Reviewerly/exHarmony huggingface dataset:
PT-MVSNet: Overlapping Attention Multi-view Stereo Network with Transformers

In this paper, we propose a new multi-view stereo vision model PT -MVSNet based on multi-view stereo (MVS). Multi-view stereo is a successful reconstruction method that uses multiple images to reconstruct a 3D scene. It has been applied in many practical scenes such as architecture, cultural heritage protection, and map making. MVS still faces a lot of challenges, including inaccurate feature matching, excessive image noise, and overly complex computation. To solve the feature-matching inaccuracy problem, we take the Transformer model as the main structure in the feature-matching and add a patch-based overlap attention module (POLA). In this paper, we proposed PT-MVSNet can solve the image feature extraction problem more effectively. To validate the effectiveness of the model, we conducted experiments on the DTU dataset and evaluated its performance by two evaluation metrics. The experiment results show that our method outperforms the latest methods, whose accuracy and completeness reach 0.386 and 0.271 respectively.
Underwater image recovery method considering target polarization characteristics

In underwater polarization imaging, the estimation of the polarization characteristics of the target is also the key to recover the original image. Most current recovery methods ignore the polarization characteristics of the target and require manual selection of the region for parameter estimation. This not only causes errors in the recovery results, but also has the limitation that a background region must exist. In this paper, we derive the reconstruction model of the original underwater image from the underwater imaging model and the polarization imaging model, considering the polarization characteristics of the target. In this paper, the background light polarization degree is obtained using the filtered median algorithm, and the bright channel principle is used to obtain the background light at infinity. Compared with the traditional method, the method in this paper can reduce the error. Finally, this paper makes improvements to the perfect reflection algorithm and enhances the recovered image.
A Research on Traditional Tangka Image Classification Based on Visual Features

With the development of computer vision technology, image classification has become an important research field. In order to achieve automated classification of Tang Ka images, this paper proposes a visual feature based classification method for Tang Ka images. The method first uses HOG features, Local binary patterns and color moments to extract image features, and then uses support vector machine classifier to classify images. The data in this article shows that the highest accuracy rate of the Hantangka obtained through support vector machine classification and filtering is 96.5%. The experimental results show that this method performs well in the classification of heritage thangka images and has a high classification accuracy.
An improved algorithm for interactive medical image segmentation based on Intelligent scissors

Image segmentation is an important part of computer vision technology, which aims to extract areas of interest for feature acquisition and analysis. The accuracy of medical image segmentation has a direct impact on the decision-making capacity of doctors to diagnose patients' condition. In this paper, the interactive medical image segmentation algorithm based on the intelligent scissors is selected to process the medical image. It can not only combine the subjective experience but also reduce the operation obstacles and speed up the segmentation. The main work of this paper is as follows: 1. Based on the full study of the intelligent scissors algorithm, the principles and methods for the digraph's weight selection in the algorithm construction are clarified, which can improve the accuracy of the algorithm effectively. 2. The IOU factor was introduced to evaluate the image segmentation effect, which can analyze the existing defects from a quantitative perspective. By calculating the similarity between pathological images and normal images, the rationality of image segmentation result analysis is improved. 3. The intelligent scissors algorithm is improved by the modified Canny operator to reduce the generation of pseudo-edges and protect the weaker edges from rejection, which largely improves the accuracy of the segmentation. 4. The research work of the thesis was validated by applying pathological images of patients with novel coronavirus pneumonia and lung cancer. It is proved that the work in this paper has certain theoretical Significance and practical application value.
LKLM: A Large-Kernel Lightweight CNN Model for Monocular Depth Estimation

Depth estimation is a fundamental task in computer vision, which is increasingly widely applied in autonomous driving. However, many existing methods have often resulted in large computational overhead and high latency. To tackle this issue, we propose a lightweight CNN architecture named Large-Kernel Lightweight CNN Model (LKLM). Extensive experiments demonstrate the superiority of our approach in lightweight depth estimation. On the KITTI dataset, LKLM achieves a large margin in accuracy, with 4.254 GFlops.
Application Analysis of Cast Steel Joint in 385m-high Long-span Transmission Tower

