Text recognition in complex scenes is a challenging task in Computer Vision. In this paper, we propose an innovative framework for scene text recognition, ASANet, which features an Alternate Attention Enhancement Encoder and a Masked Dual-modal Decoder. The encoder incorporates a 12-layer Alternating Self-Attention Module (ASAM), consisting of both Channel and Spatial Blocks, which significantly enhance the depth and breadth of feature extraction. The decoder employs a strategy that combines masking and sequence alignment modeling, effectively improving character context relevance and prediction accuracy. Extensive experimental results demonstrate that ASANet achieves state-of-the-art performance across several benchmark datasets, with a notable accuracy of 91.2% on our self-constructed Uyghur text dataset, highlighting its superior performance.

Abstract Facial expression recognition technology has become a powerful tool for conveying human emotions and intentions and is widely used in areas such as assisted driving and intelligent medical care. Due to the limited computing power of current hardware devices and the real‐time requirements of application scenarios, this paper proposes a high‐performance and lightweight framework for real‐time facial expression recognition framework to solve the problem of real‐time completion of expression recognition tasks under low hardware costs. To address these issues, this paper first designs a RepVGG and mobileNetV2 dual‐channel structure in the feature extraction. It is then input into the MobileViT Block for global feature modelling. Finally, the position vector of the capsule network is used to replace the output of the global pooling, preserving the spatial relationship of the salient features and enhancing the classification effect. Compared with the mainstream facial expression recognition algorithm that cannot get good classification results under low complexity conditions, the model has a significant accuracy improvement while ensuring lightweight. With only 294.60M FLOPS and 0.95M parameters, it achieved an accuracy of 97.53% on the KDEF dataset and 85.56% on the RAF‐DB, demonstrating the advanced nature of the algorithm.

Abstract Lung segmentation is an essential step in a computer‐aided diagnosis system for chest radiographs. The lung parenchyma is first segmented in pulmonary computer‐aided diagnosis systems to remove the interference of non‐lung regions while increasing the effectiveness of the subsequent work. Nevertheless, most medical image segmentation methods nowadays use U‐Net and its variants. These variant networks perform poorly in segmentation to detect smaller structures and cannot accurately segment boundary regions. A multi‐interaction feature fusion network model based on Kiu‐Net is presented in this paper to address this problem. Specifically, U‐Net and Ki‐Net are first utilized to extract high‐level and detailed features of chest images, respectively. Then, cross‐residual fusion modules are employed in the network encoding stage to obtain complementary features from these two networks. Second, the global information module is introduced to guarantee the segmented region's integrity. Finally, in the network decoding stage, the multi‐interaction module is presented, which allows to interact with multiple kinds of information, such as global contextual information, branching features, and fused features, to obtain more practical information. The performance of the proposed model was assessed on both the Montgomery County (MC) and Shenzhen datasets, demonstrating its superiority over existing methods according to the experimental results.

Abstract Object detection is the most important problem in computer vision tasks. After AlexNet proposed, based on Convolutional Neural Network (CNN) methods have become mainstream in the computer vision field, many researches on neural networks and different transformations of algorithm structures have appeared. In order to achieve fast and accurate detection effects, it is necessary to jump out of the existing CNN framework and has great challenges. Transformer’s relatively mature theoretical support and technological development in the field of Natural Language Processing have brought it into the researcher’s sight, and it has been proved that Transformer’s method can be used for computer vision tasks, and proved that it exceeds the existing CNN method in some tasks. In order to enable more researchers to better understand the development process of object detection methods, existing methods, different frameworks, challenging problems and development trends, paper introduced historical classic methods of object detection used CNN, discusses the highlights, advantages and disadvantages of these algorithms. By consulting a large amount of paper, the paper compared different CNN detection methods and Transformer detection methods. Vertically under fair conditions, 13 different detection methods that have a broad impact on the field and are the most mainstream and promising are selected for comparison. The comparative data gives us confidence in the development of Transformer and the convergence between different methods. It also presents the recent innovative approaches to using Transformer in computer vision tasks. In the end, the challenges, opportunities and future prospects of this field are summarized.

Precise segmentation of polyps from colonoscopic images is extremely significant for the early diagnosis and treatment of colorectal cancer. However, it is still a challenging task due to: (1)the boundary between the polyp and the background is blurred makes delineation difficult; (2)the various size and shapes causes feature representation of polyps difficult. In this paper, we propose an integration context-based reverse-contour guidance network (ICGNet) to solve these challenges. The ICGNet firstly utilizes a reverse-contour guidance module to aggregate low-level edge detail information and meanwhile constraint reverse region. Then, the newly designed adaptive context module is used to adaptively extract local-global information of the current layer and complementary information of the previous layer to get larger and denser features. Lastly, an innovative hybrid pyramid pooling fusion module fuses the multi-level features generated from the decoder in the case of considering salient features and less background. Our proposed approach is evaluated on the EndoScene, Kvasir-SEG and CVC-ColonDB datasets with eight evaluation metrics, and gives competitive results compared with other state-of-the-art methods in both learning ability and generalization capability.

