检索结果-内蒙古大学图书馆

A novel diagnostic map for computer-aided diagnosis of skin cancer

IET image processing 2021年第4期15卷 897-907页

作者： Ashour, Amira S. Wahba, Maram A. Alaa, Eman Elsaid Guo, Yanhui Hawas, Ahmed Refaat Tanta Univ Fac Engn Dept Elect & Elect Commun Engn Tanta Egypt Univ Illinois Dept Comp Sci Springfield IL 62703 USA

In teledermoscopy, images are transmitted through a communication channel to the medical facility for medical consultation. This yields to bandwidth congestion and consumption of large storage size which impairs the transmission of the high-resolution dermoscopy image. This study proposes a novel technique by generating a diagnostic feature map, named diagonal compressive sensing (CS) features map. This proposed map is generated using the significant diagnostic features by aligning the feature vector in a diagonal map, which is then compressed using CS technique. Eventually, the recovered feature map at the receiver side is applied to the classification system for decision-making. In addition, physicians at the receiver side who were trained to read the feature maps can verify the classification decision, and then provide feedback to the patient. The results demonstrated that the proposed diagnostic map achieved less transmission time due to the small size of the feature map along with the compression process. Furthermore, the feature map has drastically improved the classification performance metrics, including the accuracy, which increased, for example, from 88.6% to 98% at 80% compression ratio compared to the traditional method on the compressed whole image.

关键词： Optical, image and video signal processing image recognition Biomedical communication computer vision and image processing techniques Other topics in statistics Optical and laser radiation (medical uses) Patient diagnostic methods and instrumentation Biology and medical computing

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Multi-view intrinsic low-rank representation for robust face recognition and clustering

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IET image processing 2021年第14期15卷 3573-3584页

作者： Wang, Zhi-yang Abhadiomhen, Stanley Ebhohimhen Liu, Zhi-feng Shen, Xiang-jun Gao, Wen-yun Li, Shu-ying JiangSu Univ Sch Comp Sci & Commun Engn Zhenjiang Jiangsu Peoples R China Univ Nigeria Dept Comp Sci Nsukka Nigeria Jiangsu Univ Jingkou New Generat Informat Technol Ind Inst Zhenjiang Jiangsu Peoples R China Nanjing Informat Technol Co LTD Nanjing Jiangsu Peoples R China Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Xian Univ Posts & Telecommun Sch Automat Xian 710121 Shaanxi Peoples R China

In the last years, subspace-based multi-view face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi-view low-rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low-rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low-rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low-rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state-of-the-art methods in classification and clustering.

关键词： image recognition computer vision and image processing techniques Other topics in statistics

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Real-time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

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IET image processing 2021年第14期15卷 3623-3637页

作者： Jia, Wei Xu, Shiquan Liang, Zhen Zhao, Yang Min, Hai Li, Shujie Yu, Ye Hefei Univ Technol Sch Comp Sci & Informat 193 Tunxi Rd Hefei Peoples R China

In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.

关键词： Optical, image and video signal processing image recognition computer vision and image processing techniques Video signal processing Traffic engineering computing Other topics in statistics

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Aircraft tracking in aerial videos based on fused RetinaNet and low-score detection classification

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IET image processing 2023年第3期17卷 687-708页

作者： Ahmed, M. Maher, Ali Bai, X. Beihang Univ Image Proc Ctr Beijing Peoples R China Mil Tech Coll Cairo Egypt Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Beijing Peoples R China

Aircraft tracking in aerial videos is a challenge due to the small size and appearance affinity with other targets. Recently, the tracking-by-detection trackers, such as simple online and realtime tracking (SORT), attracted the attention of many researchers due to the great evolution in deep learning-based object detectors. SORT depends on motion cues in solving the data association problem by matching high-score detection bounding boxes to tracklets. However, motion cues are insufficient to track occluded or similar aircraft efficiently. In addition, most occluded aircrafts have a low-confidence percentage. The main objective of this paper is to propose an F-SORT tracker, an enhanced SORT tracker with the following improvements. Firstly, fused RetinaNet for small aerial targets is used as a detection framework to boost the detectability of SORT. Then, aircraft appearance features are extracted from the shallowest prediction layer and utilized as additional cues to improve the data association algorithm. Finally, the extracted features distinguish aircraft online from backgrounds via a K-nearest neighbour classifier. Experiments on an airport dataset show that F-SORT can significantly improve aircraft tracking. F-SORT outperforms other state-of-the-art trackers, achieving 72.75%, 59.63%, and 82.89% on MOTA, HOTA, and IDF1. In addition, F-SORT classifying algorithm boosts other trackers when applied.

