Traditional Fusion Algorithms Research Articles

Advances in the diagnosis of prostate cancer based on image fusion

Image fusion currently plays an important role in the diagnosis of prostate cancer (PCa). Selecting and developing a good image fusion algorithm is the core task of achieving image fusion, which determines whether the fusion image obtained is of good quality and can meet the actual needs of clinical application. In recent years, it has become one of the research hotspots of medical image fusion. In order to make a comprehensive study on the methods of medical image fusion, this paper reviewed the relevant literature published at home and abroad in recent years. Image fusion technologies were classified, and image fusion algorithms were divided into traditional fusion algorithms and deep learning (DL) fusion algorithms. The principles and workflow of some algorithms were analyzed and compared, their advantages and disadvantages were summarized, and relevant medical image data sets were introduced. Finally, the future development trend of medical image fusion algorithm was prospected, and the development direction of medical image fusion technology for the diagnosis of prostate cancer and other major diseases was pointed out.

A novel remote sensing spatio-temporal data fusion framework based on the combination of deep learning downscaling and traditional fusion algorithm

A novel remote sensing spatio-temporal data fusion framework based on the combination of deep learning downscaling and traditional fusion algorithm

A novel industrial big data fusion method based on Q-learning and cascade classifier

The traditional industrial big data fusion algorithm has low efficiency and difficulty in processing high-dimensional data, this paper proposes a Q-learning-based cascade classifier model for industrial big data fusion. By combining cascade classifier and softmax classifier, feature extraction and data attribute classification of source industrial big data are completed in this cluster. In order to improve the classification rate, an improved Q-learning algorithm is proposed, which makes the improved algorithm randomly select actions in the early stage, and dynamically change in the late stage in the random selection of actions and actions with the highest reward value. It effectively improves the defects of traditional Q-learning algorithm that it is easy to fall into the local optimal and has slow convergence speed. The experimental results show that compared with other advanced fusion algorithms, the proposed method can greatly reduce the network energy consumption and effectively improve the efficiency and accuracy of data fusion under the same data volume.

A Hybrid Human Activity Recognition Method Using an MLP Neural Network and Euler Angle Extraction Based on IMU Sensors

Inertial measurement unit (IMU) technology has gained popularity in human activity recognition (HAR) due to its ability to identify human activity by measuring acceleration, angular velocity, and magnetic flux in key body areas like the wrist and knee. It has propelled the extensive application of HAR across various domains. In the healthcare sector, HAR finds utility in monitoring and assessing movements during rehabilitation processes, while in the sports science field, it contributes to enhancing training outcomes and preventing exercise-related injuries. However, traditional sensor fusion algorithms often require intricate mathematical and statistical processing, resulting in higher algorithmic complexity. Additionally, in dynamic environments, sensor states may undergo changes, posing challenges for real-time adjustments within conventional fusion algorithms to cater to the requirements of prolonged observations. To address these limitations, we propose a novel hybrid human pose recognition method based on IMU sensors. The proposed method initially calculates Euler angles and subsequently refines them using magnetometer and gyroscope data to obtain the accurate attitude angle. Furthermore, the application of FFT (Fast Fourier Transform) feature extraction facilitates the transition of the signal from its time-based representation to its frequency-based representation, enhancing the practical significance of the data. To optimize feature fusion and information exchange, a group attention module is introduced, leveraging the capabilities of a Multi-Layer Perceptron which is called the Feature Fusion Enrichment Multi-Layer Perceptron (GAM-MLP) to effectively combine features and generate precise classification results. Experimental results demonstrated the superior performance of the proposed method, achieving an impressive accuracy rate of 96.13% across 19 different human pose recognition tasks. The proposed hybrid human pose recognition method is capable of meeting the demands of real-world motion monitoring and health assessment.

MFIRA: Multimodal Fusion Intent Recognition Algorithm for AR Chemistry Experiments

The current virtual system for secondary school experiments poses several issues, such as limited methods of operation for students and an inability of the system to comprehend the users’ operational intentions, resulting in a greater operational burden for students and hindering the goal of the experimental practice. However, many traditional multimodal fusion algorithms rely solely on individual modalities for the analysis of users’ experimental intentions, failing to fully utilize the intention information for each modality. To rectify these issues, we present a new multimodal fusion algorithm, MFIRA, which intersects and blends intention probabilities between channels by executing parallel processing of multimodal information at the intention layer. Additionally, we developed an augmented reality (AR) virtual experiment platform based on the Hololens 2, which enables students to conduct experiments using speech, gestures, and vision. Employing the MFIRA algorithm, the system captures users’ experimental intent and navigates or rectifies errors to guide students through their experiments. The experimental results indicate that the MFIRA algorithm boasts a 97.3% accuracy rate in terms of interpreting users’ experimental intent. Compared to existing experimental platforms, this system is considerably more interactive and immersive for students and is highly applicable in secondary school experimental chemistry classrooms.

