined.Second,the OOPN model is substituted step by step until the inner objects in the automotive body control modules(BCM) are indivisible and colored by colored Petri net(CPN).And the color subsets mark the node messages for the collision mechanism.Third,the OOCPN model of the automotive body CAN/LIN nets is assembled,which keeps the message sets and the system can be expanded.The proposed model is used to analyze features of information sharing among the objects,and it is also used to describe each subsystem real-time behavior of processing messages and implemental device controllers operating,and puts forward a reasonable software framework for the automotive body control subsystem.The research can help to design the communication model in the automotive body system effectively and provide a convenient and rapid way for developing the logical hierarchy software.展开更多
Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the...Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the ＂gappiness＂ or ＂emptiness＂ characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.展开更多
基金supported by the Key Research and Development Program of Shaanxi Province(2024GX-YBXM-010).
文摘Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object detection.Current frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment inconsistencies.To overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object detection.The ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter efficiency.These improvements empower the model to accurately detect objects across varying scales.The ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box coherence.This approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection accuracy.Comprehensive evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection models.These findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.
基金National Natural Science Foundation of China(Grant Nos.62005049 and 62072110)Natural Science Foundation of Fujian Province(Grant No.2020J01451).
文摘Accurate segmentation of camouflage objects in aerial imagery is vital for improving the efficiency of UAV-based reconnaissance and rescue missions.However,camouflage object segmentation is increasingly challenging due to advances in both camouflage materials and biological mimicry.Although multispectral-RGB based technology shows promise,conventional dual-aperture multispectral-RGB imaging systems are constrained by imprecise and time-consuming registration and fusion across different modalities,limiting their performance.Here,we propose the Reconstructed Multispectral-RGB Fusion Network(RMRF-Net),which reconstructs RGB images into multispectral ones,enabling efficient multimodal segmentation using only an RGB camera.Specifically,RMRF-Net employs a divergentsimilarity feature correction strategy to minimize reconstruction errors and includes an efficient boundary-aware decoder to enhance object contours.Notably,we establish the first real-world aerial multispectral-RGB semantic segmentation of camouflage objects dataset,including 11 object categories.Experimental results demonstrate that RMRF-Net outperforms existing methods,achieving 17.38 FPS on the NVIDIA Jetson AGX Orin,with only a 0.96%drop in mIoU compared to the RTX 3090,showing its practical applicability in multimodal remote sensing.
文摘In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.
文摘The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.
文摘An object oriented data modelling in computer aided design (CAD) databases is focused. Starting with the discussion of data modelling requirements for CAD applications, appropriate data modelling features are introduced herewith. A feasible approach to select the “best” data model for an application is to analyze the data which has to be stored in the database. A data model is appropriate for modelling a given task if the information of the application environment can be easily mapped to the data model. Thus, the involved data are analyzed and then object oriented data model appropriate for CAD applications are derived. Based on the reviewed object oriented techniques applied in CAD, object oriented data modelling in CAD is addressed in details. At last 3D geometrical data models and implementation of their data model using the object oriented method are presented.
文摘This paper discusses the design concept and method about window based and object oriented Graphic User Interface(GUI),and describes the definition of each class in detail. It is developed with Watcom C ++ in DOS environment.The GUI can be redeveloped conveniently and effectively by users.It consists of window,popup menu,icon,button and other components.
基金Project(2009CB226107)supported by the National Basic Research Program of China
文摘An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572127）, the Development Foundation of Shanghai Municipal Commission of Education （Grant No.05AZ43）, and the Shanghai Leading Academic Discipline Project （Grant No.T0102）
文摘In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector （MV） field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.
文摘The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set up adaptive C2 teams. In this paper, the relational problems about distributed C2 organizational structure adaptation are discussed, and the methodology for team decision making design based on the object oriented technique is studied.
文摘Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.
