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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13657 条记录，以下是491-500 订阅

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Comparison, classification, and analysis of retinal blood vessels using image processing and data mining algorithms: A review article 4

Comparison, classification, and analysis of retinal blood ve...

引用

4th International conference on Electrical, Control and Instrumentation Engineering, ICECIE 2022

作者： Dabiri, AbdolRasool Khorram Askari, Milad Ieee Ias Vienna Austria Ieee Ias KualaLumpur Malaysia

ISBN: (纸本)9781665480765

The retinal arteries separation and classification algorithms are one of the fundamental components of eye diseases diagnostic systems using image processing. In other words, it can be said that morphological changes in the retinal blood vessels and accurately analyzing them can contribute to diagnosing diseases including diabetes mellitus (DM), hypertension, or glaucoma. In recent years, numerous studies have been carried out on blood vessels classification and their processing algorithms. In the present study, the aim was to identify and apply the most recent and most practical algorithms in the ophthalmology industry and examine their performance. In addition, it was attempted to discuss important parameters in the diagnosis and operation of the classification algorithm. © 2022 IEEE.

关键词： Gabor filter image processing Neural network Retinal blood vessels Vessel classification

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Segmentation of skin layers in ultrasound images using a crowdsourcing and deep learning-based system 20

Segmentation of skin layers in ultrasound images using a cro...

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20th ACS/IEEE International conference on Computer systems and Applications (AICCSA)

作者： Moran, Maira Vaz Oliveira, Larissa Aparecida Faria, Marcelo Bastos, Luciana Giraldi, Gilson Canella, Clarissa Conci, Aura Univ Fed Fluminense Niteroi Brazil Univ Estado Rio de Janeiro Rio De Janeiro Brazil Lab Nacl Comp Cient Petropolis Brazil

ISBN: (纸本)9798350319439

Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomical layers, such as tissues, which can constitute a very helpful supporting tool in several procedures, e.g., surgeries. However, the visual identification of tissues in this type of image is still a challenge for some professionals. In this work, we evaluate deep learning (DL) segmentation algorithms for the tissue segmentation task in US. Moreover, we briefly assess whether their performance can be improved by including a crowdsourcing step. In order to perform the segmentation task, different segmentation models are trained, as posteriorly, the crowdsourcing step is included. The proposed approach is composed of the following steps: 1- automatic segmentation using a deep learning algorithm;2 - crowd evaluation and correction of the results. In order to perform step 1, different segmentation models are trained. The second step includes a visual interface where users can: a) validate the quality of the automatic segmentation;or b) correct the segmentation whether the DL result is inconsistent. All users are scored, denoting the quality of their annotations, considering the manual annotations provided by an expert for a small group of images. In order to evaluate the method and compare it to a DL algorithms alone, a total of 100 US images were used. Our experiments show that the inclusion of crowdsourcing significantly improved the performance of the tissue segmentation task compared to using the DL models alone. The performance of our method demonstrated the feasibility of applying this type of solution for the considered problem of segmenting tissues in US facial images. Moreover, the results suggest that this tool can be employed as an auxiliary tool in oral procedures.

关键词： image processing Ultrasound Segmentation Collective Intelligence Crowdsourcing

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Real-time image processing in A Multi-UAV System for Structural Surveillance Through IoT Platform

Real-time Image Processing in A Multi-UAV System for Structu...

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conference on Real-Time image processing and Deep Learning

作者： Falcon, Jose Mehrubeoglu, Mehrube Rangel, Pablo Texas A&M Univ Corpus Christi Elect Engn Program Dept Engn Corpus Christi TX 78412 USA

ISBN: (纸本)9781510650817;9781510650800

The use of location and instruction markers for multi-path planning enhancement in any set of unmanned aerial systems' tasks is crucial to the coordination and effectiveness of the individual unmanned aerial vehicles (UAVs). This research implements OpenCV algorithms that allow multiple UAVs to use ArUco markers to receive data related to location and instruction for the purposes of multi-path planning. OpenCV algorithms are utilized to develop vision-based solutions that will enhance the real-time capabilities of the UAVs. The final goal for the multi-drone system entails inspecting and surveying objects for structural damage and applying the developed image processing algorithms to collected images to determine the significance of damage. This project utilizes OpenCV and Python libraries for multi-drone pathway planning by collecting, transmitting, and displaying real-world industrially valuable data over the network infrastructure as an application of Internet of Things (IoT).