Improved Procedure for Multi-Focus Image Quality Enhancement Using Image Fusion with Rules of Texture Energy Measures in the Hybrid Wavelet Domain

Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional wavelet-based transforms produce ringing distortions and artifacts due to poor directionality and shift invariance. The Dual-Tree Complex Wavelet Transforms (DTCWT) combined with Stationary Wavelet Transform (SWT) as a hybrid wavelet fusion algorithm overcomes the deficiencies of the traditional wavelet-based fusion algorithm and preserves the directional and shift invariance properties. The purpose of SWT is to decompose the given source image into approximate and detailed sub-bands. Further, approximate sub-bands of the given source are decomposed with DTCWT. In this extraction, low-frequency components are considered to implement Texture Energy Measures (TEM), and high-frequency components are considered to implement the absolute-maximum fusion rule. For the detailed sub-bands, the absolute-maximum fusion rule is implemented. The texture energy rules have significantly classified the image and improved the output image’s accuracy after fusion. Finally, inverse SWT is applied to generate an extended fused image. Experimental results are evaluated to show that the proposed approach outperforms approaches reported earlier. This paper proposes a fusion method based on SWT, DTCWT, and TEM to address the inherent defects of both the Parameter Adaptive-Dual Channel Pulse coupled neural network (PA-DCPCNN) and Multiscale Transform-Convolutional Sparse Representation (MST-CSR).

Remote Sensing Image Fusion Based on Wavelet Transform

In this study, we propose an effective image fusion method which applies the Wavelet Transform algorithm to the remote sensing image fusion. The wavelet transform decomposes the original image into a series of frequency channels, so two images with distinct characteristics can be fused separately in multiple frequency domains. By applying the Wavelet Transform algorithm, we can get a better effect than other traditional fusion algorithms such as IHS (Intensity, Hue, and Saturation).

Mutual-Aided INS/Vision Navigation System Analysis and Optimization Using Sequential Filtering with State Recalculation

This paper presents the implementation of a mutual-aided navigation system for an aerial vehicle. Employing all available sensors in navigation is effective at maintaining continuous and optimal results. The images offer a lot of information about the surrounding environment, but image processing is time-consuming and causes timing problems. While traditional fusion algorithms tend to reduce the delay errors or ignore them, this research depends on state estimation recalculation during the delay time and on sequential filtering. To reduce the image matching time, the map is processed offline, then key point clusters are stored to avoid feature recalculation online. The sensors' information is used to bound the search space for the matched features on the map, then they are reprojected on the captured images to exclude the unuseful part from processing. The suggested mutual-aided form compensates for the inertial system drift, which enhances the system's accuracy and independence. The system was tested using data collected from a real flight using a DJI drone. The measurements from an inertial measurement unit (IMU), camera, barometer, and magnetometer were fused using a sequential Kalman Filter. The final results prove the efficiency of the suggested system to navigate with high independency, with an RMS position error of less than 3.5 m.

Gram-Schmidt Remote Sensing Image Fusion Algorithm Based on Matrix Elementary Transformation

In the application of the Internet of vehicles, it is difficult for a single remote sensing image to have accurate spatial information and spectral information at the same time, so engineers must use image fusion to improve the utilization of remote sensing image information. Aiming at the shortcomings of low spectral resolution and relatively complex programming of traditional fusion algorithms, this paper proposes a Gram-Schmidt remote sensing image fusion algorithm based on matrix elementary transformation, which aims to improve the spatial resolution of multispectral images by using panchromatic images with high spatial resolution. The orthogonalization process of the algorithm in this paper uses the elementary matrix transformation, which is simple and easy to perform. The experimental results show that it can effectively improve the spatial resolution of the original image and has good spectral retention.

Robot Path Planning Navigation for Dense Planting Red Jujube Orchards Based on the Joint Improved A* and DWA Algorithms under Laser SLAM

High precision navigation along specific paths is required for plant protection operations in dwarf and densely planted jujube orchards in southern Xinjiang. This study proposes a robotic path planning and navigation method for dense planting of red jujube orchards based on the improved A* and dynamic window approach (DWA) algorithms using Laser Radar to build maps. First, kinematic and physical robot simulation models are established; a map of the densely planted jujube orchard is constructed using Laser Radar. The robot’s position on the constructed map is described using an adaptive Monte Carlo positioning algorithm. Second, a combination of the improved A* and DWA algorithms is used to implement global and real-time local path planning; an evaluation function is used for path optimisation. The proposed path planning algorithm can accurately determine the robot’s navigation paths, with the average error U, average linear path displacement error, and L-shaped navigation being 2.69, 2.47, and 2.68 cm, respectively. A comparison experiment is set up in the specific path navigation section. The experimental results show that the improved fusion algorithm reduces the average navigation positioning deviation by 0.91cm and 0.54 cm when navigating L and U-shaped specific paths. The improved fusion algorithm is superior to the traditional fusion algorithm in navigation accuracy and navigation stability. It can improve the navigation accuracy of the dense planting jujube garden and provide a reference method for the navigation of the plant protection operation in the densely planted jujube orchards.

