Accurate face detection in low-light environments remains a challenging task due to severely degraded visibility of facial features. Conventional cascade-based methods, in which enhancement and detection are applied sequentially, are often found to achieve suboptimal accuracy. Meanwhile, detection only approaches are frequently observed to yield imprecise localization in the absence of explicit illumination adjustment. Although several joint enhancement-detection frameworks have recently been proposed, effective collaboration between the two tasks is still lacking in many cases, limiting overall efficacy. To overcome these limitations, a Value-aware Enhancement and Detection Network (VED-Net) is proposed—an end-to-end dual task architecture designed for simultaneous face enhancement and detection in low-light images. Central to the framework is a Retinex-based V-channel Enhancement and Extraction (VEE) module, through which illumination is improved and refined V-channel features are extracted in the HSV color space. The model is further equipped with complementary enhancement and detection branches that cooperatively utilize the output from the VEE module to enhance feature representation for both tasks. Experimental results demonstrate that significant improvements in both detection accuracy and image quality are achieved by VED-Net, confirming its potential for practical applications in nighttime scenarios.

In large-scale surveillance of urban or rural areas, an effective placement of cameras is critical in maximizing surveillance coverage or minimizing economic cost of cameras. Existing Surveillance Camera Placement (SCP) methods generally focus on physical coverage of surveillance by implicitly assuming uniform distribution of interested targets or objects across all blocks, which is, however, uncommon in real-world scenarios. In this paper, we are the first to propose a target-aware SCP (tSCP) model, which prioritizes optimizing the task based on uneven target densities, allowing cameras to preferentially cover blocks with more interested targets. First, we define target density as the likelihood of interested targets occurring in a block, which is positively correlated with the importance of the block. Second, we combine aerial imagery with a lightweight object detection network to identify target density. Third, we formulate tSCP as an optimization problem to maximize target coverage in surveillance area, and solve this problem with a target-guided genetic algorithm. Our method optimizes the rational and economical utilization of cameras in large-scale video survillance. Compared with the state-of-the-art methods, our tSCP achieves the highest target coverage with a fixed number of cameras (8.31%-14.81% more than its peers), or utilizes the minimum number of cameras to achieve a preset target coverage. Codes are available athttps://github.com/wu-hongxin/tSCP_main.

Single-image desnowing aims at depressing snowflake noises while preserving a clean background. Existing methods usually mask the locations of noises and remove them in RGB color space. In this paper, we rethink this problem by investigating the impacts of color space selection. Theoretical analysis and experiments reveal that the feature of snowflake noises exhibit different distributions in different color spaces. In particular, these noises are barely seen in Hue channel, which inspires us to recover global structure and texture information of the clean background from Hue channel. More low-frequency information is also found in the Hue channel. With these observations, we propose a novel Hybrid-Color-Space-based Desnowing Network (HCSD-Net). The proposed HCSD-Net extracts low-frequency and high-frequency features in Hue channel and RGB color space, respectively. After that, it utilizes a multi-scale fusion module to enhance high-frequency details at a small feature resolution. These details are further used to supervise and supplement the background information. Extensive experiments demonstrate that our proposed HCSD-Net outperforms state-of-the-art methods on various synthetic and real-world desnowing datasets. Codes are available at https://github.com/ttz-rainbow/HCSD-Net.

With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose effectiveness. Ensemble methods have been used for multi-label classification, but few methods consider both the accuracy and diversity of base classifiers. To address the above-mentioned problem, a Weighted Ensemble classification algorithm based on Nearest Neighbors for Multi-Label data stream (WENNML) is proposed. WENNML uses data blocks to train Active candidate Ensemble Classifiers (AEC) and Passive candidate Ensemble Classifiers (PEC). The base classifiers of AEC and PEC are dynamically updated using geometric and diversity weighting methods. When the difference value between the number of current instances and the number of warning instances reaches the passive warning value, the algorithm selects the optimal base classifiers from AEC and PEC according to the subset accuracy and hamming score and puts them into the predictive ensemble classifiers. Experiments are carried out on 12 kinds of datasets with 9 comparison algorithms. The results show that WENNML achieves the best average rankings among the four evaluation metrics.

Abstract At present, concept drift in the nonstationary data stream is showing trends with different speeds and different degrees of severity, which has brought great challenges to many fields like data mining and machine learning. In the past two decades, a lot of methods dedicated to handling concept drift in the nonstationary data stream have emerged. A novel perspective is proposed to classify these methods, and the current concept drift handling methods are comprehensively explained from the active handling methods and the passive handling methods. In particular, active handling methods are analyzed from the perspective of handling one specific type of concept drift and handling multiple types of concept drift, and passive handling methods are analyzed from the perspective of single learner and ensemble learning. Many concept drift handling methods in this survey are analyzed and summarized in terms of the comparing algorithms, learning model, applicable drift type, advantages, and disadvantages of the algorithms. Finally, further research directions are given, including the active and passive mixing methods, class imbalance, the existence of novel class in the data stream, and the noise in the data stream.

This paper presents an all-coefficient adaptive control method based on a characteristic model. This method consists of three parts: all-coefficient adaptive control, golden-section adaptive control, and characteristic modeling. It is applied to more than 400 systems of nine kinds of engineering plants. Two such practical examples are introduced in detail to illustrate the concrete steps to apply this method to practical engineering problems. This method has many advantages. It is simple to use, convenient to adjust and test, and able to generate highly robust systems. In certain sense, the method has solved the three principal problems in practical applications, i.e., it can guarantee the stability of a closed-loop system in the start period of a transient process or when parameter estimates have not converged to their "true values," the number of parameters to be estimated is small, and the number of parameters to be regulated online is also small

It is well recognized that blind deconvolution is a severely ill-posed problem and proper constraints on the image and the system point spread function (PSF) should be applied to counteract the ill-posedness. In this paper we investigate a novel PSF constraint, the monotonicity, which means the value of the PSF monotonically decreases (or does not increase) from the center of its support. We regard the monotonicity as a common property of many simplified but well accepted PSF models, such as the geometrical model of defocus, the Gaussian model and the synthetic model in astronomical imaging. The property is utilized as a PSF constraint in a novel iterative blind deconvolution algorithm RL-CLSE. Experiments on real microscopic data show that the proposed constraint can significantly improve the quality and stability of blind deconvolution.