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A Framework to Identify Color Blindness Charts Using image processing and CNN 2nd

A Framework to Identify Color Blindness Charts Using Image P...

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2nd international conference on Communication, Networks and Computing, CNC 2020

作者： Dhawale, Kritika Vohra, Ankit Singh Jain, Pooja Kumar, Tapan Department of ECE Indian Institute of Information Technology Nagpur India Department of CSE Indian Institute of Information Technology Nagpur India

ISBN: (纸本)9789811688959

Color vision deficit (sometimes known as colour blindness) is a term used to describe a set of diseases that impact colour perception. People take many color blindness tests while consulting an Eye Specialist to check if they are suffering from this deficiency. This test is conducted by showing different types of color blindness charts to the patient and then asking him/her to read those charts. In this paper, we make the machine learn the color blindness charts and identify the pattern present in it, using image processing and Deep learning. Charts may contain a single digit or double-digit number or any unrecognized pattern. For this, we collected color blindness charts from the internet, preprocessed and augmented them for training purposes using CNN. Deep Learning is a method in which a computer software learns statistical patterns in data so that it can recognise or help us distinguish between the patterns in the charts. The model learns about the various attributes based on the photos and represents the data mathematically, organising it in space. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Morphology

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Efficient Detection and Categorization of Thyroid Nodules from Medical Ultrasound images

Efficient Detection and Categorization of Thyroid Nodules fr...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER international conference on

作者： Prarthana A. Deshkar Punit Fulzele Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur Datta Meghe Institute of Higher Education and Research Sawangi Wardha

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The use of ML and DL in medical imaging for early illness symptom prediction is substantial. Since 2013, DL has been one of the rising trends in general data analysis. With its multiple hidden layers that allow for a high degree of data abstraction, it is an enhancement of artificial neural networks (ANN). One promising approach for computer vision applications is the use of convolutional neural networks (CNNs). Automatic learning of raw data, particularly photographs, is a capability of deep CNNs. Both the capture and interpretation of images are crucial for the correct diagnosis or evaluation of illness. Devices are able to gather data at a rapid pace with improved resolution because to advancements in image acquisition over the last decade. Nevertheless, computer technology has only just started to improve picture interpretation. Since they are subjective and need highly trained doctors, they are often created by radiologist, physicians, and senior doctors. In medical imaging, computerised tools play a crucial role in facilitating results and improving diagnosis. Early detection and categorization of thyroid nodules by visual inspection and manual study of thyroid ultrasonography (USG) images has traditionally been a tedious process. Identifying benign from malignant thyroid nodules requires manual evaluation of thyroid USG images, which may be a time-consuming and laborious process. The medical industry has seen the emergence of many deep learning models, particularly for the categorization of thyroid nodules, thanks to the growth in processing resources and the fast improvement in technology. Clinical interventions and therapies may be more successful when these nodules are detected early. Thus, a growing number of academics are pushing for the use of computer diagnostic systems (CDS) as a means to statistically and scientifically evaluate USG pictures of thyroid nodules. Creating effective models for identifying and classifying thyroid nodules using ML and DL

关键词： Industries Deep learning Computational modeling Optimization methods Ultrasonography Manuals Convolutional neural networks Medical diagnostic imaging Thyroid Cancer

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Magnetic resonance imaging of adolescent depression based on machine vision 21

Magnetic resonance imaging of adolescent depression based on...

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2nd international Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

作者： Chen, Xingyu Zhang, Yusen Bell Honors School Nanjing University of Posts and Telecommunication Jiangsu China School of Automation Northwestern Polytechnical University Xi'an Shaanxi China

ISBN: (纸本)9781450395588

Magnetic resonance imaging (MRI) is emerging as a significant technique for understanding the neuropathological mechanisms and clinical identification of mild depression disorders (MDD). Adolescent depression is a typical class of psychiatric disorders. The number of patients keeps increasing these years. Though some studies have shown pathological changes in adult depression, the study in adolescent depression is still limited. This review discussed the application of MRI techniques in adolescent depression. It summarized the abnormity between adolescent depression and healthy controls to help clinical diagnosis and adolescent depression. Structure magnetic resonance imaging (sMRI) and function magnetic resonance imaging (fMRI) effectively analyse adolescent depression. However, diffusion tensor imaging (DTI) was indicated that could find further abnormity of adolescent depression. Future research is needed to adopt a more advanced MRI methodology in the diagnosis and treatment of MDD. Besides, With the development of artificial intelligence, computer image processing technology is more closely combined with medical science. Here we discussed the application of MRI technology in adolescent depression by combining MRI technology under computer image processing technology, in order to provide better help for the analysis of the disease. © 2021 ACM.

