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Damage Analysis of Urban Comprehensive Pipe Gallery Caused by Internal Gas Explosion Based on HHT Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-18 Linna Li, Zhengying Ma, Dongwang Zhong, Tengfei Li, Qi Zhang
In this paper, the method of embedding piezoelectric ceramic sensors is used to test the damage of the materials and models of the urban comprehensive pipe gallery. The monitoring signal is processed by HHT method, and the frequency and energy changes of the piezoelectric signal before and after the explosion were analyzed, thus the damage characteristics of the urban comprehensive pipe gallery under
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CONHyperKGE: Using Contrastive Learning in Hyperbolic Space for Knowledge Graph Embedding Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-18 Mandeng Gao, Shengwei Tian, Long Yu
The embedding of Knowledge Graphs (KGs) in hyperbolic space has recently received great attention in the field of deep learning because it can provide more accurate and concise representations of hierarchical structures compared to Euclidean spaces and complex spaces. Although hyperbolic space embeddings have shown significant improvements over Euclidean spaces and complex space embeddings in handling
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The Deep Hybrid Neural Network and an Application on Polyp Detection Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-18 Yi-Ta Wu, Frank Y. Shih, Cheng-Long Wang, Kuang-Ting Hsiao, You-Cheng Liu, Fu-Chieh Chang, En-Da Yu
Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution
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Face Detection Framework for Accelerated Analysis of High-Quality Multimedia Content Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-18 Akshay Mool, Jeebananda Panda, Kapil Sharma
Modern face detection algorithms fail to provide optimal results when they have to deal with larger amounts of data per frame while processing higher quality videos. This paper tackles that problem and offers a solution to deploy commercially used state-of-the-art face detection algorithms to process only the regions of interest in a frame, and discard the rest to decrease the data to be processed
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Medical Named Entity Recognition Model Based on Knowledge Graph Enhancement Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-14 Yonghe Lu, Ruijie Zhao, Xiuxian Wen, Xinyu Tong, Dingcheng Xiang, Jinxia Zhang
To improve the recognition ability of clinical named entity recognition (CNER) in a limited number of Chinese electronic medical records, it provides meaningful support for clinical advanced knowledge extraction. In this paper, using CCKS2019 Chinese electronic medical record as an experimental data source, a fusion model enhanced by knowledge graph (KG) is proposed, and the model is applied to specific
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Detection of Dense Built-Up Area in Low-Resolution Satellite Images Using Deep Learning and DBSCAN Approaches Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-10 Shambo Chatterjee, Soumya Bhattacharyya, Sourav Saha, Anindya Halder, Priya Ranjan Sinha Mahapatra
Recent developments in satellite image processing tend to eliminate the need for intensive on-site surveys of urban or rural areas for infrastructure allocation planning. In particular, the detection of buildings in satellite images can significantly aid in rural or urban planning. However, detecting individual buildings in low-resolution satellite images is challenging due to a lack of visual clarity
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Rolling Bearing Composite Fault Diagnosis Method Based on Convolutional Neural Network Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-05 Song Chen, Dong-ting Guo, Li-ai Chen, Da-gui Wang
Rolling bearing feature extraction and fault identification techniques using deep learning algorithms have been widely adopted in recent years. We proposed a method for diagnosing composite faults in rolling bearings by employing multisensor decision fusion and convolutional neural networks. Different types of bearing faults and eccentricity faults have different fault eigenfrequencies in vibration
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Altered Handwritten Text Detection in Document Images Using Deep Learning Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-05 Gayatri Patil, Shivakumara Palaiahnakote, Shivanand S. Gornale, Daniel P. Lopresti
Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct
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UAV Target Tracking Algorithm Based on Illumination Adaptation and Future Awareness in Low Illumination Scenes Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-05 Yuan-Lian Huo, Bo Chen, Jin-Shi Zhang, Qiao-Sen Zhang
Aiming at problems such as tracking failure caused by illumination changes often encountered during unmanned aerial vehicle (UAV) tracking, a target tracking algorithm with illumination adaptive and future-aware correlation filters is proposed based on the background-aware correlation filters (BACF) algorithm, which realizes reliable UAV tracking tasks at night. First, the dark scene is recognized