Based on 385m-high long-span transmission tower of 500kV Fengcheng to Meili project, the selection of the joint of bottom-crossarm compression member is conducted. Three types of joints are put forward and compared, including tubular joint, welded hollow spherical joint and cast steel tubular joint. The cast steel joint adopts monolithic casting technology, it helps to solve the problem of space intersection of multiple members, and avoid complex welding process. Due to comparison results, the cast steel joint is selected, and steel material is G20Mn5QT. Then, the nonlinear finite element analysis is performed, the force characteristics and ultimate capacity of cast steel joint is investigated, and design capacity of the joint under different load cases is calculated. It reveals that the cast steel joint is safe and reliable, and it is suggested to be wildly used in super-high transmission towers.
An Anti-epidemic Delivery Trolley based on YOLOv5 Image Recognition of Room Numbers and QR Codes

Since the massive spread of the COVID-19 epidemic in 2020 and the recent spread of influenza, the contactless delivery trolley has met the demands of the government for logistics against epidemics and the needs of logistics companies for deliveries. This paper designs a recognition method based on YOLOv5 (You Only Look Once version 5) for detecting room numbers and QR codes for the needs of anti-epidemic delivery trolleys and implements tasks such as stopping the anti-epidemic trolley at a specified room number and opening different warehouses according to different QR code information. The experiments improved the recognition by filtering the pepper noise through mean filtering. Experimental results show an increase of 2.105 % in the recognition rate of pre-processed QR codes, with an average accuracy rate of 98.9%. The delivery trolley also achieves a stable recognition rate of around 98.81% for room numbers.
Research on Radar PRI Modulation Pattern Recognition Based on Recurrent Neural Network

In this paper, the Recurrent Neural Network (RNN) model is introduced into the identification of radar emitters, which verifies the feasibility of RNN application to radar emitter identification, and the design process is simplified and the radar emitter identification method is easy to apply in engineering. Based on the radar emission source recognition model, the Pulse Repetition Interval(PRI) modulation pattern recognition research based on recurrent neural network was carried out. Firstly, aiming at the problems of gradient vanishing and gradient explosion in RNN, a PRI modulation pattern recognition method based on LSTM is proposed, and the recognition method process is introduced in detail. According to the characteristics of pulse train data, a data preprocessing method suitable for recurrent neural network is proposed. Finally, simulation verifies the effectiveness of the proposed method and tests the performance of the algorithm under different interference conditions.
LDFUNet: Large-Kernel Convolution and Dynamic Fusion Network for Superpixel Segmentation

Superpixel is an image preprocessing technique that combines adjacent pixels in an image to form smaller connected regions. It serves as the smallest unit for subsequent image-processing tasks. Reduced computational complexity and improved accuracy of image segmentation. The past superpixel segmentation algorithms need to compute the hard associate map between pixels and seeds, which results in a huge computation cost. To reduce the computation cost, we propose LDFUNet, Large-Kernel Convolution, and Dynamic Fusion Network. Large-Kernel Convolution extract superpixel feature efficiently. And dynamic can fuse the pixel and superpixel feature dynamic, which can capture the pixel-superpixel relationship information dynamic for the final prediction. The experiments show that our method achieves a good trade-off between computational speed and segmentation accuracy.
Domain Adaptation based on Attention-Weighted Optimal Transport and Cluster Alignment

Optimal transport (OT) as an efficient way to measure differences in distribution. provide new ideas for solving the unsupervised domain adaptation (UDA) problem. However, existing work based on OT ignores the multi-modal structure of the domain and does not adequately reflect the true data distribution. Therefore, this paper proposes a new deep UDA framework, namely attention-weighted optimal transport and cluster alignment (AWOT). Specifically, the model first proposes a weighting strategy based on a self-attentive mechanism to reduce the bias caused by mini-batch selection in training. The strategy calculates weights by correlating the iterative prediction results of the source and target domains to weight the optimal in-transmission cost function. A confidence thresholding technique is also proposed to filter out low confidence target samples to enhance the robustness of pseudo-label. Moreover, AWOT is also equipped with a clustering centroid alignment loss to learn discriminative features. Comprehensive experiments have shown that AWOT outperforms existing state-of-the-art methods on several domain adaptation benchmarks.
Visual Tracking with Temporal Contextual Attention

