How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family ...How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.展开更多
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
Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an...Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.展开更多
基于华南地区176个国家级自动气象站资料以及1981~2020年ECMWF ERA5再分析资料,采用区域性极端事件的客观识别方法(OITREE)、合成分析等方法,本文研究了华南地区区域性极端降水事件的时空分布特征,并分析了事件偏多年及偏少年的大尺度...基于华南地区176个国家级自动气象站资料以及1981~2020年ECMWF ERA5再分析资料,采用区域性极端事件的客观识别方法(OITREE)、合成分析等方法,本文研究了华南地区区域性极端降水事件的时空分布特征,并分析了事件偏多年及偏少年的大尺度环流特征。主要结论如下:区域性极端降水事件的频次在年际尺度上的周期变化较为明显,并具有较明显的月变化特征,高发时段为5~6月;在极端强度及影响范围上,华南地区大部分区域性极端降水事件强度约130 mm d^(-1),较少事件强度超出320 mm d^(-1),且区域性极端降水事件的影响范围呈显著上升趋势(约310 km^(2)a^(-1));在事件的综合强度上,综合指数Z呈现显著的上升趋势[0.05(10 a)^(-1)],表明事件强度呈现显著增加的趋势;在大湾区及广东北部,区域性极端降水事件的累计降水及其对总降水的贡献呈显著上升趋势,而在广西南部地区,两者呈下降趋势;在事件偏多年,华南地区存在显著的西南风水汽输送及整层水汽通量强辐合的特征,而在事件偏少年,华南地区具有整层水汽通量辐合偏弱的特征;一般降水日,850 hPa上华南地区位于弱偏东南风区,区域性极端降水事件降水日,华南地区位于气旋性环流的东南部,受到明显的西南风风速大值带影响。展开更多
区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然...区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然后,建立碳排放量最少和运行成本最小的多目标优化模型,引入碳排放惩罚因子,将多目标转换为单目标进行求解;最后,通过实际RIES进行仿真验证,仿真结果表明所提方法的准确性与有效性。所建模型可以很好地兼顾系统的环保性和经济性,能够更好地处理不确定性,实现系统的经济优化运行。展开更多
基金supported by National High Technology Research and Development Program of China (863 Program)(No.2007AA01Z416)National Natural Science Foundation of China (No.60773056)+1 种基金Beijing New Star Project on Science and Technology (No.2007B071)Natural Science Foundation of Liaoning Province of China (No.20052184)
文摘How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of ＂TREC Video Retrieval Evaluation 2005 （TRECVID2005）＂. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.
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funded by Huanggang Normal University,China,Self-type Project of 2021(No.30120210103)and 2022(No.2042021008).
文摘Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.
文摘基于华南地区176个国家级自动气象站资料以及1981~2020年ECMWF ERA5再分析资料,采用区域性极端事件的客观识别方法(OITREE)、合成分析等方法,本文研究了华南地区区域性极端降水事件的时空分布特征,并分析了事件偏多年及偏少年的大尺度环流特征。主要结论如下:区域性极端降水事件的频次在年际尺度上的周期变化较为明显,并具有较明显的月变化特征,高发时段为5~6月;在极端强度及影响范围上,华南地区大部分区域性极端降水事件强度约130 mm d^(-1),较少事件强度超出320 mm d^(-1),且区域性极端降水事件的影响范围呈显著上升趋势(约310 km^(2)a^(-1));在事件的综合强度上,综合指数Z呈现显著的上升趋势[0.05(10 a)^(-1)],表明事件强度呈现显著增加的趋势;在大湾区及广东北部,区域性极端降水事件的累计降水及其对总降水的贡献呈显著上升趋势,而在广西南部地区,两者呈下降趋势;在事件偏多年,华南地区存在显著的西南风水汽输送及整层水汽通量强辐合的特征,而在事件偏少年,华南地区具有整层水汽通量辐合偏弱的特征;一般降水日,850 hPa上华南地区位于弱偏东南风区,区域性极端降水事件降水日,华南地区位于气旋性环流的东南部,受到明显的西南风风速大值带影响。
文摘区域综合能源系统(regional integrated energy system,RIES)含有风电、光伏等多种能源、冷热电负荷和蓄电池,具有提升可再生能源利用率等优势。首先,考虑风光出力的不确定性,构建多面体不确定集的鲁棒优化模型并对不确定性进行处理;然后,建立碳排放量最少和运行成本最小的多目标优化模型,引入碳排放惩罚因子,将多目标转换为单目标进行求解;最后,通过实际RIES进行仿真验证,仿真结果表明所提方法的准确性与有效性。所建模型可以很好地兼顾系统的环保性和经济性,能够更好地处理不确定性,实现系统的经济优化运行。
