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Morphological Gradient Analysis and Contour Feature Learning for Locating Text in Natural Scene images 6th

Morphological Gradient Analysis and Contour Feature Learning...

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6th International conference on computer vision and image processing, CVIP 2021

作者： Shekar, B.H. Raveeshwara, S. Department of Computer Science Mangalore University Karnataka Mangalore574199 India Government First Grade College Uppinangady Karnataka Uppinangady574241 India

ISBN: (纸本)9783031113482

We use text as the primary medium for providing precise information. Text could be major source of information for understanding of a scene imagery or video, once it is recognized. Although identifying and understanding textual information is fairly simple for us, it can be extremely complex task for machines. Variations such as color, orientation, scale, flow, lighting, noise, occlusion, language features and font can make this task of computer vision challenging. Detecting presence of text and precisely locating regions of text are vital for faster and precise recognition. Due to the complexity of the task, most of the popular techniques of today require an intense training phase and powerful computation infrastructure. In the proposed method, we have tried to minimize the amount of training required to achieve a decent text localization result. We have observed that, morphological gradient analysis enhances textual regions and contour feature analysis can help to eliminate non-textual components. Combination of these techniques produces promising results with small dataset, minimal training and limited computational ability. Also, the proposed detector can detect text across multiple languages and is fairly robust against the variations such as orientation and scale. the proposed method achieves an F-measure of 0.77 on MSRA-TD500 after the training with 300 images. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Single image super-resolution algorithm based on enhanced generative adversarial network 6

Single image super-resolution algorithm based on enhanced ge...

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6th International conference on image, vision and Computing, ICIVC 2021

作者： Du, Tianwen Zhang, Yifeng School of Information Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665443685

With the advancement of technology, the processing of increasing image data becomes particularly important. computer vision can process the hidden information behind these data. image super-resolution aims to use a low-resolution image to generate a corresponding high-resolution image through the convolutional neural network, improving the quality of the image and the display effect. this paper proposes an Enhanced Generative Adversarial Network (EGAN), designing a new loss function, so that the generated image has more texture details than the previous method. On three benchmark datasets, Set5, Set14 and BSD100, the proposed method has a great improvement on the peak signal-to-noise ratio and structural similarity of the generated images. © 2021 IEEE.

关键词： Generative adversarial networks

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A Lightweight 3D Segmentation Network for Abdominal Liver in CT image 6

A Lightweight 3D Segmentation Network for Abdominal Liver in...

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6th International conference on Advances in image processing, ICAIP 2022

作者： Zhao, Junlin Zhang, Tianlin Gao, Ling Wan, Wenbo Wang, Jian School of Information Science and Engineering Shandong Normal University Jinan250358 China

ISBN: (纸本)9781450397155

Abnormal liver function is linked to a variety of disorders. Precise and quick automatic liver segmentation can help clinicians make better diagnosis and treatment decisions. With the development of computer vision and deep learning approaches, there are more solutions for biomedical image segmentation tasks. In recent years, the U-Net architecture is by far the most widely used backbone architecture for biomedical image segmentation. Deep convolutional neural networks-based semantic segmentation has achieved sufficient accuracy. However, the scale of high-precision networks is growing, requiring an increasing amount of storage and computational resources. Furthermore, the deep neural network's operating time is lengthy, making it difficult to satisfy practical needs. As a result, the lightweight convolutional neural network design is used to the semantic segmentation task. As a consequence, in this article, a lightweight convolutional neural network is proposed to solve the aforementioned problems in the task of biomedical image segmentation. 3D U-Net is used as the backbone architecture and a modification of the Ghost module from GhostNet is introduced to boost up the effectiveness and the learning efficiency. the experimental results demonstrate that the proposed network improved the segmentation performance with fewer network parameters and requiring less floating-point computation. © 2022 ACM.

关键词： Semantic Segmentation

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HSADML: Hyper-Sphere Angular Deep Metric Based Learning for Brain Tumor Classification 12th

HSADML: Hyper-Sphere Angular Deep Metric Based Learning for ...

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12th indian conference on computer vision, graphics, and image processing, ICVGIP 2021

作者： Verma, Aman Singh, Vibhav Prakash Department of Electronics and Communications National Institute of Technology Raipur Raipur India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811941351

Brain Tumors are an abnormal mass of clustered cells penetrating regions of the brain. their timely identification and classification help doctors to provide appropriate treatment. However, Classification of Brain Tumors is quite intricate because of high-intra-class similarity and low-inter- class variability. Due to morphological similarity amongst various MRI-Slices of different classes the challenge deepens more. this all leads to hampering the generalizability of classification models. To this end, this paper proposes HSADML, a novel framework which enables Deep Metric Learning (DML) using SphereFace Loss. SphereFace loss embeds the features into a hyperspheric-manifold and then imposes margin on the embedding to enhance differentiability between the classes. With the utilization of SphereFace loss-based deep metric learning it is ensured that samples from class are clustered together while the different ones are pushed apart. Results reflect the prominence of the approach, the proposed framework achieved state-of-the-art 98.69% validation accuracy using k-NN (k = 1) and this is significantly higher than normal Soft Max Loss training which though obtains 98.47% validation accuracy but that too with limited inter-class reparability and intra-class closeness. Experimental analysis is done over various classifiers and loss function settings suggest potential in the approach. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Brain

