Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of labelling.However,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background information.Therefore,an intuitive idea is to infer annotations that cover more complete object and background regions for training.To this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent labels.Specifically,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster centres.Next,the same annotations for pixels with similar colours within each kernel neighbourhood was set further.Extensive experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

Multi-Stream Temporally Enhanced Network for Video Salient Object Detection

Video salient object detection(VSOD)aims at locating the most attractive objects in a video by exploring the spatial and temporal features.VSOD poses a challenging task in computer vision,as it involves processing complex spatial data that is also influenced by temporal dynamics.Despite the progress made in existing VSOD models,they still struggle in scenes of great background diversity within and between frames.Additionally,they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration.We propose a multi-stream temporal enhanced network(MSTENet)to address these problems.It investigates saliency cues collaboration in the spatial domain with a multi-stream structure to deal with the great background diversity challenge.A straightforward,yet efficient approach for temporal feature extraction is developed to avoid the accumulative noises and reduce time consumption.The distinction between MSTENet and other VSOD methods stems from its incorporation of both foreground supervision and background supervision,facilitating enhanced extraction of collaborative saliency cues.Another notable differentiation is the innovative integration of spatial and temporal features,wherein the temporal module is integrated into the multi-stream structure,enabling comprehensive spatial-temporal interactions within an end-to-end framework.Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance on five benchmark datasets while maintaining a real-time speed of 27 fps(Titan XP).Our code and models are available at https://github.com/RuJiaLe/MSTENet.

ANALYSIS OF WORKING MODES OF HYBRID EXCITATION DOUBLY SALIENT MOTOR

A more accurate analysis method on working modes is proposed by considering the winding terminal voltage and the eondueting power device as state parameters. For the three-phase hybrid excitation doubly salient machine （HEDSM） motor and its three-phase full-bridge inverter, in the proposed analytical method, all possible working modes are generally listed. Then, with the H_PWM-L_ON control strategy, the working modes are detailed with eorresponding equivalent circuits. Experimental results verify the robustness of the analysis.

THREE-PHASE BRIDGE INVERTER FOR 9kW DOUBLY SALIENT PERMANENT MAGNET MOTOR

The three-phase bridge inverter is used as the converter topology in the power controller for a 9 kW doubly salient permanent magnet （DSPM） motor. Compared with common three-phase bridge inverters, the proposed inverter works under more complicated conditions with different principles for special winding back EMFs, position signals of hall sensors, and the given mode of switches. The ideal steady driving principles of the inverter for the motor are given. The working state with asymmetric winding back EMFs, inaccurate position signals of hall sensors, and the changing input voltage is analyzed. Finally, experimental results vertify that the given anal ysis is correct.

On the priority of salient meanings in English idioms understanding

Giora proposed that general principle of salience： salient comprehension of figurative and meanings are processed first and literal language be governed by a more meaning salience determines the type of processing invoked. According to the Graded Salience Hypothesis, processing familiar metaphors should involve the activation of both their metaphoric and literal meanings, regardless of the type of context in which they are embedded. Processing less familiar metaphors should activate the literal meaning in both types of contexts; however, in the literally biased context, it should be the only one activated. Processing familiar idioms in context biased towards the idiomatic meaning should evoke their figurative meaning almost exclusively, because their figurative meaning is much more salient than their literal meaning. However, processing less familiar idioms in an idiomatic context should activate both their literal and idiomatic meanings because both meanings enjoy similar salience status.

Implementation and Study of a Novel Doubly Salient Structure Starter/Generator System

A new type of double salient starter/generator is presented, which can be used in aircraft Low Voltage Direct Current (LVDC), Variable Speed Constant Frequency (VSCF) and High Voltage Direct Current (HVDC) systems. The operational theory of the motor and generator is analyzed, and corresponding control strategies are given. An 18kW prototype has been implemented to verify the system performance. It is shown that the DSM S/G system possesses simple structure, high efficiency and flexible control. It is ap...

Automatic salient object segmentation using saliency map and color segmentation

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.

Torque Calculation of Five-Phase Synchronous Reluctance Motors With Shifted-Asymmetrical-Salient-Poles Under Saturation Condition

In traditional analytical method(AM),the magnetic saturation is always ignored to simplify the calculation process.However,synchronous reluctance motors(SynRMs)often operate around saturation point to achieve higher torque density.Therefore,a new AM is proposed,in which the saturation of stator iron has been considered.The key of the proposed method includes a saturation factor,and an iterative method is adopted to compute the saturation factor in the SynRM by increasing the air-gap length.Especially,the proposed AM can be applied to a SynRM even with shifted-asymmetrical-salient-poles.In the process of AM,the expression of stator magnetomotive force(MMF)is built firstly.Additionally,the air-gap density including slotting effect and salient-poles is calculated.Then,the rotor MMF under saturation of the stator iron is obtained.Therefore,the precision of the instantaneous torque can be improved significantly.Eventually,by the verification of finite elements method(FEM)and experiments,the torque performance of SynRMs with shifted asymmetrical rotor can be predicted accurately by the proposed AM.