Robustness enhancement and feature selection are the two crucial issues to be resolved in robust palmprint recognition. However, existing regression-based methods are insufficient to handle outliers and select significant features. From a statistical viewpoint, we present a general framework to intrinsically resolve the two issues. By investigating the role of outliers in the formation of coding errors, we devise a double-Laplacian mixture-error model to faithfully fit the error distribution. Additionally, we design a supervised group-sparse regularizer to enforce the locality and group sparsity of the codes. Integrating the two parts into the framework produces a nonconvex constrained problem, for which we develop an iteratively reweighted${l} _{ {1}}$-${l} _{{1}}$minimization algorithm by combining the majorization-minimization strategy and the alternating direction method of multipliers. The weighted vector learned from the error model and the local group sparsity enforced by the regularizer enable our method to better handle outliers and select more significant features than the state-of-the-art methods. Extensive experimental results verify the flexibility and robustness of our method to various contaminations.

The spectral amplitude of most natural images is approximately isotropic and follows the power law. In this study, the authors propose a new non‐iterative blind image deconvolution algorithm that builds an isosceles curve model to approximate the spectrum amplitude of the real image. In the authors’ proposed algorithm, the optical transfer function (OTF) is obtained by comparing the reconstructed and degraded spectra. Then they employ the integrated multidirectional comprehensive estimation to reduce the OTF estimation error. The restored image is then obtained by applying the estimated OTF and the Wiener filter. Experiments on image deconvolution tasks indicate that the proposed algorithm provides a significant performance gain by obtaining an accurate OTF, reducing ringing artefacts compared with existing algorithms, and realising real‐time image restoration.

In the classic algorithm, palmprint recognition requires extraction of palmprint features before classification and recognition, which will affect the recognition rate.To solve this problem, this paper uses the convolutional neural network (CNN) structure Alexnet to realize palmprint recognition.First, according to the characteristics of the geometric shape of palmprint, the ROI area of palmprint was cut out.Then the ROI area after processing is taken as input of convolutional neural network.Next the PRelu activation function is used to train the network to select the best learning rate and super parameters.Finally, the palmprint was classified and identified.The method was applied to PolyU Multi-Spectral Palmprint Image Database and PolyU 2D+3D Palmprint Database, and the recognition rate of a single spectrum was up to 99.99%.

Although the unimodal biometric recognition (such as face and palmprint) has higher convenience, its security is also relatively weak.The recognition accuracy is easy affected by many factors such as ambient light and recognition distance etc.To address this issue, we present a weighted multimodal biometric recognition algorithm with face and palmprint based on histogram of contourlet oriented gradient (HCOG) feature description.We employ the nonsubsampled contour transform (NSCT) to decompose the face and palmprint images, and the HOG method is adopted to extract the feature, which is named as HCOG feature.Then the dimension reduction process is applied on the HCOG feature and a novel weight value computation method is proposed to accomplish the multimodal biometric fusion recognition.Extensive experiments illustrate that our proposed weighted fusion recognition can achieve excellent recognition accuracy rates and outmatches the unimodal biometric recognition methods.

Power quality events are usually associated with more than one disturbance and their recognition is typically based on multilabel learning. In this study, we propose a new method for recognizing multiple power quality disturbances (MPQDs) based on variational mode decomposition (VMD) and a random discriminative projection extreme learning machine for multilabel learning (RDPEML). First, VMD is employed to decompose the MPQDs into several intrinsic mode functions and the standard energy differences of each mode are extracted as features that form the input vectors of the classifier. Second, a novel multilabel classifier called RDPEML is constructed by combining a random discriminative projection multiclass extreme learning machine (ELM) and a thresholding learning method-based kernel ELM. In order to obtain better classification performance, a tenfold cross-validation embedded particle swarm optimization approach is utilized to search for the optimal values of the structural parameters. Finally, a test study was conducted using MATLAB synthetic signals and real signals sampled from a three-phase standard source under different noise conditions. Compared with the several recent state-of-the-art multilabel learning algorithms, RDPEML achieved better classification performance with superior computational speed.

For a well-established digital library (e.g., PubMed), searching in terms of a newly established ontology (e.g., Gene Ontology (GO)) is an extremely difficult task. Making such a digital library adaptive to any new ontology or to reorganize knowledge automatically is our main objective. The decomposition of the knowledge base into classes is a first step toward our main objective. In this paper, we will demonstrate an automated linking scheme for PubMed citations with GO terms using an improved version of extreme learning machine (ELM) type algorithms. ELM is an emergent technology, which has shown excellent performance in large data classification problems, with fast learning speeds.