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IET IMAGE PROCESSING 2019年第5期13卷 722-728页

作者： Li, Ning Ji, Qingge Ma, Tianjun Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou 510006 Guangdong Peoples R China

Owing to convolutional neural network (CNN) models' success in various fields of computer vision, the authors proposed an advanced convolutional network (ACTNet) to enhance the accuracy of visual tracking. Different from prior methods, they regard a CNN as not only a semantic feature map extractor but also a position predictor. Rectified Linear Unit (RLU) and sigmoid are both used in ACTNet for feature extraction and position determination. To avoid overfitting in pre-training, they introduce adding Erlang noise to create more training samples and to improve the robustness of each base learner. Experiments on widely used evaluation datasets demonstrate that their proposed ACT method outperforms state-of-the-art methods.

关键词： feature extraction learning (artificial intelligence) computer vision object tracking convolutional neural nets image denoising position determination ACT method ACTNet visual tracking convolutional neural network models CNN computer vision advanced convolutional network semantic feature map extractor position predictor feature extraction ReLU sigmoid Erlang noise

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An interactive instance segmentation system with multi-resolution convolutional neural networks

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IET COMPUTER VISION 2021年第2期15卷 99-109页

作者： Sung, Po-Wei Yang, Wei-Jong Yang, Jar-Ferr Chan, Din-Yuan Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Tainan Taiwan Natl Chiayi Univ Dept Comp Sci & Informat Engn Chiayi Taiwan

In this paper, a fast interactive instance segmentation (IIS) system is proposed and it is composed of an effective heatmap generator, a multi-resolution network (MRNet), and an adaptive threshold refiner to promptly and precisely predict the masks of the objects. The proposed heatmap generator after interaction clicks can help the MRNet to successfully learn the sensitive features for better prediction. Based on convolutional neural network models, the proposed MRNet backbone produces multiple features across multiple resolutions and can intrinsically predict the sharp contour of the object. After the probabilistic prediction achieved by the MRNet, the Otsu's threshold refiner is proposed to further remove some uncertain pixels in the predicted mask. Experimental results demonstrate that the proposed IIS system can promptly predict sharp masks of the targeted objects with mIoU of 89.1% in PASCAL VOC 2012 [1] validation set. Compared to other existing interactive methods, the proposed system can effectively predict the segmentation mask with higher accuracy and less interaction efforts.

关键词： terrain mapping targeted objects interaction efforts learning (artificial intelligence) interactive methods interaction clicks object detection data visualisation sharp contour convolutional neural nets convolutional neural network models heatmap generator interactive instance segmentation system MRNet backbone multiple resolutions multiresolution convolutional neural networks image segmentation sharp masks probabilistic prediction multiresolution network Otsu's threshold refiner image recognition IIS system adaptive threshold refiner segmentation mask sensitive features

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Artificial Intelligence-Based Referral System for Patients With Diabetic Retinopathy

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COMPUTER 2020年第10期53卷 77-87页

作者： Gonzalez-Briceno, Gaspar Sanchez, Abraham Ortega-Cisneros, Susana Salvador Garcia-Contreras, Mario Pinedo Diaz, German Alonso Ulises Moya-Sanchez, E. CINVESTAV Dept Elect Engn Guadalajara Jalisco Mexico Govt Jalisco Guadalajara Jalisco Mexico Ctr Med Nacl Occidente Guadalajara Jalisco Mexico CINVESTAV Elect Engn Guadalajara Jalisco Mexico Univ Autonoma Guadalajara Guadalajara Jalisco Mexico

This article introduces a diabetic retinopathy screening program based on artificial intelligence that will be implemented in three hospitals in Mexico. It details the steps for the clinical integration of the system ... 详细信息

关键词： Artificial Intelligence convolutional neural Nets Diseases Eye Medical Diagnostic Computing convolutional neural network models Diabetic Retinopathy Artificial Intelligence Based Referral System Hospitals Data models Artificial Intelligence Guidelines Retina Adaptation models

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Robust face anti-spoofing using CNN with LBP and WLD

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IET IMAGE PROCESSING 2019年第11期13卷 1880-1884页

作者： Khammari, Mohammed Univ Bejaia Lab Informat Med Fac Sci Exactes Bejaia 06000 Algeria

Automatically recognising people by their biometric characteristics is a well-established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, video, or mask to spoof the real identity. This study introduces a novel approach to detect face-spoofing, by extracting the local features local binary pattern (LBP) and simplified weber local descriptor (SWLD) encoded convolutional neural network (CNN) models, WLD and LBP features are combined together to ensure the preservation of the local intensity information and the orientations of the edges. These two components are complementary to each other. Specifically, differential excitation preserves the local intensity information but omits the orientations of edges. On the contrary, LBP describes the orientations of the edges but ignore the intensity information, the proposed approach presents a very low degree of complexity which makes it suitable for real-time applications, Finally, a non-linear support vector machine (SVM) classifier with kernel function was used for determining whether the input image corresponds to a live face or not. Authors' experimental analysis on two publicly available databases REPLAY-ATTACK and CASIA face anti-spoofing showed that their approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.