关键词： Magnetic resonance imaging

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A Quantitative Comparison of the Role of Parameter Selection for Regularization in GRAPPA-Based Autocalibrating Parallel MRI 3rd

A Quantitative Comparison of the Role of Parameter Selection...

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3rd international conference on computer vision and image processing (cvip)

作者： Mathew, Raji Susan Paul, Joseph Suresh Indian Inst Informat Technol & Management Kerala Med Image Comp & Signal Proc Lab Trivandrum Kerala India

ISBN: (纸本)9789813290884;9789813290877

The suitability of regularized reconstruction in autocalibrating parallel magnetic resonance imaging (MRI) is quantitatively analyzed based on the choice of the regularization parameter. In this study, L-curve and generalized cross-validation (GCV) are adopted for parameter selection. The results show that: (1) Presence of well-defined L-corner does not guarantee an artifact-free reconstruction, (2) Sharp L-corners are not always observed in GRAPPA calibration, (3) Parameter values based on L-curves always exceed those based on GCV, and (4) Use of a predetermined number of filters based on the local signal power can result in a compromise between noise and artifacts as well as better visual perception. It is concluded that appropriate use of regularized solutions facilitates minimization of noise build-up in the reconstruction process, without enhancing the effects of aliasing artifacts.

关键词： L-curve GCV g-factor GRAPPA Regularization

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computer vision Based Research on PCB Recognition Using SSD Neural Network 2

Computer Vision Based Research on PCB Recognition Using SSD ...

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2020 2nd international conference on computer, Communications and Mechatronics Engineering, CCME 2020

作者： Li, Dashuang Xu, Lei Ran, Guangzai Guo, Zhanling School of Mechanical Engineering Sichuan University Chengdu China

Printed circuit board (PCB) is an important component in the information technology industry, during PCB board assembly process, jack localization and recognition is particularly important. In view of the problem that the accuracy of target detection is not ideal, this paper applies deep learning convolutional neural network framework SSD (single shot multibox detection) to PCB element detection. By obtaining a large number of original PCB images, creating semantic label image dataset, feeding them into neural networks, and adjusting the hyperparameters, the best classification accuracy and localization accuracy are achieved. At the same time, the advantages of different algorithms are analysed after tests with other deep learning network methods. Experiments show that the SSD deep learning algorithm can realize accurate detection process of jack localization and classification, which is better than other algorithms. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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Implementation of image Resampling Algorithm Based on Compressed Sensing 2

Implementation of Image Resampling Algorithm Based on Compre...

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2nd international Seminar on computer Science and Engineering Technology, SCSET 2020

作者： Li, Denghui Wang, Yanhong Institute of Information Technology of Guet Guilin China Guangxi Key Lab of Wireless Wideband Communication AndSignal Processing Guilin China Guet Guilin China

In this paper, the sampling rate of traditional signal reconstruction should be more than 2 times the maximum frequency of the original signal in order to ensure the non-distortion reconstruction of the signal. The theoretical knowledge of compressed sensing is deeply analysed, and the image signal is reconstructed by block compression sensing method. Experiments show that the sampling rate is more higher, and the reconstruction error is more smaller, but the processing complexity is more higher;conversely, the lower the sampling rate, the lower the processing complexity, the greater the reconstruction error. © Published under licence by IOP Publishing Ltd.

关键词： Compressed sensing

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Crop-Guided Neural Network Segmentation of High-Resolution Skin Lesion images 2nd

Crop-Guided Neural Network Segmentation of High-Resolution ...

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2nd international conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2024

作者： Benčević, Marin Habijan, Marija Galić, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia Department of Telecommunications and Information Processing TELIN-GAIM Ghent University Ghent Belgium

ISBN: (纸本)9783031620577

Medical images are often exceedingly large in width and height, limiting the maximum batch size when training convolutional neural networks and requiring models with a large number of parameters. Typically, images are uniformly downsampled, leading to losing fine-detailed information. Instead of uniformly downsampling images, we introduce a two-stage end-to-end segmentation network utilizing image crops to reduce network input size. Initially, a uniformly downscaled image is first segmented with a rough segmentation module, and the rough segmentation is used as a saliency map to crop the original high-resolution image to a region of interest. This crop is then re-segmented with a fine segmentation module. Our method’s effectiveness is demonstrated in segmenting lesion boundaries in clinical images across two datasets. We establish that this technique maintains comparable segmentation quality to a baseline model while reducing the network input size. Furthermore, our approach enhances the robustness of segmentation outcomes with smaller input sizes, outperforming uniformly downscaled images and baseline models. This improvement is consistent in both in-sample and out-of-sample evaluations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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The Monitoring System of Urban Road Waterlogging Depth with Smart Lamp Pole 2

The Monitoring System of Urban Road Waterlogging Depth with ...