关键词： unmanned aerial vehicle UAV OpenCV ArUco marker Internet of Things real-time image processing

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RWU3D: Real World ToF and Stereo Dataset with High Quality Ground Truth 12

RWU3D: Real World ToF and Stereo Dataset with High Quality G...

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12th International conference on image processing Theory, Tools and Applications, IPTA 2023

作者： Agrawal, Ankita Muller, Tobias Schmahling, Tobias Elser, Stefan Eberhardt, Jorg University of Applied Sciences Institute for Artificial Intelligence Institute for Photonic Systems Hochschule Ravensburg-Weingarten Weingarten Germany

ISBN: (纸本)9798350325416

This paper introduces a new dataset RWU3D that consists of ToF depth images and the corresponding amplitude images as well as high-resolution Stereo images and the respective ground truth data. The corresponding disparity and depth images are also available. The dataset is recorded for the real world scenarios that can be used to obtain a more accurate estimation of a particular scene through the fusion of ToF and Stereo characteristics. We built a system comprising of a state of the art ToF sensor, high-resolution RGB cameras, and a laser scanner for obtaining ground truth. The data from the different sensors is calibrated to one coordinate system, which is a challenging task in itself. The data is recorded for 11 different real world scenes with 770 total frames in total obtained with our system. The dataset would present new challenges for the experimentation with the ToF data and contribute towards the development of algorithms designed to analyze and improve the ToF data. © 2023 IEEE.

关键词： Stereo image processing

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Maritime low-light image enhancement study based on improved ResUnet 23

Maritime low-light image enhancement study based on improved...

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6th International conference on Signal processing and Machine Learning, SPML 2023

作者： Wang, Jingyang Pan, Mingyang Zhang, Ruolan Liu, Zongying Li, Shaoxi Navigation College Dalian Maritime University China

ISBN: (纸本)9798400707575

Recent years, both the domestic and international frontiers made headway in the realm of intelligent navigation for ships, so vision-based ship monitoring systems and smart ships had a great development prospect. However, in the night-time environment at sea, the images usually have low contrast, low visibility and generous noise splash. Deep learning algorithms are affected by these problems, which not only limit the detecting and recognizing capability but also bring the unstable elements in the aspect of safety. To solve these problems, this study constructs paired Maritime-Low-Light datasets and build the MLL-ResUnet (Maritime low-light-ResUnet) model based on the ResUnet model to enhance nighttime images at sea. The CurveNLU module is designed, which adjusts the image brightness curve to make an adaptive adjustment of the image brightness for achieving the image enhancement tasks. Lastly, to consider the image output effect and model light-weighting, we introduce the ORSnet (Original Resolution Subnetwork) module and the GhostConv module. In the experimental part, the MML dataset is applied in the several other state-of-the-art image enhancement algorithms and the proposed algorithm. Based on the compared results, we find that the model proposed achieves impressive results. Specifically, it scores 26.865 and 0.84 in the PSNR and SSMI quality evaluation indexes, surpassing the performance of other algorithms. There is strong evidence to support that the proposed model not only ensures the authenticity of the output image, but also has a certain guarantee for the speed of calculation. Overall, the MLL-ResUnet model meets the requirements of the ship monitoring system and enables real-time monitoring of ship movement at night at sea. © 2023 ACM.

关键词： image enhancement

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Military vehicles marking detection algorithm on a digital image 4

Military vehicles marking detection algorithm on a digital i...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Skyruta, Vladyslav Yurchuk, Iryna Software Systems and Technologies department Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9798350395532

Constant complicating of wars due to considerable development of military weapon and moving to new generation of warfare led to a significant change in means of it. More and more armed forces involve AI into solving a wide range of tasks, since modern war produces a lot of data which need to be processed and analyzed. In this work, an effective military vehicles marking detection algorithm was developed in order to make it possible to determine the ownership of military equipment based on its marking. It uses pure image processing algorithms instead of involving neural networks, like in R-CNN model or its derivatives. As a result, the algorithm does not require a huge dataset of marking templates and time for network training and can be applied immediately. The algorithm is not bound to a specific marking pattern and can be applied to any. Also it is designed for detecting military marking without reference to objects. As a result, the algorithm can be easily integrated with other image processing systems, e.g. military vehicles detection. © 2023 IEEE.

关键词： Template matching

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Deep learning in the design and application of thoracic medical image-Assisted diagnosis algorithm 7

Deep learning in the design and application of thoracic medi...