关键词： airports aircraft appearance features feature extraction low-confidence percentage appearance affinity video signal processing Sensor fusion Aerospace engineering computing image classification enhanced SORT tracker deep learning-based object detectors object tracking detection framework Neural nets F-SORT classifying algorithm aerial videos image fusion computer vision and image processing techniques simple online tracking detectability data association problem object detection aircraft tracking deep learning (artificial intelligence) image recognition realtime tracking target tracking Video signal processing F-SORT tracker high-score detection fused RetinaNet tracking-by-detection trackers low-score detection classification aerial targets occluded aircrafts motion cues

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A robust tracking algorithm for a human-following mobile robot

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IET image processing 2021年第3期15卷 786-796页

作者： Tsai, Tsung-Han Yao, Chia-Hsiang Natl Cent Univ Dept Elect Engn Taoyuan Taiwan

The capability of recognizing and tracking a specific human is considered a key technique for serving mobile robots. This paper proposes a real-time tracker that uses a stereo camera to track specific people in different environments. A tracker has been designed to detect the selected target from multiple people by use of block matching algorithm, human detection, and colour histogram comparison. Considering the similar colour of the objects, the predictor of the Kalman filter determines the position of the object. Finally, the mobile robot is moved according to the tracking results with a relative distance between the robot and the target. The effectiveness of the proposed method is demonstrated by experiments on many video sequences and real environments.

关键词： Optical, image and video signal processing image recognition Mobile robots computer vision and image processing techniques Video signal processing

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Crowd counting with segmentation attention convolutional neural network

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IET image processing 2021年第6期15卷 1221-1231页

作者： Chen, Jiwei Wang, Zengfu Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Hefei Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei Anhui Peoples R China

Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi-scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.

关键词： image recognition computer vision and image processing techniques Neural nets

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New image quality metric using random projection

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IET image processing 2012年第9期6卷 1246-1255页

作者： Kim, D. -O Park, R. -H. Sogang Univ Dept Elect Engn Seoul 121742 South Korea

This study proposes a new image quality metric using random projection (RP). Generally, distortion is wide spread over the entire region in an image when the image is degraded by compression, blurring, additive noise and so on, which signifies that it is not necessary to compute the degree of distortion at every pixel. RP has been used for representing a signal to a small number of measurements. In this study, to evaluate image quality, distortions between the reference and distorted images are measured at a small number of pixels that are preselected or randomly chosen through the RP and to beat the simple random sampling, the saliency-based weighted sum is used. Experimental results with the laboratory for image and video engineering data set show the effectiveness of the proposed quality metric.

关键词： additive noise video engineering data set blurring computer vision and image processing techniques reference image random projection distorted image image and video coding image coding image compression image quality metric saliency-based weighted sum data compression

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A stochastic approach for automated brain MRI segmentation

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IET image processing 2021年第3期15卷 735-745页

作者： Chatterjee, Pubali Sharma, Kaushik Das Chakrabarti, Amlan Univ Calcutta AK Choudhury Sch Informat Technol Kolkata W Bengal India Univ Calcutta Dept Appl Phys Kolkata W Bengal India

This paper presents an approach to segment lesions from brain magnetic resonance images in a fully automatic manner. The proposed idea leverages the strength of classical random walker algorithm and graph cut optimization technique in a single framework. We demonstrate that formulating a "prior" from a stochastic model can ameliorate the need of manually selected seed selection process in the random walker framework, thus making the algorithm fully automatic in a generic manner. By analytically solving a linear system of equations in the random walk process, initial labelling is an element of [0, 1] of each pixel in the image are computed to obtain the likelihood probability. These probabilities are then used to compute the likelihood for the data fidelity term in the energy function to compute the final segmentation, which is minimized by min cut max flow algorithm. Experimental results show the superiority of the proposed method over the state-of-the-art techniques on publicly available datasets.