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An Analysis of Hierarchical Routing Strategy with Advanced Additional Sensors in WSNs Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-04 Dongmei Xing
Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called
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Advancing Handwritten Musical Notation Recognition Using Deep Learning: A Convolutional Neural Network-Based Approach with Improved Accuracy Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-03 Ee Hern Kheng, Chia Pao Liew, Tianhao Lan, Kim Geok Tan
The use of computers to read musical scores is referred to as optical music recognition (OMR). The recent advancements in artificial intelligence and big data have led to the development of deep learning approaches for recognizing musical notes. Previous research has shown that there is a lot of room for improvement in handwritten musical notation recognition systems due to differences in writing styles
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Effective Document Image Rectification via a Deep Learning Framework Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-05 Hsiau-Wen Lin, Hwei Jen Lin, Yihjia Tsai, Yoshimasa Tokuyama, Chou-Wei Kong
This paper proposes an efficient method for rectifying distorted document images via deep learning, ultimately improving the legibility of graphics and text in documents. The framework comprises two interconnected UNets, working in tandem to predict a 3D coordinate map and a forward map for the input distorted document image, respectively. At the beginning of the process, a page mask is predicted and
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Few-Shot Text Classification with an Efficient Prompt Tuning Method in Meta-Learning Framework Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-01 Xiaobao Lv
Meta-learning stands as a prevalent framework utilized in few-shot learning methods. Nonetheless, its efficacy hinges on substantial data availability during meta-training. Recent work adeptly tackled this hurdle by synergizing prompt tuning with the meta-learning paradigm, consequently attaining unparalleled performance on four benchmarks (FewRel, HuffPost, Reuters and Amazon). Nonetheless, the implementation
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Pinball-OCSVM for Early-Stage COVID-19 Diagnosis with Limited Posteroanterior Chest X-Ray Images Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-04-01 Sanjay Kumar Sonbhadra, Sonali Agarwal, P. Nagabhushan
The conventional way of respiratory coronavirus disease 2019 (COVID-19) diagnosis is reverse transcription polymerase chain reaction (RT-PCR), which is less sensitive during early stages; especially if the patient is asymptomatic, which may further cause more severe pneumonia. In this context, several deep learning models have been proposed to identify pulmonary infections using publicly available
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Perspective Collaboration for Multi-domain Fake News Detection Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-27 Hui Li, Yuanyuan Jiang, Xing Li, Chenxi Wang, Yanyan Chen, Haining Li
Fake news is widely spread on social media. Much research works have been done on automatic fake news detection in single domain. However, fake news exists in various domains, so the detection model based on single domain is less effective in multiple domain scenes. To improve the detection ability of multi-domain fake news, we propose a perspective collaboration for multi-domain fake news detection
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Intelligent Classification of Metallographic Based on Improved Deep Residual Efficiency Networks Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-21 Xiaohong Huang, Yanping Liu, Xueqian Qi, Yue Song
The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant
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PRLDPC: A Heuristics Prototype Reduction Method Based on Supervised Local Density Clustering for Instance-Based Classifiers Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-20 Xing Huang, Junnan Li
The prototype reduction (PR) methods, as an important data pre-processing task, can improve instance-based classifiers by removing noise and/or redundant samples. Recently, a series of PR methods with different heuristic strategies have been developed. Among them, clustering-based PR methods have shown competitive performance. Yet, they still suffer from the following issues: (a) most methods heavily
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A Domain Variable Prior Based Multi-Style Transfer Network for Data Augmentation of Tidal Stream Turbine Rotor Image Dataset Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-15 Guohan Jiang, Tianzhen Wang, Dingding Yang, Jingyi You
The style of the underwater images varies according to the region of the sea. However, Tidal Stream Turbine (TST) rotor images captured in the laboratory environment cannot reflect the real underwater environment in image style, resulting in poor generalization of image signal-based fault detection algorithms. Due to the fixed capture position of the camera, the TST rotor image dataset has a high semantic