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An Efficient Method for Addressing COVID-19 Proximity Related Issues in Autonomous Shuttles Public Transportation 1

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18th International conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Tsiktsiris, Dimitris Lalas, Antonios Dasygenis, Minas Votis, Konstantinos Tzovaras, Dimitrios Ctr Res & Technol Hellas Inst Informat Technol 6th Km Xarilaou Thermi Thessaloniki 57001 Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece

ISBN: (数字)9783031083334

ISBN: (纸本)9783031083334;9783031083327

the COVID-19 pandemic has created significant restrictions to passenger mobility through public transportation. Several proximity rules have been applied to ensure sufficient distance between passengers and mitigate contamination. In conventional transportation, abiding by the rules can be ensured by the driver of the vehicle. However, this is not obvious in Autonomous Vehicles (AVs) public transportation systems, since there is no driver to monitor these special circumstances. Since, AVs constitute an emerging mobility infrastructure, it is obvious that creating a system that can provide a sense of safety to the passenger, when the driver is absent, is a challenging task. Several studies employ computer vision and deep learning techniques to increase safety in unsupervised environments. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase the COVID-19 safety rules adherence of the passengers inside an autonomous shuttle. the proposed real-time service, can detect deviations from proximity rules and notify the authorized personnel, while it is possible to be further extended in other application domains, where automated proximity assessment is critical.

关键词： Autonomous vehicles Public transportation Neural networks image processing

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Enhancing CNN Model by Incorporating Upscaling and Downscaling Techniques for image Classification

Enhancing CNN Model by Incorporating Upscaling and Downscali...

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International conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Shamim Ahmed Sadikur Rahman Saiful Azad K.M. Shadman Wadith Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

image classification is a fundamental task in computer vision with numerous applications. Even though, the rapid development of Convolutional Neural Networks (CNNs) has revolutionized the field of image classification, there is a constant pursuit for further improvement. Existing CNN models typically rely solely on downscaling techniques for pre-processing, limiting their potential for extracting intricate image details and effectively handling varying image resolutions. In this paper, we propose an approach that incorporates both upscaling and down-scaling techniques to enhance the performance of CNN models for image classification. Our method leverages the benefits of both upscaling and downscaling operations to capture fine-grained details and extract high-level features, respectively. through extensive experiments on benchmark datasets, namely CIFAR-10 and CIFAR-100 dataset, we demonstrate the superiority of our proposed approach over existing methods in terms of accuracy and robustness. For instance, our proposed model, named Super-Res VGG has received 85.57% testing accuracy whereas VGG16, GoogLeNet (Inception), and ResNet50 have received 84.40%, 73.93%, and 65.29% accuracy on CIFAR-10 dataset, respectively.

关键词： image resolution Redundancy Benchmark testing Feature extraction Data models Robustness Convolutional neural networks

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Microaneurysm Detection for Early DR Diagnosis Using Advanced image processing and a Lightweight Feature Pyramid Network

Microaneurysm Detection for Early DR Diagnosis Using Advance...

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International conference on Advancements in Computational Sciences (ICACS)

作者： Muhammad Zeeshan Tahir Muhammad Nasir Tufail Muhammad Sanyuan Zhang Muhammad Ahsan Qureshi Dept. of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China School of Biomedical Engineering University of Science and Technology of China Hefei Anhui China Department of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Diabetic Retinopathy (DR) is a diabetes-related condition that can lead to vision loss and even permanent blindness. the growing number of diabetic patients combined with a shortage of ophthalmologists underscores the need for automated screening tools to enable early detection. Microaneurysms (MAs), the earliest signs of DR, yet identifying them in fundus images is challenging because of their tint size and subtle features. Furthermore, low contrast, noise, and lighting variations in fundus images, including glare and shadows, add complexity to the detection process. To address these challenges, we applied image enhancement techniques such as green channel extraction, gamma correction, and median filtering to improve image quality. Additionally, to boost the performance of the MA detection model, we integrated a lightweight Feature Pyramid Network (FPN) with a pre-trained ResNet34 backbone, allowing for multiscale feature capture. Our method was evaluated on the E-ophtha dataset, achieving a sensitivity of 0.601, precision of 0.697 and F1 score of 0.651. the experimental findings indicate that the proposed method surpasses current approaches.

关键词： image quality Diabetic retinopathy Sensitivity Filtering Noise Lighting Blindness Feature extraction Complexity theory image enhancement

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Analysis and visual exploration of prediction algorithms for public bicycle sharing systems 15

Analysis and visual exploration of prediction algorithms for...