Due to changes in the appearance, scale, and other aspects of tracking objects, accurate tracking has great difficulties in the current existing visual trackers. Recently, there has been significant progress in time-domain trackers, which has inspired us to solve this problem. Therefore, in this paper, we effectively address the issue of object scale and appearance changes during the tracking process from the perspective of mining temporal context information. This paper proposes Visual Tracking with Temporal Contextual Attention (TCFormer). The modeling scheme not only extracts the target-specific distinguishing features and the association between the target and the search area, but also effectively integrate temporal and spatial context information. Specifically, the proposed method includes a temporal context feature extraction module (TCE) and a extraction and integration feature module (EIF). TCE uses attention mechanism to effectively fuse temporal and spatial context information of keyframes. EIF is utilized to extract features from templates and search regions in multi-stage feature networks, as well as effectively combine the template and search regions features. By multi-level matching of templates and search features, non-target features can be suppressed to achieve discriminative feature extraction. Experimental results on visual tracking benchmarks including OTB, UAV123, GOT-10K, LASOT and TrackingNet demonstrate that TCFormer achieves the convincing performance outperforming most state-of-the-art trackers.
Summary of Static Graph Embedding Algorithms

A graph is used to describe the connections between entities. By conducting effective analysis of graphs, it is helpful to dig deeper into some of the hidden features of the graph itself, and to gain a deeper understanding of the content behind the data. Graph embedding aims at preserving the information in the original graph and representing the nodes in the graph as a low dimensional vector space. In recent years, graph embedding method is popular and widely used in different fields. In this paper, the static graph embedding methods based on dimensionality reduction, matrix decomposition, random walk and neural network are introduced, and the algorithm of each method is discussed. A statistical presentation was conducted on commonly used static graph datasets, and the application scenarios of graph embedding were summarized; Finally, the current shortcomings and the development trend of graph embedding were proposed.
Deep Learning in Image Classification: An Overview

Deep learning has been one of the main trends in machine learning, and one of the most popular scientific research trends in recent years, which has played a revolutionary role in the development of computer vision. As a vital part of computer vision, many researchers have been attracted to participate in the research of image classification. With the emergence and development of deep neural networks, combined with the emerging various training and optimization methods, it has achieved great success in this field. In recent years, Transformer-based models has achieved the best experimental results in image classification and many other fields. This article will compactly introduce the development process of deep learning in image classification in the past few years from three aspects of training and optimization methods, deep neural networks and Transformers.
Development of magnetic-sensor-based hand gesture recognition system for sign language

Sign language recognition is essential for the automatic translation of sign languages to enable communication for hearing-impaired people. This work proposes a system based on multiple magnetic sensors for recognizing hand gestures related to sign language alphabets. In particular, a magnetic detection system consisting of six magnetic sensor nodes measures the orientation of fingers and palms. A deep learning classification algorithm processes the measured orientation data. Experimental tests validate the proposed system and classification method. The results show that the proposed method provides close to 100% classification accuracy for 26 sign language alphabets under laboratory conditions. Thus, the feasibility of the proposed gesture recognition system for automatic translation of sign language alphabets is proved.
Visual cryptography scheme by freeform optics based on optimal mass transport

An information hiding scheme for visual cryptography based on freeform optics is provoked. The encryption and decryption of information are achieved by the redistribution of light energy between two freeform surfaces lenses. Each freeform lens provides a preset light energy distribution constructed by optimal mass transport theory, and the hidden information can be clearly displayed only by illuminating the encryptor with the distribution of the decryptor. The encryption key can be transmitted in the form of freeform lens coordinates on Internet, and the decryption process can be completed by a single computer. The simulation results show that the encrypted information can be clearly showed when using the preset decryptor distribution and decryption conditions. When encryptor is illuminated by other distribution or the decryption conditions do not match, the hidden information cannot be displayed. By increasing the complexity of decryptor distribution, the security of encrypted information can be further strengthened. This visual cryptography scheme admit the daily secret communication because of its good transmission privacy and strong security.
Research on image recognition based on neural network model learning algorithm

The model based on object detection algorithm shows great advantages in accuracy and precision rate. However, due to the different sizes of the targets to be detected in the images, coupled with the interference of factors such as occlusion and scene complexity, at the same time, the object detection has the problem of too small a percentage, which is prone to miss detection and false detection. Therefore, the performance of the model needs to be further improved. In this paper, based on the shortcomings of existing models, we propose the Invo-YOLOv5s model, which can well improve the accuracy of detection and anti-interference ability. We conducted experiments on the model on the helmet dataset, and the experiments showed that the Invo-YOLOv5s model was selected for training, and the final obtained model detection accuracy reached 94.9%, which is 2.3% higher than the accuracy of the original YOLO model.
A Lightweight YOLOv5 Model Integrating GhostNet and Attention Mechanism

