The goal of feature matching is to establish accurate correspondences between feature points in different images depicting the same scene. To address the polymorphism of local structures, the authors propose a mismatch removal method using bilateral local–global structural consistency. This method incorporates the problem of mismatch removal into the framework of graph matching, constructs a global affinity matrix using local structural similarity and global affine transformation consistency, and optimizes it using a constrained integer quadratic programming method. To comprehensively describe the local structure, the signature quadratic form distance (SQFD) is used to measure the consistency of the neighbourhood structure. Specifically, the weights of edges are constructed based on the SQFD of the local structure, while the matching correctness of nodes and edges between the two graphs is described using local vector similarity. Furthermore, the consistency of the global affine transformation is evaluated by assessing the consistency of the local neighbourhood affine transformation between different corresponding point pairs. In estimating the local affine transformation, a bilateral correction is performed using a total least‐squares (TLS) algorithm to measure the similarity of nodes between the two different graphs. Experimental results demonstrate that the proposed algorithm outperforms state‐of‐the‐art methods in terms of accuracy and effectiveness.

To address the issue of polymorphism in local structure, the BLGSC algorithm integrates two fundamental metrics: local structure similarity and global affine transformation consistency. The BLGSC algorithm incorporates both signature quadratic form distance (SQFD) and vector consistency, enabling a comprehensive representation of local structures. Additionally, it assesses the consistency of affine transformations across multiple local structures, thereby quantifying the similarity of the global structure.image

The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross‐stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop.

We propose a lightweight bus passenger detector based on the network YOLOv5 to improve the bus passenger detection speed. The inner cross bottleneck ICB and outer cross bottleneck OCB are introduced into the YOLOv5 network to achieve model compression, maintain detection accuracy and improve speed. The NFP pooling module is incorporated into the network to aggregate discriminative features and improve the detection accuracy of the model.image

Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.

The article presents a novel tamper detection and localization scheme for multiple faces in digital images. The novelty of the proposed scheme can be summarized in its new algorithms and the idea of protecting and authenticating multiple faces in the image. The experimental results of the proposed scheme are very promising, and it is predicted to open the door for many future researches in this field.image

Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Alzheimer's disease is a severe neurodegenerative disorder causing irreversible memory loss and is most common among seniors 65 years and older. Early diagnosis can slow its progress. Artificial intelligence, particularly deep and machine learning models, can help with diagnosis by identifying subtle patterns in extensive patient datasets. A new Hybrid CNN methodology using brain MRI images and transfer learning for multi‐class classification achieves 97.31% accuracy and outperforms state‐of‐the‐art models in accuracy, precision, recall and F‐score.image

Brain tumour diagnosis is significant for both physicians and patients, but the low contrast and the artefacts of MRI glioma images always affect the diagnostic accuracy. The existing mainstream image enhancement methods are insufficient in improving contrast and suppressing artefacts simultaneously. To enrich the field of glioma image enhancement, this research proposed a glioma image enhancement method based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. Firstly, this method modifies the cumulative distribution function on the image histogram and performs gamma correction on the image according to the modified histogram to obtain a contrast‐enhanced image. Then, the method suppresses the artefacts of glioma images by total variational and wavelet denoising algorithm. To get better enhancement images, the optimal parameters in the proposed method are searched by the SPGD algorithm. The statistical studies performed on 580 real glioma images demonstrate that the authors’ approach can outperform the existing mainstream image enhancement methods. The results show that the proposed method increases the discrete entropy of the image by 8.9% and the contrast by 2.8% compared to original images. The enhanced images are produced by the proposed method with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation.

To enrich the field of glioma image enhancement, this paper proposed a glioma image enhancement framework based on histogram modification and total variational using stochastic parallel gradient descent (SPGD) algorithm. The results show that the proposed framework has superior performance compared to several existing glioma image enhancement methods. image