关键词： image texture face recognition support vector machines feature extraction neural nets image classification biometrics (access control) WLD LBP features local intensity information nonlinear support vector machine classifier live face robust face anti-spoofing CNN biometric characteristics well-established research area biometric systems presentation attacks face-spoofing simplified weber local descriptor convolutional neural network models

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Fault Diagnosis of Rolling Element Bearing with Operationally Developed Defects Using Various convolutional neural networks

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2024年第3期24卷 1310-1323页

作者： Sahu, Devendra Dewangan, Ritesh Kumar Matharu, Surendra Pal Singh CSVTU Univ Teaching Dept Bhilai 491107 Chhattisgarh India Rungta Coll Engn & Technol Raipur 490299 Chhattisgarh India Natl Inst Technol NIT Raipur 492001 Chhattisgarh India

Rolling element bearings are critical building blocks of any rotating machine. Achieving effective and precise fault diagnosis through various neural network models plays a pivotal role in ensuring the accuracy of rolling element bearing fault diagnosis. This research paper represented comparative study of artificial neural network (ANN), 1-D CNN, multi-input 1-D CNN, and 2-D CNN in fault diagnosis of rolling element bearings. The experiment was conducted on a roller bearing test rig over 2000 hours at constant speed of 800 rpm along with radial load of 1.5 kN till the development of naturally occurring operational surface defects on the bearing components. The proposed neural network architecture utilized multiple parallel convolutional layers to effectively extract rich and complementary fault features. The model was configured by implementing the categorical crossentropy loss function and Adam optimizer. Evaluation of the neural network models was performed using a confusion matrix and t-SNE visualization to ensure accurate fault identification. Comparative analysis among the convolutional neural network techniques was conducted to show their effectiveness toward fault diagnosis. The multi-input 1-D CNN achieved 97% prediction accuracy. The results demonstrate that multi-input 1-D CNN model provides better accuracy in fault diagnosis compared to the other models.

关键词： Rolling element bearing Natural defect Fault diagnosis convolutional neural network models

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Tomato Disease Detection using Multispectral Imaging with Deep Learning models. 7

Tomato Disease Detection using Multispectral Imaging with De...

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7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (ICABCD)

作者： De Silva, Malithi Brown, Dane Rhodes Univ Comp Sci Grahamstown South Africa

ISBN: (纸本)9798350387919;9798350387902

Identifying plant diseases plays a pivotal role in managing plant health. It poses significant challenges due to the diverse manifestations of diseases. This study investigates the impact of environmental conditions and imaging factors on analysing multispectral images captured through six filters spanning various portions of the Near Infrared spectrum for tomato disease identification. Two distinct datasets were compiled for analysis. Dataset 1 comprised uniform images across all filters. Dataset 2 incorporated variations in image capture to explore the influence of environmental and imaging factors on filter performance. Classification between healthy and diseased states was conducted on both datasets utilising the popular convolutional neural network, Vision Transformer, Hybrid Vision Transformer, and Swin Transformer models. Among all the filters tested, K590 demonstrated the highest average accuracy, reaching 88.69% for Dataset 1 and 93.31% for Dataset 2. Furthermore, this filter consistently outperformed others in terms of precision and recall across both datasets. ViT-B16 emerged as the most effective model across all evaluation metrics, with an average accuracy of 89.92%. Furthermore, comparisons were drawn with prior literature, encompassing balanced and unbalanced datasets for tomato disease classification tasks. The findings indicated that environmental and imaging factors do not significantly influence disease classification using multispectral imaging.

关键词： Plant Disease Identification convolutional neural network models Multispectral Images Near Infrared Vision Transformer models Hybrid Vision Transformer models Swin Transformer models

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Harnessing image processing for precision disease diagnosis in sugar beet agriculture

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Crop Design 2024年第4期3卷

作者： Misra, Varucha Mall, A.K. ICAR-Indian Institute of Sugarcane Research Lucknow India

Sugar beet, a sugar crop, faces a persistent threat from foliar and root diseases, finally leading to substantial yield losses. Traditional methods of disease identification and severity assessment are often time-consuming, error-prone, and impractical, particularly in large production areas. In response to this challenge, researchers have recently turned to innovative solutions involving image processing and machine learning techniques for efficient disease detection in sugar beet plants. Image processing technology has emerged as a rapid and precise disease identification technology in sugar beet. By capitalizing on the ability of image processing to differentiate coloured objects, this approach facilitates the accurate determination of disease severity, enabling timely intervention measures. The urgency of developing faster and more practical methods becomes evident, highlighting the need to decrease human errors in identifying plant diseases and assessing their severity and progression. This review showcases the potential of image processing technology in revolutionizing disease detection strategies for sugar beet crops. The ability to swiftly and accurately determine disease outbreak, severity, and progression addresses a critical gap in current agricultural practices. Image processing technology holds promise as a practical and efficient solution for large-scale disease management in sugar beet cultivation, paving the way for sustainable and high-yield sugar production. © 2024 The Authors

关键词： Artificial intelligence Automation detection convolutional neural network models Hyperspectral imaging Machine learning Unmanned aerial vehicles

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