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2nd IEEE international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： He, Dou Yuan, Haibo Zhang, Geng Huang, Wei Southwest University School of Computer and Information Science Chongqing400715 China

ISBN: (纸本)9780738131221

Urban road waterlogging affects traffic safety and pedestrian passing seriously for urban traffic. At present, most of the existing methods to measure the actual maximum depth of urban road waterlogging are difficult to ensure its accuracy. To overcome this shortcoming, this paper establishes a calculation model to determine the maximum depth of road waterlogging. When the depth cannot be detected by the pressure sensor due to its installation position deviation or its detection zero-point in high value, the model will calculate the maximum depth based on image processing. After the change of depth can be detected by the pressure sensor, these measure values are not only used to identify the model parameters and calculate the current water level, but also used to accomplish the future road waterlogging depth pre-alarming. Finally, a monitoring system for road waterlogging depth is established to realize the pre-alarming based on the smart lamp pole. Experiment result shows that the presented model and pre-alarming system are effective and accurate. © 2021 IEEE.

关键词： Poles

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27th international Workshop on Frontiers of computer vision, IW-FCV 2021

27th International Workshop on Frontiers of Computer Vision,...

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27th international Workshop on Frontiers of computer vision, IW-FCV 2021

ISBN: (纸本)9783030816377

The proceedings contain 25 papers. The special focus in this conference is on Frontiers of computer vision. The topics include: Challenges and Applications of Face Deepfake;study on image processing of Capillaries Using Microscope: Initial Considerations;stair-Step Feature Pyramid Networks for Object Detection;the 2nd Korean Emotion Recognition Challenge: Methods and Results;crack Detection and Location Estimation Using a Convolutional Neural Network;rice Leaf Diseases Recognition Based on Deep Learning and Hyperparameters Customization;ST-GCN Based Human Action Recognition with Abstracted Three Features of Optical Flow and image Gradient;deep Visual Anomaly Detection with Negative Learning;video Analysis of Wheel Pushing Actions for Wheelchair Basketball Players;robust Tracking via Feature Enrichment and Overlap Maximization;efficient Spatial-Attention Module for Human Pose Estimation;GCN-Calculated Graph-Feature Embedding for 3D Endoscopic System Based on Active Stereo;uncalibrated Photometric Stereo Using Superquadrics with Cast Shadow;robust Training of Deep Neural Networks with Noisy Labels by Graph Label Propagation;fast Separation of Specular, Diffuse, and Global Components via Polarized Pattern Projection;saliency Prediction with Relation-Aware Global Attention Module;the Emerging Field of Graph Signal processing for Moving Object Segmentation;multi-scale Global Reasoning Unit for Semantic Segmentation;multi-modality Based Affective Video Summarization for Game Players;focusing on Discrimination Between Road Conditions and Weather in Driving Video Analysis;age Estimation from the Age Period by Using Triplet Network;development of an Algae Counting Application to Support Vegetation Surveys in Fishing Grounds;ISHIGAKI Region Extraction Using Grabcut Algorithm for Support of Kumamoto Castle Reconstruction.

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image Denoising Method Based on Hybrid Deep Dictionary Learning and Elastic Threshold 2

Image Denoising Method Based on Hybrid Deep Dictionary Learn...

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2nd international conference on Information Technology and computer Application, ITCA 2020

作者： Liu, Guanlin Guangxi University of Foreign Languages School of Information and Engineering Guangxi China

ISBN: (纸本)9780738111414

The application of machine vision in the modern industry makes intelligent manufacturing possible. At present, circuit board related fault detection, such as broken wires, missing solder joints, and other problems frequently occur, the manual recognition speed is slow and the false detection rate is high. In response to this problem, this research proposes an image noise reduction processing method that combines deep dictionary learning and elastic thresholds, which can serve computer image processing, thereby greatly improving the detection speed of printed circuit boards, and ultimately serving the processing and production of modern electronic products. © 2020 IEEE.

关键词： image denoising

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