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Scale Enhancement Network for Object Detection in Aerial Images Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-13 Shihan Mao, Zhi Wang, Qineng He, Zhangqing Zhu
The main challenge for object detection in aerial images is small object detection. Most existing methods use feature fusion strategies to enhance small object features in shallow layers but ignore the problem of inconsistent small object local region responses between feature layers, namely the semantic gap, which may lead to underutilization of small object information in multiple feature layers
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DAGAN: A GAN Network for Image Denoising of Medical Images Using Deep Learning of Residual Attention Structures Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-13 Guoxiang Tong, Fangning Hu, Hongjun Liu
Medical images are susceptible to noise and artifacts, so denoising becomes an essential pre-processing technique for further medical image processing stages. We propose a medical image denoising method based on dual-attention mechanism for generative adversarial networks (GANs). The method is based on a GAN model with fused residual structure and introduces a global skip-layer connection structure
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Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-07 Debasmita GhoshRoy, P. A. Alvi, KC Santosh
Designing effective AI models becomes a challenge when dealing with imbalanced/skewed class distributions in datasets. Addressing this, re-sampling techniques often come into play as potential solutions. In this investigation, we delve into the male fertility dataset, exploring 14 re-sampling approaches to understand their impact on enhancing predictive model performance. The research employs conventional
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Residual Network for Image Compression Artifact Reduction Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-07 Jianhua hu, Guixiang Luo, Bo Wang, Weimei Wu, Jiahui Yang, Jianding Guo
This paper proposes an image compression algorithm based on Swin Transformer and residual network (STRN), aiming to reduce blurring and distortions in traditionally compressed images. The algorithm utilizes a dual-channel mechanism to remove artifacts from the image, which takes advantage of the complementary features of the transform and residual networks. The Swin Transformer networks address the
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A Rate Control Scheme for VVC Intercoding Using a Linear Model Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-03-05 Heqiang Wang, Xuekai Wei, Weizhi Xian, Jun Luo, Huayan Pu, Zhigang Chu, Xin Wang, Xueyong Xu, Chang Lu, Mingliang Zhou
Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation
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Identification Method of Unmanned Aerial Vehicle Graphical Control Strategy Based on Cloud Server Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-29 Zhengyu Liu, Zhenbang Cheng, Yu Liu, Qing Jiang
With the rapid development of unmanned aerial vehicle (UAV) technology, UAV has been widely used in agricultural plant protection, electric power inspection, security patrols, and other fields. However, the control system of the UAV is a complex human–computer interaction system, which requires higher requirements in practical applications. Due to differences in hardware design, software development
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Research on Multi-Source Heterogeneous Big Data Fusion Method Based on Feature Level Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-29 Yanyan Chen, Chenxi Wang, Yuchen Zhou, Yuhang Zuo, Zixuan Yang, Hui Li, Juan Yang
With the development of research on multi-modal data fusion and its combination with online data management, the application of multi-modal big data fusion in information management systems is more and more extensive. How to integrate multi-modal big data effectively is the key technology to building an efficient information management system. In this paper, based on the combination of a multi-support
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Head Pose Estimation Based on Multi-Level Feature Fusion Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-28 Chunman Yan, Xiao Zhang
Head Pose Estimation (HPE) has a wide range of applications in computer vision, but still faces challenges: (1) Existing studies commonly use Euler angles or quaternions as pose labels, which may lead to discontinuity problems. (2) HPE does not effectively address regression via rotated matrices. (3) There is a low recognition rate in complex scenes, high computational requirements, etc. This paper
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Optimized Ensemble Machine Learning Approach for Emotion Detection from Thermal Images Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-22 Jayaprakash Katual, Amit Kaul
