The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective s...The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.展开更多
Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occ...Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.展开更多
Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image ...Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.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 paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sli...This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.展开更多
High performance of parallel computing on a message-passing multicomputer System relies on the balance of the workloads located on the processing elements of the System and the minimum communication ovcrheads among th...High performance of parallel computing on a message-passing multicomputer System relies on the balance of the workloads located on the processing elements of the System and the minimum communication ovcrheads among them. Mapping is the technology to partition the problem domain wellbalanced into multiple distinct execution tasks based on some measures. In mapping, a good objective function is the criterion to guarantce the distinct execution tasks equitable. In this paper, we evaluate five categories of those existed objective functions with three different problem subjects using experiments and find an objective function is much suitable for all kinds of problems.展开更多
基金supported by the National Natural Science Foundation of China(No.52174021)Key Research and Develop-ment Project of Hainan Province(No.ZDYF2022GXJS 003).
文摘The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.
基金a grant from the Basic Science Research Program through the National Research Foundation(NRF)(2021R1F1A1063634)funded by the Ministry of Science and ICT(MSIT)Republic of Korea.This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Group Funding program Grant Code(NU/RG/SERC/12/6).
文摘Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.
基金The National Natural Science Foundation of China under contract No.41201328the Science Foundation for Young Scholars of China’s State Oceanic Administration under contract No.2013415
文摘Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.
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 the National Natural Science Foundation of China(No.11172197 and No.11332008)a key-project grant from the Natural Science Foundation of Tianjin(No.010413595)
文摘This paper presents a numerical algorithm tuning aircraft landing gear control system with three objectives,including reducing relative vibration, reducing hydraulic strut force and controlling energy consumption. Sliding mode control is applied to the vibration control of a simplified landing gear model with uncertainty. A two-stage generalized cell mapping algorithm is applied to search the Pareto set with gradient-free scheme. Drop test simulations over uneven runway show that the vibration and force interaction can be considerably reduced, and the Pareto optimum form a tight range in time domain.
基金Supported by the National Natural Science Foundation of China(Nos.11172197,11332008 and 11572215)the Natural Science Foundation of Tianjin through a key-project Grant(12JCZDJC30400)the UC MEXUS-CONACy T through the project Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-objective Optimization Algorithms
文摘High performance of parallel computing on a message-passing multicomputer System relies on the balance of the workloads located on the processing elements of the System and the minimum communication ovcrheads among them. Mapping is the technology to partition the problem domain wellbalanced into multiple distinct execution tasks based on some measures. In mapping, a good objective function is the criterion to guarantce the distinct execution tasks equitable. In this paper, we evaluate five categories of those existed objective functions with three different problem subjects using experiments and find an objective function is much suitable for all kinds of problems.