关键词： Algebra, set theory, and graph theory Probability theory, stochastic processes, and statistics Biophysics of neurophysiological processes Medical magnetic resonance imaging and spectroscopy Patient diagnostic methods and instrumentation Other topics in statistics Combinatorial mathematics Optical, image and video signal processing Biomedical magnetic resonance imaging and spectroscopy Other topics in statistics Combinatorial mathematics computer vision and image processing techniques Biology and medical computing

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Phone-vehicle trajectory matching framework based on ALPR and cellular signalling data

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第1期15卷 107-118页

作者： Wan, Wei Cai, Ming Sun Yat Sen Univ Guangdong Prov Key Lab Intelligent Transportat Sy Sch Intelligent Syst Engn Guangzhou Peoples R China Sun Yat Sen Univ Res Ctr Intelligent Transportat Syst Sch Intelligent Syst Engn Guangzhou Peoples R China

With the advancement of positioning techniques, a large amount of trajectory data has been produced. Matching vehicles with mobile phones using different trajectories can benefit many applications, such as driving behaviour analysis and travel mode split. Moreover, as a privacy attack method, it can provide theoretical inspiration for privacy protection theory. To address this problem, a new trajectory matching framework for processing massive Automatic License Plate Recognition (ALPR) and cellular signalling data is proposed. Information entropy was adopted to address the movement frequency of trajectories and then the infrequent vehicles and phones that did not meet the threshold were pruned. Next, an effective matching algorithm was devised to match the trajectories of vehicles and mobile phones. Moreover, to solve the problem of obtaining a small number of matching results, a data augmentation algorithm was proposed to add new, matching records. Last, a classification model was constructed with LightGBM to determine whether the vehicle matches the phone. Experimental results on real datasets show that the framework outperforms typical techniques in terms of effectiveness and efficiency. The data obtained by data augmentation have distribution characteristics similar to those of the original data. The proposed classification model achieves an accuracy of 93.6%.

关键词： image recognition computer vision and image processing techniques Data security Traffic engineering computing

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An unsupervised multi-focus image fusion method based on Transformer and U-Net

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IET image processing 2023年第3期17卷 733-746页

作者： Jin, Xin Xi, Xiuliang Zhou, Ding Ren, Xiaoxuan Yang, Jie Jiang, Qian Yunnan Univ Engn Res Ctr Cyberspace Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China Zunyi Normal Univ Sch Phys & Elect Sci Zunyi Guizhou Peoples R China

This work presents a multi-focus image fusion method based on Transformer and U-Net with an unsupervised training fashion. In this work, the authors introduce Transformer into image fusion because it has great ability to capture the global dependencies and low-frequency features. In image processing, convolutional neural network (CNN) has good performance of detailed feature extraction but a weakness for global feature extraction, and Transformer has limited power in local or detailed information extraction but a strong capacity for global feature extraction. Thus, this work combines the advantages of CNN and Transformer to propose an unsupervised decision map making model for image fusion joint U-Net. The authors construct a model including feature extraction and feature reconstruction modules which correspond to the encoder and decoder network of U-Net, respectively. In addition, perceptual loss is introduced on the basis of structural similarity loss because the combination of these two loss functions can achieve better performance with lower training cost. Experiments show that the proposed image fusion method performs better fusion performance compared with the existing methods.

关键词： feature extraction multifocus image fusion method Sensor fusion Transformer feature reconstruction modules unsupervised learning fusion performance low-frequency features global dependencies Neural nets detailed feature extraction Optical, image and video signal processing image fusion computer vision and image processing techniques convolutional neural nets image fusion joint U-Net deep learning (artificial intelligence) image processing global feature extraction local information extraction Unsupervised learning unsupervised decision map making model detailed information extraction unsupervised training fashion

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