Salient Object Detection from Multi-spectral Remote Sensing Images with Deep Residual Network

alient object detection aims at identifying the visually interesting object regions that are consistent with human perception. Multispectral remote sensing images provide rich radiometric information in revealing the physical properties of the observed objects, which leads to great potential to perform salient object detection for remote sensing images. Conventional salient object detection methods often employ handcrafted features to predict saliency by evaluating the pixel-wise or superpixel-wise contrast. With the recent use of deep learning framework, in particular, fully convolutional neural networks, there has been profound progress in visual saliency detection. However, this success has not been extended to multispectral remote sensing images, and existing multispectral salient object detection methods are still mainly based on handcrafted features, essentially due to the difficulties in image acquisition and labeling. In this paper, we propose a novel deep residual network based on a top-down model, which is trained in an end-to-end manner to tackle the above issues in multispectral salient object detection. Our model effectively exploits the saliency cues at different levels of the deep residual network. To overcome the limited availability of remote sensing images in training of our deep residual network, we also introduce a new spectral image reconstruction model that can generate multispectral images from RGB images. Our extensive experimental results using both multispectral and RGB salient object detection datasets demonstrate a significant performance improvement of more than 10% improvement compared with the state-of-the-art methods.

Oil exploration breakthrough in the Wensu salient, northwest Tarim Basin and its implications

Both the XWD1 and XWD2 wells drilled in 2017 in the Wensu salient, northwest Tarim Basin have achieved high-yield industrial oil flow. Based on the comprehensive research on drilling, oil testing, geochemistry and logging data, in combination with the field surveys, 2 D seismic data processing and interpretation as well as sedimentation and accumulation history comparison, we carefully compared the source conditions, migration channels, reservoir-cap distribution and trapping types in the Wensu salient, and subsequently constructed a reservoir-forming pattern. Though the Wensu salient is lack of source rocks, some drainage systems were widely developed and efficiently connected to adjacent fertile depressions. Due to the moderate Miocene paleogeomorphic conditions in the Wensu salient, the delta and shore-shallow lacustrine beach bar sandy bodies were developed within the Jidike formation, and consequently form widely distributed structural-lithologic traps. The hydrocarbon generation, migration and accumulation mainly happened in the Neogene-Quaternary period, which suggests that the reservoir-forming pattern should be characterized as late-period and compound accumulation. It suggests that, although the border belts in the Tarim Basin might be short of source rocks and structural traps, they are potential to accumulate hydrocarbon in a large scale; the description of efficient hydrocarbon migration channels and structural-lithologic traps is crucial for any successful exploration.

Detection of fabric defects based on frequency-tuned salient algorithm

The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results.

Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

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.

边缘信息增强的显著性目标检测网络

针对显著性目标检测任务中识别结果边缘模糊的问题,提出了一种能够充分利用边缘信息增强边缘像素置信度的新模型。该网络主要有两个创新点:设计三重注意力模块,利用预测图的特点直接生成前景、背景和边缘注意力,并且生成注意力权重的过程不增加任何参数;设计边缘预测模块,在分辨率较高的网络浅层进行有监督的边缘预测,并与网络深层的显著图预测融合,细化了边缘。在6种常用公开数据集上用定性和定量的方法评估了该模型,并且与其他模型进行充分对比,证明设计的新模型能够取得最优的效果。此外,该模型参数量为30.28 M,可以在GTX 1080 Ti显卡上达到31帧·s^(-1)的预测速度。

Development and Analysis of the Magnetic Circuit on Double-Radial Permanent Magnet and Salient-Pole Electromagnetic Hybrid Excitation Generator for Vehicles

With the improvement of vehicles electrical equipment, the existing silicon rectification generator and permanent magnet generator cannot meet the requirement of the electric power consumption of the modern vehicles electrical equipment. It is di cult to adjust the air gap magnetic field of the permanent magnet generator. Consequently, the output voltage is not stable. The silicon rectifying generator has the problems of low e ciency and high failure rate.In order to solve these problems, a new type of hybrid excitation generator is developed in this paper. The developed hybrid excitation generator has a double-radial permanent magnet, a salient-pole electromagnetic combined rotor,and a fractional slot winding stator, where each rotor pole corresponds to 4.5 stator teeth. The equivalent magnetic circuit diagram of permanent magnet rotor and magnetic rotor is established. Magnetic field finite element analysis(FEA) software is used to conduct the modeling and simulation analysis on double-radial permanent magnet magnetic field, salient-pole electro-magnetic magnetic field and hybrid magnetic field. The magnetic flux density mold value diagram and vector diagram are obtained. The diagrams are used to verify the feasibility of this design. The designed electromagnetic coupling regulator controller can ensure the stable voltage export by changing the magnitude and direction of the excitation current to adjust the size of the air gap magnetic field. Therefore, the problem of output voltage instability in the wide speed range and wide load range of the hybrid excitation generator is solved.