Aiming at the problems of complex structure of neural network, large number of parameters and difficult deployment to terminal devices, this paper proposes an improved lightweight object detection model for the purpose of building a model with high classification accuracy and low complexity. First, the one-stage detector yolov5 is used as the base network. Considering the excessive redundant features of feature extraction, Ghost module is used to replace the traditional convolution, effectively allocate computing resources, to reduce the size of the model and improve the speed of model detection. When designing model bottleneck structure, Normalization-based Attention Module (NAM) is added to improve training efficiency and reduce model complexity. Experimental results show that compared with YOLOv5, the calculation speed of the proposed model is increased by 17% and the accuracy is reduced by less than 2%, which is of great significance in the environment of low-performance computing equipment.
A New Strategy for Reducing Errors in Scene Text Detection

Compared to regression based text detection methods, segmentation based methods can better handle irregularly shaped text regions and effectively reduce detection errors. They can flexibly cope with different text forms and better handle the problem of multilingual text detection. However, due to the limitations of segmentation algorithms themselves, segmentation based methods may result in significant errors in situations where the boundaries between text and non text regions are blurred. In this article, we propose a novel strategy to address a series of inaccuracies and errors that arise during the detection process. This method also simplifies some tedious operations during the detection process, thereby improving the overall performance of the model. We have conducted multiple experiments on multiple common benchmarks to verify the performance and effectiveness of our proposed model, which has reached the level of state-of-the-art methods in terms of accuracy and speed in detection results.
Parameter selection of Gaussian kernel for cost-sensitive support vector machines in imbalanced data classification

The classification of imbalanced datasets has recently gained significant attention and research, with cost-sensitive support vector machines (CS-SVM) demonstrating strong performance in addressing this issue. However, the performance of model usually involves parameter adjustment but does not understand its principle. This study examines how the CS-SVM performs when the Gaussian kernel function and penalty factor parameters in imbalanced datasets have varied values. On this basis, a new CS-SVM parameter optimization approach is proposed. This approach can reduce the search space of parameters. Compared with global search, the calculation time is significantly shortened. Also, a novel assessment index built on g-means is suggested in this study in order to make the class of minority more significant. The findings demonstrate that this approach can increase the recall to 1 of the class of the minority. Compared with other classification models, this approach has higher search efficiency and better average performance.
Decision Forest Training Accelerator Based on Binary Feature Decomposition

In recent years, while Deep Neural Networks (DNNs) have revolutionized various fields, it is widely acknowledged that they are not always the optimal solution, and complementary Machine Learning (ML) tools are necessary. For instance, developing DNN models that can effectively handle tabular data with rows and columns remains a challenging open question. Additionally, the difficulty of interpreting DNN models poses a significant obstacle that hinders their use in many practical applications where the interpretability of the inference results and the ability to offer advice on how to modify input for desired output are required. In such cases, Decision Forests (DFs) have been widely considered a promising solution.
“Software is the easy part of Software Engineering” - Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course

Capstone courses in undergraduate software engineering are a critical final milestone for students. These courses allow students to create a software solution and demonstrate the knowledge they accumulated in their degrees. However, a typical capstone project team is small containing no more than 5 students and function independently from other teams. To better reflect real-world software development and meet industry demands, we introduce in this paper our novel capstone course. Each student was assigned to a large-scale, multi-team (i.e., company) of up to 20 students to collaboratively build software. Students placed in a company gained first-hand experiences with respect to multi-team coordination, integration, communication, agile, and teamwork to build a microservices based project. Furthermore, each company was required to implement plug-and-play so that their services would be compatible with another company, thereby sharing common APIs. Through developing the product in autonomous sub-teams, the students enhanced not only their technical abilities but also their soft skills such as communication and coordination. More importantly, experiencing the challenges that arose from the multi-team project trained students to realize the pitfalls and advantages of organizational culture. Among many lessons learned from this course experience, students learned the critical importance of building team trust. We provide detailed information about our course structure, lessons learned, and propose recommendations for other universities and programs. Our work concerns educators interested in launching similar capstone projects so that students in other institutions can reap the benefits of large-scale, multi-team development.