Emotions indicate the feelings of the individual which are linked with personal experiences, moods, and affective states. Detection of emotion can be helpful in many fields like maintaining a patient’s psychological well-being, surveillance, driver monitoring, etc. In this paper, an effective machine learning approach has been put forth for emotion detection where an ensemble of three out of five best-performing
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A Novel Multi-Data-Augmentation and Multi-Deep-Learning Framework for Counting Small Vehicles and Crowds Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-20 Chun-Ming Tsai, Frank Y. Shih
Counting small pixel-sized vehicles and crowds in unmanned aerial vehicles (UAV) images is crucial across diverse fields, including geographic information collection, traffic monitoring, item delivery, communication network relay stations, as well as target segmentation, detection, and tracking. This task poses significant challenges due to factors such as varying view angles, non-fixed drone cameras
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Medical Image Segmentation Using Grey Wolf-Based U-Net with Bi-Directional Convolutional LSTM Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-19 G. Tamilmani, Phaneendra Varma CH, V. Brindha Devi, Ramesh Babu G
In recent years, deep learning-based networks have been able to achieve state-of-the-art performance in medical image segmentation. U-Net, one of the currently available networks, has proven to be effective when applied to the segmentation of medical images. A Convolutional Neural Network’s (CNN) performance is heavily dependent on the network’s architecture and associated parameters. There are many
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Hybrid Optimized Deep Learning-Based Bacilli Segmentation and Infection-Level Identification of Tuberculosis Using Sputum Images Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-14 P. Sathish, Preethi D, Clara Shanthi Dominic, G. Kadiravan
Presently, one of the foremost health issues and an extremely transferrable disease is Tuberculosis which is spreading worldwide. Tuberculosis is generally produced by mycobacterium tuberculosis and can cause death if it is not detected at premature stages. Therefore, a precise and efficient approach is essential for the identification of tuberculosis. The physical analysis of sputum smears through
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Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-14 Suresh G, Manikandan G, Bhuvaneswari G, Shanthakumar P
Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research
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Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-09 Xuekai Wei, Qunyue Huang, Bin Fang, Lei Ouyang, Weizhi Xian, Jun Luo, Huayan Pu, Xueyong Xu, Chang Lu, Hao Nan, Xu Liu, Yachao Li, Mingliang Zhou
With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image
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LCSTR: Scene Text Recognition with Large Convolutional Kernels Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-09 Jiale Wang, Lina Yang, Jing Wang, Haoyan Yang, Lin Bai, Patrick Shen-Pei Wang, Xichun Li, Huiwu Luo, Huafu Xu
The task of scene text recognition involves processing information from two modalities: images and text, thereby requiring models to have the ability to extract features from images and model sequences simultaneously. Although linguistic knowledge greatly aids scene text recognition tasks, the extensive use of language models in sequence modeling and model prediction stages in recent years has made
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A Gaze Estimation Method Based on Binocular Cameras Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-02-01 Zihan Wu, Changyuan Wang, Gang Sun, Zhen Fu
In recent years, multi-stream gaze estimation methods have become mainstream, which estimate gaze point by eye picture or combine with facial appearance, have achieved considerable accuracy. However, these methods based on a single camera fail to obtain accurate eye spatial position information. To address this issue, we propose a multi-stream gaze estimation model that incorporates spatial position
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Boosting Multi-Label Classification Performance Through Meta-Model Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-31 Sonia Guehria, Habiba Belleili, Nabiha Azizi, Djamel Zenakhra
Multi-label classification problem, where each instance can be associated with multiple labels, has received considerable attention from machine learning community. To address the inherent challenges of multi-label classification including data imbalance, label dependence, and high dimensionality, ensemble approaches have been developed, gaining popularity across various real-world applications. This
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All-Day Object Detection and Recognition for Blind Zones of Vehicles Using Deep Learning Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-31 Tsorng-Lin Chia, Pei-June Liu, Ping-Sheng Huang