Gender Equality:Now a Basic State Strategy——On the Salient Features of the National Program on the Development of Chinese Women(2011-2020)

The National Program on the Development of Chinese Women （2011-2020）, herein- after referred to as the ＂NewProgram,＂ was published in August 2011. This is an important document designed to ensure implementation of the basic state strategy of gender equal- ity and the all-round development of Chinese women. The New Program is a part of China＇s policy program for the protection of human rights. It sets 57 major targets to be attained over the decade. The targets cover seven fields, namely, health, education, economy,

四凸极液冷电磁缓速器数值模拟与实验研究

为解决中重型车辆长下坡时因主制动器频繁工作导致其制动效能严重衰退问题,提出一种四凸极液冷电磁缓速器结构。建立车辆下坡动力学模型分析制动需求,采用等效磁路法对其制动力矩进行计算,使用有限元法对其制动特性进行数值分析。利用分级变论域模糊控制策略结合缓速器进行车辆下坡时的制动控制,使用MATLAB/Simulink建立控制器和整车长下坡制动模型并进行联合仿真。设计了2100 N·m样机,通过台架实验和车载道路实验对样机制动特性进行测试,实际测量值与分析计算值基本一致,平均误差在5%内,在转速为1250 r/min时制动力矩可达2200 N·m,满足中重型车辆制动需求。

基于显著性区域检测的多尺度图像盲复原算法

针对大多数基于先验的盲图像去模糊算法耗时较长和显著边缘结构提取不理想的问题,提出一种基于显著性区域检测的多尺度图像盲复原算法。为了复原出更加清晰的图像,采用由粗略到精细的多尺度交替迭代框架构建图像金字塔。在图像单一尺度方面,首先提取出图像中具有强边缘结构的显著性区域,并对其施加l_(0)范数约束,提出显著映射先验;将显著性映射先验和最大后验概率相结合并引入传统图像去模糊模型中,构造出点扩散函数估算模型,利用半二次分裂算法解决模型的非凸问题;对点扩散函数进行复原时,利用点扩散函数相似度的变化量限制每个尺度中的过渡迭代;对模糊图像和最终估计的点扩散函数进行非盲解卷积,获得复原图像。实验结果表明:与现有的主流去模糊算法相比,新算法在合成数据集和真实数据集中都可以有效抑制振铃和伪影现象,得到了很好的视觉体验,且评价指标均优于对比算法,同时大大缩减了复原时间。

基于特征注意力提纯的显著性目标检测模型

近年来,显著性目标检测技术取得了巨大进展,其中如何选择并有效集成多尺度特征扮演了重要角色。针对现有特征集成方法可能导致的信息冗余问题,提出了一种基于特征注意力提纯的显著性检测模型。首先,在解码器中采用一个全局特征注意力引导模块(GAGM)对带有语义信息的深层特征进行注意力机制处理,得到全局上下文信息;然后,通过全局引导流将其送入解码器各层进行监督训练;最后,利用多尺度特征融合模块(FAM)对编码器提取出的多尺度特征与全局上下文信息进行有效集成,并在网格状特征提纯模块(MFPM)中进行进一步细化,以生成清晰、完整的显著图。在5个公开数据集上进行实验,结果表明,所提模型优于现有的其他显著性检测方法,并且处理速度快,当处理320×320尺寸的图像时,能以30帧以上的速度运行。

微博用户和热点话题间的社交吸引法则研究

[研究目的]探究微博用户和热点话题间社交吸引力的内在作用机制有助于深入理解社交吸引力的决定因素和热门话题的流行成因。[研究方法]以2022年微博平台的7类14个热点话题为研究对象,从用户行为视角构造用户与热点话题之间的吸引力测算公式,从话题、用户、距离、环境属性出发,确定影响社交吸引力的23个指标。采用4种机器学习模型构建吸引力的影响因素模型,利用SHAP模型对结果最好的模型XGBoost进行解释,分析各项特征的贡献大小和相互影响关系。[研究结论]研究发现,用户属性的重要性非常明显,环境属性对社交吸引力基本没有显著贡献。在实践中,必要的高影响媒体转发造势、更多的话题上榜时间和更具有情感倾向的话题名称能带来更大的社交吸引力,帮助塑造与推进有益的热点话题,也为政府引导网络空间话题的健康发展提供决策参考。

基于先验特征聚类的目标检测优化方法

针对显著目标检测问题在没有任何先验信息的情况下,通过特征聚类和紧致性先验方案实现目标检测优化。优化后的方法包括四个步骤:首先采用超像素预处理将图像分割成超像素,以抑制噪声并降低计算复杂度;其次应用改进的虾群聚类算法对颜色特征进行分类;接着利用二维熵来衡量每个簇的紧密度,并构建背景模型;最后以背景区域与其他区域之间的对比度作为显著特征,并通过设计高斯滤波器增强其显著性。为了更好地评价显著目标检测的精度,本文通过多维评价指标进行优劣性实验分析,实验结果表明,文中算法具有较好的实时性与鲁棒性。