基金supported in part by the National Key R&D Program of China(2017YFB0502904)the National Science Foundation of China(61876140)。
文摘Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.
基金supported by the National Natural Science Foundation of China(61233010 61305106)+2 种基金the Shanghai Natural Science Foundation(17ZR1409700 18ZR1415300)the basic research project of Shanghai Municipal Science and Technology Commission(16JC1400900)
文摘This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.
文摘While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.
基金This work was supported by National Natural Science Foundation of China (No.60372066)
文摘Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.
文摘A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation(MSS)image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80%,89%and 80%of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.
基金Supported by Guangdong Natural Science Foundation(No.011628)
文摘This letter presents an efficient and simple image segmentation method for semantic object spatial segmentation. First, the image is filtered using contour-preserving filters. Then it is quasi-flat labeled. The small regions near the contour are classified as uncertain regions and are eliminated by region growing and merging. Further region merging is used to reduce the region number. The simulation results show its efficiency and simplicity. It can preserve the semantic object shape while emphasize on the perceptual complex part of the object. So it conforms to the human visual perception very well.
文摘Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a software development framework acting as compiler, cross-project linker and data fetcher, which allow hot-swaps in order to compare various versions of software under test. The flexibility fostered by IRIS allowed modular exchange of software libraries among developers, making it a powerful development tool. The IRIS platform used input data ROOT-ntuples [3];however a new data model is sought, in line with the facilities offered by IRIS. The schematic of a possible new data structuring—as a user implemented object oriented data base, is presented.
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
基金supported by National Natural Science Foundation of China (Grant No. 60873003)
文摘Recently automotive nets are adopted to solve increasing problems in automotive electronic systems.Technologies of automotive local area network from CAN and LIN can solve the problems of the increasing of wire bunch weight and lack in module installation space.However,the multilayer automotive nets software becomes more and more complex,and the development expense is difficult to predict and to keep in check.In this paper,the modeling method of hierarchical automotive nets and the substitution operation based on object-oriented colored Petri net(OOCPN) are proposed.The OOCPN model which analyzes the software structure and validates the collision mechanism of CAN/LIN bus can speed the automobile system development.First,the subsystems are divided and modeled by object-oriented Petri net(OOPN).According to the sets of message sharing relations,the message ports among them are set and the communication gate transitions are defined.Second,the OOPN model is substituted step by step until the inner objects in the automotive body control modules(BCM) are indivisible and colored by colored Petri net(CPN).And the color subsets mark the node messages for the collision mechanism.Third,the OOCPN model of the automotive body CAN/LIN nets is assembled,which keeps the message sets and the system can be expanded.The proposed model is used to analyze features of information sharing among the objects,and it is also used to describe each subsystem real-time behavior of processing messages and implemental device controllers operating,and puts forward a reasonable software framework for the automotive body control subsystem.The research can help to design the communication model in the automotive body system effectively and provide a convenient and rapid way for developing the logical hierarchy software.
基金supported by the National Natural Science Foundation of China (30671212)
文摘Normalized Difference Vegetation Index （NDVI） is a very useful feature for differentiating vegetation and non-vegetation in remote sensed imagery. In the light of the function of NDVI and the spatial patterns of the vegetation landscapes, we proposed the lacunarity texture derived from NDVI to characterize the spatial patterns of vegetation landscapes concerning the ＂gappiness＂ or ＂emptiness＂ characteristics. The NDVI-based lacunarity texture was incorporated into object-oriented classification for improving the identification of vegetation categories, especially Torreya which was the targeted tree species in the present research. A three-level hierarchical network of image objects was defined and the proposed texture was integrated as potential sources of information in the rules base. A knowledge base of rules created by classifier C5.0 indicated that the texture could potentially be applied in object-oriented classification. It was found that the addition of such texture improved the identification of every vegetation category. The results demonstrated that the texture could characterize the spatial patterns of vegetation structures, which could be a promising approach for vegetation identification.