The neglect of perception ability to the surrounding traffic conditions has always been the major cause of traffic accidents and the inattention to blind spots is the most important factor during driving. Existing solutions are facing the problems of using expensive equipment, wrong classification of the target object type, not suitable for nighttime, and incorrectly determining if the target object
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Spatial Decomposition and Aggregation for Attention in Convolutional Neural Networks Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-31 Meng Zhu, Weidong Min, Hongyue Xiang, Cheng Zha, Zheng Huang, Longfei Li, Qiyan Fu
Channel attention has been shown to improve the performance of deep convolutional neural networks efficiently. Channel attention adaptively recalibrates the importance of each channel, determining what to attend to. However, channel attention only encodes inter-channel information but neglects the importance of positional information. Positional information is crucial in determining where to attend
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Neural Network-Based Algorithm for Identification of Recaptured Images Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 Changming Liu, Yanjun Sun, Lin Deng, Yan Sun
With the improvement of digital image display technology, the “secondary imaging” caused by digital cameras is also gradually popularized, and the quality of the recaptured image formed by this imaging is also getting higher and higher, and this kind of high-quality fake image has caused great threat to digital images security. We propose a neural network-based recaptured image identification algorithm
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A Locally Weighted Linear Regression-Based Approach for Arbitrary Moving Shaky and Nonshaky Video Classification Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 Arnab Halder, Palaiahnakote Shivakumara, Umapada Pal, Michael Blumenstein, Palash Ghosal
Classification and identification of objects are complex and challenging in pattern recognition and artificial intelligence if a shaky and nonshaky camera captures the videos at different distances during the day and nighttime. This work presents a model for classifying a given video as a static, uniform, or arbitrarily moving videos so that the complexity of the problem can be reduced. To avoid the
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An End-to-End Video Coding Method via Adaptive Vision Transformer Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 Haoyan Yang, Mingliang Zhou, Zhaowei Shang, Huayan Pu, Jun Luo, Xiaoxu Huang, Shilong Wang, Huajun Cao, Xuekai Wei, Weizhi Xian
Deep learning-based video coding methods have demonstrated superior performance compared to classical video coding standards in recent years. The vast majority of the existing deep video coding (DVC) networks are based on convolutional neural networks (CNNs), and their main drawback is that since CNNs are affected by the size of the receptive field, they cannot effectively handle long-range dependencies
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Transformer with a Parallel Decoder for Image Captioning Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 Peilang Wei, Xu Liu, Jun Luo, Huayan Pu, Xiaoxu Huang, Shilong Wang, Huajun Cao, Shouhong Yang, Xu Zhuang, Jason Wang, Hong Yue, Cheng Ji, Mingliang Zhou
In this paper, a parallel decoder and a word group prediction module are proposed to speed up decoding and improve the effect of captions. The features of the image extracted by the encoder are linearly projected to different word groups, and then a unique relaxed mask matrix is designed to improve the decoding speed and the caption effect. First, since image captioning is composed of many words, sentences
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Deep Residual Network with Pelican Cuckoo Search for Traffic Sign Detection Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 T. Kumaravel, P. Natesan
The timely and precise discovery of traffic signs is considered an effective part of modeling automated vehicle driving. However, the dimension of traffic signs accounted for a lower ratio of input pictures which elevated the complexity of discovery. Hence, a new model is devised using faster region-based convolution neural network (faster R-CNN) traffic for detecting traffic signs. The Region of Interest
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M2-YOLOX: A Novel Method for Object Detection Based on an Improved YOLOX Algorithm Introducing a Global Attention Mechanism and a Feature Enhancement Module Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2024-01-29 Xiaofeng Bai, Kaijun Wu, Chenshuai Bai
Deep learning-based algorithms for detecting objects in remote sensing images have produced excellent results recently. However, the target recognition and classification process of remote sensing images has problems such as dense targets, uneven distribution, large-scale changes and complex backgrounds. In order to improve the effectiveness of existing detection methods, based on the YOLOX algorithm
