In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted...In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).展开更多
To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level ...To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.展开更多
Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregul...Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.展开更多
A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not on...A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not only causes a washed-out effect, but also blocks. To solve these drawbacks, this paper derives an optimal global equalization function with variable size block based local contrast enhancement. The optimal equalization function makes it possible to get a good quality image through the global contrast enhancement. The variable size block segmentation is firstly exeoated using intensity differences as a measure of similarity. In the second step, the optimal global equalization function is obtained from the enhanced contrast image having variable size blocks. Conformed experiments have showed that the proposed algorithm produces a visually comfortable result image.展开更多
In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further...In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp edges could be obtained directly from deep convolutional neural networks. We propose to learn edge maps as the representation of local contrast with a novel local contrast loss. The local contrast is optimized in a stochastic way to focus on specific edge directions. Experiments show that the edge detection network trained with local contrast loss achieves a high accuracy comparable to previous methods and dramatically improves the crispness. We also present several applications of the crisp edges, including image completion, image retrieval, sketch generation, and video stylization.展开更多
To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(L...To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.展开更多
The important effects of local land-sea thermodynamic contrast between the South China Sea (SCS) and Indochina Peninsula on SCS summer monsoon onset are preliminarily studied by using two sets of SSTA tests and two id...The important effects of local land-sea thermodynamic contrast between the South China Sea (SCS) and Indochina Peninsula on SCS summer monsoon onset are preliminarily studied by using two sets of SSTA tests and two ideal tests in s-p regional climate model. The result shows that warm SST in the SCS in winter and spring is favorable for the formation of monsoon circulation throughout all levels of the atmosphere over the sea, which hastens the onset of SCS summer monsoon. The effects of cold SST are generally the opposite. The local land-sea contrast in the SCS is one of the possible reasons for SCS summer monsoon onset. Superposed upon large-scale land-sea thermodynamic differences, it facilitates the formation of out-breaking onset characteristics of SCS summer monsoon in the SCS area.展开更多
An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution func...An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to avoid noise overenhancement and ringing artifacts while improving the detail contrast with less computational burden. The effectiveness of our method is demonstrated with radiological images and compared with other algorithms.展开更多
基金supported by the National Natural Science Foundation of China (No.U1833203),the National Natural Science Foundation of China (No.62301036)the Aviation Science Foundation (No.2020Z019055001)China Postdoctoral Science Foundation Funded Project (No.2022M720446)。
文摘In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).
文摘To overcome the shortcomings of 1 D and 2 D Otsu’s thresholding techniques, the 3 D Otsu method has been developed.Among all Otsu’s methods, 3 D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3 D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image;it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional1 D Otsu, 2 D Otsu and 3 D Otsu methods, as evident from the objective and subjective evaluations.
基金supported by the National Natural Science Foundation of China under Grant 62003247, Grant 62075169, and Grant 62061160370。
文摘Infrared(IR) small target detection is one of the key technologies of infrared search and track(IRST)systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure(PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously.With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker(RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.
文摘A conventional global contrast enhancement is difficult to apply in various images because image quality and contrast enhancement are dependent on image characteristics largely. And a local contrast enhancement not only causes a washed-out effect, but also blocks. To solve these drawbacks, this paper derives an optimal global equalization function with variable size block based local contrast enhancement. The optimal equalization function makes it possible to get a good quality image through the global contrast enhancement. The variable size block segmentation is firstly exeoated using intensity differences as a measure of similarity. In the second step, the optimal global equalization function is obtained from the enhanced contrast image having variable size blocks. Conformed experiments have showed that the proposed algorithm produces a visually comfortable result image.
基金supported by the National Natural Science Foundation of China under Grant No. 61832016.
文摘In recent years, the accuracy of edge detection on several benchmarks has been significantly improved by deep learning based methods. However, the prediction of deep neural networks is usually blurry and needs further post-processing including non-maximum suppression and morphological thinning. In this paper, we demonstrate that the blurry effect arises from the binary cross-entropy loss, and crisp edges could be obtained directly from deep convolutional neural networks. We propose to learn edge maps as the representation of local contrast with a novel local contrast loss. The local contrast is optimized in a stochastic way to focus on specific edge directions. Experiments show that the edge detection network trained with local contrast loss achieves a high accuracy comparable to previous methods and dramatically improves the crispness. We also present several applications of the crisp edges, including image completion, image retrieval, sketch generation, and video stylization.
文摘To address the problems of missing inside and incomplete edge contours in camouflaged target detection results,we propose a camouflaged moving target detection algorithm based on local minimum difference constraints(LMDC).The algorithm first uses the mean to optimize the initial background model,removes the stable background region by global comparison,and extracts the edge point set in the potential target region so that each boundary point(seed)grows along the center of the target.Finally,we define the minor difference constraints term,combine the seed path and the target space consistency,and calculate the attributes of each pixel in the potential target area to realize camouflaged moving target detection.The algorithm of this paper is verified based on a public data sofa video and test videos and compared with the five classic algorithms.The experimental results show that the proposed algorithm yields good results based on integrity,accuracy,and a number of objective evaluation indexes,and its overall performance is better than that of the compared algorithms.
基金National Natural Science Foundation of China (40175021 40233037)
文摘The important effects of local land-sea thermodynamic contrast between the South China Sea (SCS) and Indochina Peninsula on SCS summer monsoon onset are preliminarily studied by using two sets of SSTA tests and two ideal tests in s-p regional climate model. The result shows that warm SST in the SCS in winter and spring is favorable for the formation of monsoon circulation throughout all levels of the atmosphere over the sea, which hastens the onset of SCS summer monsoon. The effects of cold SST are generally the opposite. The local land-sea contrast in the SCS is one of the possible reasons for SCS summer monsoon onset. Superposed upon large-scale land-sea thermodynamic differences, it facilitates the formation of out-breaking onset characteristics of SCS summer monsoon in the SCS area.
基金the National Natural Science Foundation of China(No:3 963 0 1 1 0 ) the National Key Technologies R&D Programme under Con-tract96-92 0 -1 2 -0 1
文摘An adaptive contrast enhancement (ACE) algorithm is presented in this paper, in which the contrast gain is determined by mapping the local standard deviation (LSD) histogram of an image to a Gaussian distribution function. The contrast gain is nonlinearly adjusted to avoid noise overenhancement and ringing artifacts while improving the detail contrast with less computational burden. The effectiveness of our method is demonstrated with radiological images and compared with other algorithms.
文摘具有混合记忆的自步对比学习(Self-paced Contrastive Learning,SpCL)通过集群聚类生成不同级别的伪标签来训练网络,取得了较好的识别效果,然而该方法从源域和目标域中捕获的行人数据之间存在典型的分布差异,使得训练出的网络不能准确区别目标域和源域数据域特征。针对此问题,提出了双分支动态辅助对比学习(Dynamic Auxiliary Contrastive Learning,DACL)框架。该方法首先通过动态减小源域和目标域之间的局部最大平均差异(Local Maximum Mean Discrepancy,LMMD),以有效地学习目标域的域不变特征;其次,引入广义均值(Generalized Mean,GeM)池化策略,在特征提取后再进行特征聚合,使提出的网络能够自适应地聚合图像的重要特征;最后,在3个经典行人重识别数据集上进行了仿真实验,提出的DACL与性能次之的无监督域自适应行人重识别方法相比,mAP和rank-1在Market1501数据集上分别增加了6.0个百分点和2.2个百分点,在MSMT17数据集上分别增加了2.8个百分点和3.6个百分点,在Duke数据集上分别增加了1.7个百分点和2.1个百分点。
