Aurora is the most visible manifestation of the sun’s effect on Earth. The ground-based all-sky imager (ASI) can observe a wealth of multiscale morphological features. Auroral image classification is an important tool for studying magnetospheric regimes and dynamic activities of aurora. Previous studies of automated auroral image classification focused more on auroral large-scale features across the entire image, ignoring small-scale auroral structures. In this letter, we introduce an object detection approach to investigate the small-scale features of auroral morphology. Since the small-scale auroral structures are nonrigid, morphologically diverse, and undefined boundaries, pixel-level labeling is labor-intensive and error-prone for human experts. Therefore, a weakly supervised object detection method for auroral vortexes is proposed in the absence of pixel-level annotations. We first perform global semantic identification and coarse localization using image-level labels as supervision. Considering the motion properties of vortexes, the global and local semantic information in a spatiotemporal volume is leveraged as semantic and location continuity constraints to generate high-confidence pseudo-labels. The experiments demonstrate that the proposed method can identify the vortexes more accurately. The method can retrieve small-scale auroral events in the aurora image dataset, allowing the study of the correlation of multiscale auroral events to be carried out.

The Possibilistic c-means clustering (PCM) is an important unsupervised pattern recognition method. However, it is still faced with huge challenges in clustering multidimensional data with multiple characteristics, such as imbalanced sample sizes, imbalanced feature components, noise and outlier corruption, and the sparse distribution of small targets in the feature space caused by the “curse of dimensionality”. In view of this, this paper proposes a possibilistic c-means clustering algorithm based on the Mahalanobis-Kernel Distance and the suppressed competitive learning strategy. To begin with, the Mahalanobis-Kernel Distance combined with the absolute attribute of possibilistic memberships is proposed to enhance the intra-class compactness of small targets with sparse distribution and feature imbalance. In addition, to overcome the inherent coincident clustering problem caused by possibilistic memberships, the “suppressed competitive learning” mechanism based on the Mahalanobis-Kernel distance is designed to generate cluster cores and correct memberships of objects located within the cluster cores, thus guiding purposefully the clustering process. Furthermore, spatial information is introduced by the membership filtering scheme to improve the segmentation effect of color images with small targets and noise injection. Experimental results show that the algorithm in this paper can achieve better clustering and segmentation performance than several state-of-the-art fuzzy clustering methods for color images with imbalanced sizes and features, and noise injection.

Multiobjective clustering algorithms (MOCAs) are becoming increasingly popular with the merit of segmenting images from multiple perspectives. The performances of MOCAs highly depend on their fitness functions. However, most existing MOCAs adopt one pair of complementary fitness functions, which always measure intra-class compactness and inter-class separation, respectively. This may result in insufficient ability to recognize and mine feature structures from complex images. Moreover, information within color images, such as region information and uncertain information, can barely receive enough attention in MOCAs. To resolve these problems, we propose a reliable region information driven Kriging-assisted multiobjective rough fuzzy clustering algorithm (RRI-KMRFC). Firstly, a reliability-based region information extraction strategy (RRIES) is designed to obtain reliable image information with satisfactory regional homogeneity and abundant image details. Secondly, the derived region information is used to construct three complementary fitness functions, focusing on rough intra-class compactness, rough inter-class separation, and regional consistency, respectively. Such fitness functions can effectively identify the clustering structure, maintain contour details, and characterize uncertain information from color images. To efficiently optimize the proposed functions, an incremental Kriging-assisted evolutionary framework is presented to decrease the expensive function evaluations in which an improved infill sampling strategy is devised to assist in finding unexplored areas in the decision space. Finally, a rough fuzzy clustering validity index with reliable region information is proposed to select the optimal trade-off solution. Experiments performed on Berkeley and Weizmann images confirm the effectiveness and robustness of RRI-KMRFC.

Abstract There are still some challenges in the task of single image rain removal, such as artefact remnant, background over‐smooth, and increasingly complex and heavy‐weight network architecture. Especially too heavy‐weight network to fit outdoor detection devices or mobile devices. To address the above challenges, we propose a lightweight single image Deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The proposed network consists of five blocks and two convolution operations, where each block consists of a dilation convolution module and a convolutional block attention module (CBAM). Specifically, visual saliency module CBAM is used for accurate localization of rain streak, and further the combinations of dilated convolution with CBAM is used to extract feature maps of rain streaks faithfully, which is able to remove artefact remnant while maintaining background details. A good tradeoff is presented between the network's weight size and effect of rain removal. Specifically, with only 48,268 parameters, the proposed model can achieve a guaranteed performance. Extensive experiments on a few typical rainy scenarios on synthetic and real‐world datasets have demonstrated that to achieve the same level of performance, the proposed method has far smaller size than most of the baselines under both qualitative and quantitative analyses.