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Multi-Scale Feature Refined Network for Human Pose Estimation Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-11-09 Qiaoning Yang, Xiaodong Ji, Xiuhui Yang
Occlusive keypoints has been a challenge for human pose estimation, especially the mutual occlusion of human bodies. One possible solution to this problem is to utilize multi-scale features, where small scale features are capable of identifying keypoints, while large-scale features can capture the relationship between keypoints. Feature fusion among multi-scale features allows for the exchange of information
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Depth-Constrained Network for Multi-Scale Object Detection Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-24 Guohua Liu, Yijun Li
Challenges such as complex backgrounds, drastic variations in target scales, and dense distributions exist in natural scenes. Some algorithms optimize multi-scale object detection performance by combining low-level and high-level information through feature fusion strategies. However, these methods overlook the inherent spatial properties of objects and the relationships between foreground and background
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Drug Toxicity Prediction by Machine Learning Approaches Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-24 Yucong Shen, Frank Y. Shih, Hao Chen
Drug property prediction, especially toxicity, helps reduce risks in a range of real-world applications. In this paper, we aim to apply various machine-learning models for solving the drug toxicity prediction problem. Among various machine-learning approaches, we select five suitable representatives: random forest, multi-layer perceptron, logistic regression, graph convolutional neural network, and
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Counting with Self-Weighted Multi-Scale Fusion Networks Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-19 Xin Xiong, Jie Shen, Ying Li, Wei He, Peng Li, Wenjie Yan
Because of the large-scale variation, counting in scenes of different densities is an extremely difficult task. In this paper, based on the attention mechanism, we propose a new self-weighted multi-scale fusion network structure named SMFNet to solve the problem of multi-scale changes and can significantly improve the effect of crowd counting in monitoring scene. The proposed SMFNet uses VGG as the
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A Novel Thanka Image Inpainting Method with Euler’s Elastica and Iterative Denoising and Backward Projections Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-08 Qiaoqiao Li, Weilan Wang
This paper presents a brand-new Thanka picture inpainting technique based on Euler’s elastica, iterative denoising, and backward projections (EEIDBP). Specifically, a model of Euler’s elastica is introduced to estimate the original observation due to its lower staircasing effects and better approximation of natural images. A method for backward projection and iterative denoising is applied to achieve
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Copy-Move Forgery Detection and Localization Using Deep Learning Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-04 Fatemeh Zare Mehrjardi, Ali Mohammad Latif, Mohsen Sardari Zarchi
Forgery detection is one of the challenging subjects in computer vision. Forgery is performed using image manipulation with editor tools. Image manipulation tries to change the concept of the image but preserves the integrity of the texture and structure of the image as much as possible. Images are used as evidence in some applications, so if the images are manipulated, they will not be reliable. The
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A Framework for Personalized Human Activity Recognition Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-08-01 Hasan Ali Eri̇ş, Mehmet Ali Ertürk, Muhammed Ali Aydın
In today’s world, Human Activity Recognition (HAR) through video streams is actively used in every aspect of our life, such as automated surveillance systems and sports statistics are computed according to the videos with the help of HAR. Activity detection is not a new subject, and several methods are available. However, the most recent and most promising techniques rely on Convolutional Neural Networks
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Deepfake Speech Recognition and Detection Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-21 Hung-Chang Chang
Deepfake technology, especially deep voice, which has been derived from artificial intelligence in recent years, is potentially harmful, and the public is not yet wary. However, many speech synthesis models measure the degree of true restitution by Mean Opinion Rating (MOS), a subjective assessment of naturalness and quality of speech by human subjects, but in future it will be difficult to distinguish
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An Adaptive Ant Colony Algorithm Based on Local Information Entropy to Solve Distributed Constraint Optimization Problems Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-21 Meifeng Shi, Shichuan Xiao, Xin Feng