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International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021

作者： Ordoñez, Alexandra Cortez Vázquez, Pere-Pau ViRVIG Universitat Politècnica de Catalunya Carrer Llorens I Artigas 4-6 Barcelona08028 Spain

ISBN: (纸本)9789898704320

Public bicycle sharing systems have become an increasingly popular means of transportation in many cities around the world. However, the information shown in mobile apps or websites is commonly limited to the system's current status and is of little use for both citizens and responsible planning entities. the vast amount of data produced by these managing systems makes it feasible to elaborate and present predictive models that may help its users in the decision-making process. For example, if a user finds a station empty, the application could provide an estimation of when a new bicycle would be available. In this paper, we explore the suitability of several prediction algorithms applied to this case of bicycle availability, and we present a web-based tool to visually explore their prediction errors under different time frames. Even though a quick quantitative analysis may initially suggest that Random Forest yields a lower error, our visual exploration interface allows us to perform a more thorough analysis and detect subtle but relevant differences between algorithms depending on variables such as the station's behavior, hourly intervals, days, or types of days (weekdays and weekends). this case illustrates the potential of visual representation together with quantitative metrics to compare prediction algorithms with a higher level of detail, which can, in turn, assist application designers and decision-makers to dynamically adjust the best model for their specific scenarios. © 2021 International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Websites

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Efficient image Super-Resolution using Large Kernel Convolution Attention Mechanism

Efficient Image Super-Resolution using Large Kernel Convolut...

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International conference on Communications, Information System and computer Engineering (CISCE)

作者： Yan Wang School of Computer Science and Technology Ocean University of China Qingdao China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Super-resolution techniques serve as a cornerstone in the realm of image processing, offering a pathway to elevate the resolution and fidelity of images, thus opening up a myriad of possibilities across various domains within computer vision. In our paper, we introduce an innovative approach aimed at striking a delicate balance between parameter efficiency and performance in the realm of SR tasks. Our method capitalizes on the potent capabilities of the Large Convolution Kernel Attention Mechanism, a novel framework that enables us to achieve remarkable results by harnessing the power of attention mechanisms alongside large convolutional kernels. At the heart of our proposed methodology lies a profound innovation: the seamless integration of attention mechanisms into the SR framework, with a specialized focus on leveraging large convolutional kernels. this strategic fusion allows our model to operate with unparalleled efficiency, dynamically spotlighting crucial features within the image while concurrently dampening the influence of extraneous or less relevant information. this selective attention mechanism proves to be instrumental in driving substantial enhancements in the quality of reconstructed images, effectively refining the fine details and overall perceptual fidelity. the method proposed in this study achieves a balance between the amount of parameters and calculations and super-resolution performance.

关键词： Deep learning Technological innovation Attention mechanisms Convolution Computational modeling Superresolution Computational efficiency

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Performance, Trust, or both? COVID-19 Diagnosis and Prognosis using Deep Ensemble Transfer Learning on X-ray imagesg± 13

Performance, Trust, or both? COVID-19 Diagnosis and Prognosi...

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13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Tiwari, Abhishek Singh, Rajeev Kumar Department of Computer Science and Engineering Shiv Nadar University Gautam Buddha Nagar UP Delhi India

ISBN: (纸本)9781450398237

the COVID-19 pandemic still affects most parts of the world today. Despite a lot of research on diagnosis, prognosis, and treatment, a big challenge today is the limited number of expert radiologists who provide diagnosis and prognosis on X-Ray images. thus, to make the diagnosis of COVID-19 accessible and quicker, several researchers have proposed deep-learning-based Artificial Intelligence (AI) models. While most of these proposed machine and deep learning models work in theory, they may not find acceptance among the medical community for clinical use due to weak statistical validation. For this article, radiologists' views were considered to understand the correlation between the theoretical findings and real-life observations. the article explores Convolutional Neural Network (CNN) classification models to build a four-class viz. "COVID-19", "Lung Opacity", "Pneumonia", and "Normal"classifiers, which also provide the uncertainty measure associated with each class. the authors also employ various pre-processing techniques to enhance the X-Ray images for specific features. To address the issues of over-fitting while training, as well as to address the class imbalance problem in our dataset, we use Monte Carlo dropout and Focal Loss respectively. Finally, we provide a comparative analysis of the following classification models - ResNet-18, VGG-19, ResNet-152, MobileNet-V2, Inception-V3, and EfficientNet-V2, where we match the state-of-the-art results on the Open Benchmark Chest X-ray datasets, with a sensitivity of 0.9954, specificity of 0.9886, the precision of 0.9880, F1-score of 0.9851, accuracy of 0.9816, and receiver operating characteristic (ROC) of the area under the curve (AUC) of 0.9781 (ROC-AUC score). © 2022 ACM.

关键词： Coronavirus

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