The research proposes an innovated structure of the density map-based crowd counting network augmented by information entropy. The network comprises of a front-end network to extract features and a back-end network to generate density maps. In order to validate the assumption that the entropy can boost the accuracy of density map generation, a multi-scale entropy map extraction process is imported into the front-end network along with a fine-tuned convolutional feature extraction process, In the back-end network, extracted features are decoded into the density map with a multi-column dilated convolution network. Finally, the decoded density map can be mapped as the estimated counting number. Experimental results indicate that the devised network is capable of accurately estimating the count in extremely high crowd density. Compared to similar structured networks which don’t adapt entropy feature, the proposed network exhibits higher performance. This result proves the feature of information entropy is capable of enhancing the efficiency of density map-based crowd counting approaches.

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper and lower approximations of rough clusters, which may bring a great effect on the clustering performance. When applied to image segmentation, rough clustering is always sensitive to the initialized cluster centers and image noise. Furthermore, only one clustering criterion is considered in rough clustering, which cannot satisfy diverse practical requirements. To handle these issues, a particle competitive mechanism based multiobjective rough clustering algorithm (PCM-MORCA) for image segmentation is proposed. First, a rough intraclass compactness function considering the nonlocal spatial information derived from an image is constructed to overcome the sensitivity to image noise. Next, the constructed rough intraclass compactness function and an interclass separation function are optimized simultaneously to make cluster centers meet diverse segmentation requirements. Then, an adaptive threshold determination mechanism by which the threshold adaptively varies with the clustered data is presented to well determine the upper and lower approximations of rough clusters. After that, to effectively search appropriate cluster centers, a novel pair competition-based particle weight updating strategy is designed for multiobjective particle swarm optimization by improving the elite particle selection and particle update. Finally, a rough clustering index with the nonlocal spatial information is constructed for selecting the optimal solution for PCM-MORCA. Segmentation experiments on Berkeley and magnetic resonance images reveal that PCM-MORCA behaves well on the segmentation accuracy and noise robustness.

Tyre pattern image retrieval (TPIR) is an important tool in the investigation of criminal activities and traffic accidents. Although content-based image retrieval (CBIR) has been developed for decades with abundant results, the study on TPIR which started in the 1990s has not made much progress. The lack of large standard test datasets is a crucial shortcoming which limits the research in this field. Information presented in this paper is a result of the authors’ literature research on recent academic publications and practical field investigation in the public security and transportation sectors. The state-of-the-art technologies in the field of TPIR are surveyed in detail from two aspects of tyre patterns – their low-level spatial features and high-level semantic features. Existing algorithms are examined and their pros and cons are compared and verified through experimental results. This paper also surveys the available tyre pattern datasets used in all available literature. Finally, with the considerations on technology trends in image retrieval and application requirements in TPIR, the future research directions in this field are laid out.

Multiobjective evolutionary algorithms (MOEAs) are effective optimization methods. To improve the segmentation performance and time efficiency of MOEAs-based fuzzy clustering algorithms for color images, a semisupervised surrogate-assisted multiobjective kernel intuitionistic fuzzy clustering (S3MKIFC) algorithm is proposed in this article. The main contributions of S3MKIFC can be summarized as follows: 1) semisupervised kernel intuitionistic fuzzy objective functions are constructed for optimization to search satisfactory segmentation results; 2) to reduce the computational cost, the Kriging model is used to predict the values of objective functions instead of directly calculating the expensive objective functions; 3) a semisupervised selection strategy and a semisupervised model management mechanism are proposed to balance the convergence and diversity and improve the predicted accuracy of the Kriging model, respectively; and 4) a novel semisupervised kernel intuitionistic fuzzy cluster validity index is defined to select the optimal solution from the final nondominated solution set. Experimental results on two color image libraries demonstrate that S3MKIFC outperforms state-of-the-art methods in segmentation performance and meanwhile possesses a low time cost.

Image upscaling is needed in many areas. There are two types of methods: methods based on a simple hypothesis and methods based on machine learning. Most of the machine learning‐based methods have disadvantages: no support is provided for a variety of upscaling factors, a training process with a high time cost is required, and a large amount of storage space and high‐end equipment are required. To avoid the disadvantages of machine learning, upscaling images with a simple hypothesis is a promising strategy but simple hypothesis always produces jaggy artifacts. The authors propose a new method to remove these jagged artifacts. They consider an edge in an image as a deformed curve. Removing jagged artefacts is considered equivalent to shortening the full arc length of a curve. By optimising the regularization model, the severity of the artifacts decreases as the number of iterations increases. They compare nine existing methods on the Set5, Set14, and Urban100 datasets. Without using any external data, the proposed algorithm has high visual quality, has few jagged artefacts and performs similarly to very recent state‐of‐the‐art deep convolutional network‐based approaches. Compared to other methods without external data, the proposed algorithm balances the quality and time cost well.