As a meta-heuristic algorithm, the ant colony algorithm has been successfully used to solve various combinatorial optimization problems. However, the existing algorithm that takes the power of ants to solve distributed constraint optimization problems (ACO_DCOP) is easy to fall into local optima. To deal with this issue, this paper presents an adaptive ant colony algorithm based on local information
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DOMOPT: A Detection-Based Online Multi-Object Pedestrian Tracking Network for Videos Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-21 Ruohong Huan, Shuaishuai Zheng, Chaojie Xie, Peng Chen, Ronghua Liang
Due to the problem of low tracking accuracy and weak tracking stability of current multi-object pedestrian tracking algorithms in complex scenes for videos, a Detection-based Online Multi-Object Pedestrian Tracking (DOMOPT) network is proposed. First, a Multi-Level Feature Fusion (MLFF) pedestrian detection network is proposed based on the Center and Scale Prediction (CSP) algorithm. The pyramid convolutional
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Intelligent Inversion of Coastal Earth Resistivity Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-20 Bo Tan, Zhuohong Pan, Xuefang Tong, Yan Wang, Xianghan Wang, Lei Gao
Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction
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A Method on Classification and Recognition of Noisy Plant Images Based on Visual Domain Perception Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-19 Hongbiao Xie, Mingkun Feng, Zhijie Lin, Jiyi Wu, Zhe Feng
At present, some achievements have been made in the research of plant leaf classification such as the introduction of artificial intelligence algorithm. But there are still some problems. First, the existing achievements do not consider the subjective perception mechanism and role of human visual system in leaf classification data labels. Second, the implementation of the deep learning algorithm completely
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Customized Information Extraction and Processing Pipeline for Commercial Invoices Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-17 Pierce Lai, Abhishek Mohan, Seok Kim, Jung Soo Victor Chu, Samuel Lee, Prabhakar Kafle, Patrick Wang
Extracting information from scanned invoices and other commercial documents, a critical component of corporate function, typically requires significant manual processing. Much research has been conducted in the field of automated information extraction and document processing to alleviate the manual resources used for document analysis, but resultant literature and commercially available products have
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A Novel Sentimental Analysis for Response to Natural Disaster on Twitter Data Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-17 Sachin Minocha, Birmohan Singh
The response to a natural disaster ultimately depends on credible and real-time information regarding impacted people and areas. Nowadays, social media platforms such as Twitter have emerged as the primary and fastest means of disseminating information. Due to the massive, imprecise, and redundant information on Twitter, efficient automatic sentiment analysis (SA) plays a crucial role in enhancing
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Deep Active Recognition through Online Cognitive Learning Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-14 Jing Yang, Wencang Zhao, Minghua Lu, Jincai Huang
Deep models need a large number of labeled samples to be trained. Furthermore, in practical application settings where objects’ features are added or changed over time, it is difficult and expensive to get enough labeled samples in the beginning. Cognitive learning mechanism can actively raise the deep models’ proficiency online with a few training labels gradually. In this paper, inspired by human
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Quality Inspection of 3D Printed Tubular Tissue Based on Machine Vision Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-14 Xiaoyan Wu, Shu Wang
This study investigated the three-dimensional (3D) printing of tubular tissue, especially vascular tissue, using a self-developed 3D bioprinter platform and tubular tissue support frame system based on machine vision technology. A 3D printing quality inspection scheme for tubular tissue based on machine vision was proposed by combining the current advanced image acquisition sensor device and theoretical
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Energy-Saving Strategy Based on Image Super-Resolution for Wireless Image Sensor Networks Assisted by Cloud Int. J. Pattern Recognit. Artif. Intell. (IF 1.5) Pub Date : 2023-07-07 Yalin Nie, Lei Gong, Zeyu Sun
Wireless image sensor networks (WISNs) collect surveillance images, resulting in copious quantities of data requiring processing and transmission within the network. To reduce and balance energy expenditure during in-network image data processing and transmission, this study introduces an energy-saving strategy based on image super-resolution for WISNs assisted by cloud. The strategy constructs an