A Two-To-One Deep Learning General Framework for Image Fusion.

The image fusion algorithm has great application value in the domain of computer vision, which makes the fused image have a more comprehensive and clearer description of the scene, and is beneficial to human eye recognition and automatic mechanical detection. In recent years, image fusion algorithms have achieved great success in different domains. However, it still has huge challenges in terms of the generalization of multi-modal image fusion. In reaction to this problem, this paper proposes a general image fusion framework based on an improved convolutional neural network. Firstly, the feature information of the input image is captured by the multiple feature extraction layers, and then multiple feature maps are stacked along the number of channels to acquire the feature fusion map. Finally, feature maps, which are derived from multiple feature extraction layers, are stacked in high dimensions by skip connection and convolution filtering for reconstruction to produce the final result. In this paper, multi-modal images are gained from multiple datasets to produce a large sample space to adequately train the network. Compared with the existing convolutional neural networks and traditional fusion algorithms, the proposed model not only has generality and stability but also has some strengths in subjective visualization and objective evaluation, while the average running time is at least 94% faster than the reference algorithm based on neural network.

MFA: A Smart Glove with Multimodal Intent Sensing Capability.

At present, virtual-reality fusion smart experiments mostly employ visual perception devices to collect user behavior data, but this method faces the obstacles of distance, angle, occlusion, light, and a variety of other factors of indoor interactive input devices. Moreover, the essence of the traditional multimodal fusion algorithm (TMFA) is to analyze the user's experimental intent serially using single-mode information, which cannot fully utilize the intent information of each mode. Therefore, this paper designs a multimodal fusion algorithm (hereinafter referred to as MFA, Algorithm 4) which focuses on the parallel fusion of user's experimental intent. The essence of the MFA is the fusion of multimodal intent probability. At the same time, this paper designs a smart glove based on the virtual-reality fusion experiments, which can integrate multichannel data such as voice, visual, and sensor. This smart glove can not only capture user's experimental intent but also navigate, guide, or warn user's operation behaviors, and it has stronger perception capabilities compared to any other data glove or smart experimental device. The experimental results demonstrate that the smart glove presented in this paper can be widely employed in the chemical experiment teaching based on virtual-reality fusion.

Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm.

In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.

Review on Millimeter-Wave Radar and Camera Fusion Technology

Cameras allow for highly accurate identification of targets. However, it is difficult to obtain spatial position and velocity information about a target by relying solely on images. The millimeter-wave radar (MMW radar) sensor itself easily acquires spatial position and velocity information of the target but cannot identify the shape of the target. MMW radar and camera, as two sensors with complementary strengths, have been heavily researched in intelligent transportation. This article examines and reviews domestic and international research techniques for the definition, process, and data correlation of MMW radar and camera fusion. This article describes the structure and hierarchy of MMW radar and camera fusion, it also presents its fusion process, including spatio-temporal alignment, sensor calibration, and data information correlation methods. The data fusion algorithms from MMW radar and camera are described separately from traditional fusion algorithms and deep learning based algorithms, and their advantages and disadvantages are briefly evaluated.

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.

Multimodal Medical Image Fusion Using Nonsubsampled Shearlet Transform and Smallest Uni-Value Segment Assimilating Nucleus

This paper presents a new fusion scheme for medical (CT-MRI) images which is based on the nonsubsampled shearlet transform (NSST). The various image pairs to be fused are obtained from primary and internet sources. Initially, the images are decomposed through NSST into general and detailed features. The smallest uni-value segment assimilating nucleus (SUSAN) and local sum of Gaussian weighted pixel intensities-based activity measures are proposed to fuse the detailed sub-bands and low-frequency sub-band of NSST, respectively, for faster execution of the algorithm. Visual and parametric comparison of the proposed scheme is done through five traditional fusion algorithms using nine fusion performance parameters. In addition, Wilcoxon signed ranks test is also applied to compare different methods scientifically with the proposed fusion scheme. It is observed that the presented method is better in retaining bone, calcification, cerebrospinal fluid (CSF), edema and tumor details of the source images and is faster than other classical fusion schemes. The fused images of the proposed method are suitable for locating the site of biopsy externally or incision location in the bone of the brain skull with minimum diagnostic time.