Images are always contaminated by noise, increasing uncertainty. Fuzzy set (FS) theory is a useful tool for dealing with uncertainty in images. When comparing with the FS, an intuitionistic fuzzy set (IFS) can better describe the blurred characteristic in images due to the membership, nonmembership, and hesitation degrees. However, when applied to an image segmentation, the IFS cannot completely overcome the influence of noise. With the aim of performing noisy image segmentation under several criteria, this paper defines a noise robust IFS (NR-IFS) for an image and then presents a novel noise robust multiobjective evolutionary intuitionistic fuzzy clustering algorithm (NR-MOEIFC). A majority dominated suppressed similarity measure using the neighborhood statistics and the competitive learning is proposed to obtain the NR-IFS representation for the image corrupted by noise. Then, the NR-IFS is fully used to motivate the whole process of multiobjective evolutionary clustering: first, computing a three-parameter intuitionistic fuzzy distance measure; second, constructing intuitionistic fuzzy fitness functions; third, designing a nonuniform intuitionistic fuzzy mutation operator; and forth, defining an intuitionistic fuzzy cluster validity index to select the optimal solution from the final nondominated solution set. The histogram statistics of NR-IFS are adopted in the NR-MOEIFC to greatly reduce the computational complexity. Experimental results on Berkeley and real magnetic resonance images reveal that the NR-MOEIFC behaves well in noise robustness and segmentation performance while requiring a low time cost.

Self-learning super-resolution (SLSR) algorithms have the advantage of being independent of an external training database. This paper proposes an SLSR algorithm that uses convolutional principal component analysis (CPCA) and random matching. The technologies of CPCA and random matching greatly improve the efficiency of self-learning. There are two main steps in this algorithm: forming the training and testing the data sets and patch matching. In the data set forming step, we propose the CPCA to extract the low-dimensional features of the data set. The CPCA uses a convolutional method to quickly extract the principal component analysis (PCA) features of each image patch in every training and testing image. In the patch matching step, we propose a two-step random oscillation accompanied with propagation to accelerate the matching process. This patch matching method avoids exhaustive searching by utilizing the local similarity prior of natural images. The two-step random oscillation first performs a coarse patch matching using the variance feature and then performs a detailed matching using the PCA feature, which is useful to find reliable matching patches. The propagation strategy enables patches to propagate the good matching patches to their neighbors. The experimental results demonstrate that the proposed algorithm has a substantially lower time cost than that of many existing self-learning algorithms, leading to better reconstruction quality.

Abnormal crowd behaviors of varied real-world settings could represent or pose serious threat to public safety. The video data required for relevant analysis are often difficult to acquire due to security, privacy and data protection issues. Without large amounts of realistic crowd data, it is difficult to develop and verify crowd behavioral models, event detection techniques, and corresponding test and evaluations. This paper presented a synthetic method for generating crowd movements and tendency based on existing social and behavioral studies. Graph and tree searching algorithms as well as game engine-enabled techniques have been adopted in the study. The main outcomes of this research include a categorization model for entity-based behaviors following a linear aggregation approach; and the construction of an innovative agent-based pipeline for the synthesis of A-Star path-finding algorithm and an enhanced Social Force Model. A Spatial-Temporal Texture (STT) technique has been adopted for the evaluation of the model's effectiveness. Tests have highlighted the visual similarities between STTs extracted from the simulations and their counterparts - video recordings - from the real-world.

The security vulnerability of space information network is stimulating interest in developing security mechanisms study of heterogeneous network. However, it is noted that neither the single certificate authority (CA) nor the distributed CA can meet the security requirements for this type of network. In this study, the authors propose an adaptive distributed certificate management scheme in which the nodes that provide certificate services are selected dynamically in the network. The security risks caused by the static nodes are avoided in the proposed scheme. The status of the nodes in the network is completely equal and the credit values that vary with the operation of the network are the only criterion to measure their responsibilities. The scheme can effectively prevent attackers from getting permission of the certificate services and could be applicable to the complex and dynamic networks.

Glomerulus extraction is an important step for analyzing kidney-tissue image in the computer aided diagnosis system of kidney disease. According to the characteristic of these images, this paper proposes a glomerulus extraction method based on genetic algorithm and watershed transform. Firstly, a LOG filter is applied to get binary images that contain less noise by adjusting the parameters of Gaussian function. After labeling to remove the noises and thinning, a genetic algorithm is applied to these preprocessing images to search the best fitting curve, which determines the barycenter position of glomerulus and set this barycenter as seed. Secondly, the image which contains complete object boundary can be obtained through watershed transform, after region growing operation, glomerulus region can be extracted. With abundant samples, experimental result indicates our method can extract the glomerulus from kidney-tissue image both accurately and availably