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7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xiao, Yineng The Global Intellectual Property Institute Nanjing University Suzhou215163 China Advanced Institute of Information Technology Peking University Hangzhou311200 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

In recent years, with the development of deep learning (DL) technology and the continuous improvement of algorithms, DL-Assisted diagnosis systems based on medical images have rapidly developed. Compared with traditional image processing methods, DL trains models by combining a large amount of relevant data (e.g. clinical data, imaging data, etc.) and then uses the models to predict disease-related information. Compared with traditional medical image processing algorithms, DL has better performance in recognition, segmentation and classification of medical images. In this paper, an AI-Aided diagnosis algorithm is developed for lung cancer, a malignant tumour disease, based on clinical chest CT data and imaging data. The algorithm uses chest CT images as the object of study, and classifies and evaluates patients based on various factors such as their age, gender, tumour volume and location, as well as their knowledge of the disease. A pre-Trained model was first used to establish an algorithm for lung cancer tumour segmentation and recognition. Convolutional NNs are then applied to learn solutions to the feature extraction and classification problems. Finally, the results obtained are used as model output and the system performance is evaluated to complete the diagnostic process for lung cancer as a class of malignant tumour disease. © 2023 SPIE. All rights reserved.

关键词： Diseases

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Research on Ink Style image Generation Based on Deep Learning

Research on Ink Style Image Generation Based on Deep Learnin...

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2023 International conference on Electronic Information Engineering and Data processing, EIEDP 2023

作者： Zheng, Tianyi Huang, Zhangqin Zhang, Runmin Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9781510666160

Most of the current existing style transfer methods are based on photographs or Western paintings. Due to the inherent differences between Chinese and Western paintings, direct application of existing algorithms cannot generate satisfactory style transfer results for Chinese ink paintings. Accordingly, I propose a new feedforward style transfer method [1], called inkStyle, which uses a draw Network to transfer global style patterns at low resolution and a higher resolution details Networks to modify local style patterns in a pyramidal manner based on the multi-level Laplacian filtering output of the content image. The experimental results show that the method in the paper performs better and produces better visual results. © 2023 SPIE.

关键词： Deep learning

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ResNet-50 for Flower image Classification: A Comparative Study of Segmentation and Non-Segmentation Approaches 19

ResNet-50 for Flower Image Classification: A Comparative Stu...

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19th International conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Manga, Abdul Rachman Nirmala Azis, Huzain Fattah, Farniwati Salim, Yulita Darwis, Herdianti Departement of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Flower image classification poses a challenge in digital image processing, requiring effective methods for feature extraction and classification. The aim of this research is to improve the accuracy of flower image classification by employing ResNet-50 for feature extraction, with and without segmentation, and evaluating the effectiveness of various classification algorithms. The dataset consists of images of Calendula and Coreopsis flowers, totaling 2,025 samples. Four segmentation techniques—Canny, Thresholding, Otsu, and Mean Shift—along with a non-segmentation approach are applied. The features extracted using ResNet-50 are classified with Naive Bayes, Support Vector Machine (SVM), Decision Tree, and K-Neighbors Classifier (KNN). Performance evaluation is conducted using accuracy, precision, recall, and F1-score with 5-fold cross-validation. The results show that SVM delivers the best performance in most scenarios. The highest accuracy in segmentation scenarios was achieved with the Mean Shift technique at 0.91, while the non-segmentation approach yielded the highest accuracy of 0.95. The non-segmentation approach proves more effective, indicating segmentation is not always required for high classification accuracy with ResNet-50. This study shows that ResNet-50, especially without segmentation, can significantly improve flower image classification compared to traditional methods, opening opportunities for more efficient systems. © 2025 IEEE.

关键词： Support vector machines

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Fairness-aware Dimensionality Reduction 31

Fairness-aware Dimensionality Reduction

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31st European Signal processing conference, EUSIPCO 2023

作者： Kose, O. Deniz Shen, Yanning Electrical Engineering and Computer Science University of California Irvine Irvine United States

ISBN: (纸本)9789464593600

In this era of digitalization, the massive increase in available data leads to great potential for advancing various domains. However, the available data, such as images, videos, and speech signals, generally lie in high-dimensional space, which calls for efficient dimensionality reduction techniques to process them efficiently. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, the effect of dimensionality reduction on fairness is an under-explored research area. Motivated by this, this paper puts forth a fairness-aware dimensionality reduction framework that is capable of properly compressing the data while mitigating bias. Specifically, our design targets at reducing the correlation between the compressed data and sensitive attributes, while projecting the data into a new coordinate system where most of its variation can be described. Experimental results on the CelebA dataset demonstrate that the proposed dimensionality reduction framework can improve group fairness measures for image classification while providing comparable utility to the conventional techniques. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： image classification

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