Multi-sensor fusion of sparse point clouds based on neuralnet works

The fusion of laser point cloud and visual image depends on the point cloud density and the target framing effect, the traditional laser point cloud processing for sparse point cloud clustering effect is poor, it is difficult to frame small objects as well as medium and long distance objects. Then the subsequent sensor fusion is easy to miss the recognition of obstacles. In this paper, we improve the frame selection method for sparse point clouds, firstly build a deep learning framework pointpillar, use pointpillar to frame the sparse laser point clouds, then spatially calibrate the lidar coordinate system and camera coordinate system, project the lidar point clouds to the camera image, improve the late fusion method, effectively use the detection results of single sensor, and finally The late-fusion is performed with the target detection results of the camera image to output the exact distance as well as the category of the target. Experiments show that compared with the recognition effect of the traditional fusion algorithm, the number of frames is increased by 6 and the missed recognition rate is reduced from 31.41% to 12.31%.

SwinSTFM: Remote Sensing Spatiotemporal Fusion Using Swin Transformer

Remote sensing images with high temporal and spatial resolutions have broad market demands and various application scenarios. This paper aims to generate high-quality remote sensing image time series for feature mining of the growth quality of traditional Chinese medicine. Spatiotemporal fusion is a flexible method that combines two types of satellite images with high temporal resolution or high spatial resolution to generate high-quality remote sensing images. In recent years, many spatiotemporal fusion algorithms have been proposed, and deep learning-based methods show extraordinary talents in this field. However, the current deep learning-based methods have three problems: 1) most algorithms do not support models with large-scale learnable parameters; 2) the model structure based on convolutional neural networks will bring noise to the image fusion process; 3) current deep learning-based methods ignore some excellent modules in traditional spatiotemporal fusion algorithms. For the above problems and challenges, this paper creatively proposes a new algorithm based on Swin Transformer and linear spectral mixing theory. The algorithm makes full use of the advantages of Swin Transformer in feature extraction, and integrates the unmixing theories into the model based on the self-attention mechanism, which greatly improves the quality of generated images. In the experimental part, the proposed algorithm achieves state-of-the-art results on three well-known public datasets, and has been proved effective and reasonable in ablation study.

A Multisource Data Fusion Modeling Prediction Method for Operation Environment of High‐Speed Train

Providing accurate and reliable railway regional environmental data is a key consideration in operation control and dynamic dispatching of high‐speed train. However, there are problems of low reliability and high uncertainty in the single data processing of high‐speed train operating area environment. Therefore, this paper proposes a novel multisource sensor data fusion method based on a three‐level information fusion framework. Firstly, the feature of the same kind of sensor data is extracted by the Kalman Filter (KF) algorithm as the input of back propagation neural network (BPNN). Then input the sample site into the BPNN for training and recognition, the feature fusion of heterogeneous sensor data is carried out, the decision output of BPNN is obtained, the output results are normalized, and its output is used as the basic probability assignment of Dempster–Shafer (D‐S) evidence theory and synthesis rules. Finally, the decision fusion of multisource data is realized by D‐S evidence theory. The simulation results show that compared with the traditional single fusion algorithm, the algorithm improves the accuracy of the prediction of high‐speed train operation environment and reduces the MAPE from 13.82% to 7.455%, and the RMSE from 0.77 to 0.69, and meanwhile, increases the R2 from 0.87 to 0.97.

An accurate cloud‐based indoor localization system with low latency

Indoor positioning systems are becoming increasing popular recently. While most existing indoor positioning studies focus on improving accuracy, less attention is paid to the latency problem. The traditional fusion algorithm uses the received signal strength-based (RSS-Based) positioning result to correct the current position of the system. However, it fails to keep up with the actual moving speed of the user during the navigation. The location retrospective adjustment (LRA) method proposed in this paper uses the RSS-Based positioning result to correct the past position of the system, which can effectively eliminate positioning delay and improve the real-time response of navigation. We tested in a 70 m linear promenade and found that the addition of LRA results in a reduction of the positioning error around −0.3 to +0.4 m, which improves 85%. Additionally, the LRA method alleviates the requirements for the immediate response of RSS positioning, and the RSS positioning algorithm can be moved to the cloud. It reduces the download resources and computing load on the mobile phone. The complete indoor navigation application is presented in HTML5 which allows users to navigate without having to download the APP in advance, and it takes only 4–9 s for users to launch the application for the first time. We tested the application in a hospital with a total floor area of 79,000 m2 in 7 buildings. The system achieves an average positioning accuracy of 0.65 m at a long navigation distance of 220 m. To our knowledge, this paper is the first to consider the latency issue in indoor navigation. The proposed LRA approach improves real-time navigation performance, lightens the computation load on the mobile phone, and allows cloud-based positioning systems to provide stable and accurate navigation even under poor network quality in crowded areas.