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

共 15616 条记录，以下是411-420 订阅

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Analysis of modern learning-based multimodal fusion algorithms for neuromorphic vision sensors

Analysis of modern learning-based multimodal fusion algorith...

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SPIE conference on Future Sensing Technologies

作者： AlRemeithi, Hamad Swamidoss, Issac Niwas Al Mansoori, Abdulla AlMarzooqi, Abdulrahman Sayadi, Slim Bouamer, Tarek Tawazun Technol & Innovat TTI POB 127788 Abu Dhabi U Arab Emirates

ISBN: (纸本)9781510657229

image enhancement is an ongoing research problem that the community is addressing through the development of fusion algorithms. Such techniques typically involve the reconstruction of RGB images by removing environmental artifacts and enhancing desired features. Infrared imagery is also widely used to improve situational awareness in low visibility scenarios. Recently, learning-based approaches are used for fusion purposes to extract meaningful representations from images and capture latent features that could otherwise be inaccessible using conventional image processing algorithms. The inadequacies of RGB images in these algorithms' pipelines are still obvious, despite the fact that the viability of RGB-Infrared image fusion has been thoroughly demonstrated in the literature. For example, RGB images often have artefacts like sudden changes in exposure or motion blur when the illumination changes or sudden changes in the scene. A novel imaging sensor operating in the visible light spectrum has been developed to address these issues. In this paper, we explore the cutting-edge paradigm of Neuromorphic vision Sensors (NVS), a class of asynchronous analog imaging platforms that operate based on the change of pixel luminosity within a scene. When compared to frame-based counterparts, NVS enhances scene interpretation, processing time, reaction time, and power consumption. Deep-learning reconstruction networks are evaluated in this study to determine the applicability of existing state-of-the-art multi-modal image fusion techniques with the addition of NVS data rather than RGB data. As a benchmark, metrics such as signal-to-noise ratio (SNR) and pixel wise error are used.

关键词： Autonomous robotics Off-road navigation Event camera Neuromorphic sensing Robotic vision Deep learning systems

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image processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International conference on Information Management and Technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. image processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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image Understanding Through visual Question Answering: A Review from Past Research 23th

Image Understanding Through Visual Question Answering: A Rev...

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23rd International conference on Intelligent systems Design and Applications, ISDA 2023

作者： Yanda, Nagamani Tagore Babu, J. Aswin Kumar, K. Taraka Rama Rao, M. Ranjith Varma, K.V. Rahul Babu, N. GMR Institute of Technology Rajam532127 India

ISBN: (纸本)9783031648465

visual Question Answering (VQA) lies at the crossroads of computer vision, natural language processing, and deep learning, captivating researchers across various AI domains. This dynamic field involves processing an image alongside a corresponding textual question, generating, or selecting an answer from provided options. The past five years have witnessed substantial advancements in VQA and visual reasoning, fueled by deep learning and extensive annotated datasets. This study presents a comprehensive literature review, delving into the current state-of-the-art from four perspectives: problem definition, existing datasets, literature review, and evaluation metrics. Through a critical analysis, we address dataset limitations and scrutinize contemporary algorithms. Here we use multimodal Fusion, which achieves the state of the art compared to existing methodologies. Moreover, we explore potential future research directions to inspire innovative solutions and applications in this evolving domain, aiming to propel VQA into new realms of exploration and practical utility. This project will allow users to input an image and image-related text so that it will aid the question-answering system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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A Comparative Analysis Between the Performance of the Extracted Features of JPEG and PNG on a Raspberry Pi Iris Recognition System 38

A Comparative Analysis Between the Performance of the Extrac...

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38th IEEE Region 10 conference, TENCON 2023

作者： Martinez, Gio Arturo S. Moralde, Reese Elijah B. Linsangan, Noel B. Ang, Rayellee Myrtle Laire School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (纸本)9798350302196

The image file format used in iris recognition systems has been proven to affect the performance of iris authentication. PNG and JPEG, the image file formats that are among the most common, will be compared in this study by converting a single iris image to the two image file formats, which will then be evaluated using total and average processing time FRR and FAR. The two file formats differ in their used image compression algorithms. JPEG achieves a lossy image compression, which results in irreversible data loss after every compression, using DCT (Discrete Cosine Transform). On the other hand, PNG achieves lossless image compression using the DEFLATE algorithm. Both algorithms' motive is to compress data. The latter, however, executes this without losing data. The image file formats are compared on a Raspberry Pi 4B that runs the iris recognition system utilizing Daugman's algorithm. After conducting 80 trials of iris recognition in the system, the JPEG database resulted in a total and average processing time of 70.8301s and 1.7708s respectively, and a FRR of 0.05 and a FAR of 0.05. Meanwhile, the PNG database yielded a total and average processing time of 72.2576s and 1.8064s respectively, and a FRR of 0.10 and a FAR of 0.05. This comparative study identified that the extracted iris features from JPEG produced better recognition performance results in the implemented iris recognition system over PNG. © 2023 IEEE.

关键词： image compression

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FPGA-Based High-Speed Remote Sensing Satellite image Data Transmission System 10th

FPGA-Based High-Speed Remote Sensing Satellite Image Data Tr...

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10th International conference on Signal and Information processing, Network and Computers, ICSINC 2022

作者： He, Yifeng Sun, Peng Liu, Qian Zhang, Hao Zhao, Dayang Xi’an Institute of Space Radio Technology Xi’an710100 China

ISBN: (纸本)9789811999673

At present, with the increasing number of remote sensing satellite systems established in China, a large amount of remote sensing satellite image data has been obtained. Based on FPGA, this paper studies the transmission of high-speed remote sensing satellite image data, studies the image characteristics, image preprocessing methods and image detection principles of remote sensing satellite images, and analyzes various methods of image preprocessing. For different targets and different image backgrounds, the specific processing algorithms are also different. Only by flexibly using the basic theory of image processing can a good image processing effect be achieved. Among them, the fast connected region labeling algorithm designed and implemented can realize the merging of equivalent pairs while scanning. The results show that the system can reliably implement the algorithm in this paper, complete the processing of image data, and meet the requirements of the project. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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Feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection 4th

Feature Extraction and Dimensionality Reduction to Evaluate ...

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4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. This paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. The hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. The results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. Therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： Computer-Aided Diagnosis Diabetic Retinopathy Machine learning Deep learning Ensemble learning image processing

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Enhancing image Security Using Legacy-Based Encryption With Chaotic Tent Map and Memristor 26

Enhancing Image Security Using Legacy-Based Encryption With ...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Elnoamy, Omar Gabr, Mohamed Korayem, Youssef Alexan, Wassim El-Aasser, Minar The German University in Cairo Faculty of MET Cairo Egypt The German University in Cairo Faculty of IET Cairo Egypt

ISBN: (纸本)9798350304985

The development and utilization of effective image encryption techniques is seeing an unprecedented need with the advancements in multimedia production and exchange over unsecured networks. In a simultaneous manner, chaotic functions are increasingly being used by scholars to effectuate chaos in a deterministic manner, to develop pseudo-random bit-streams for the construction of encryption keys and substitution boxes. This work proposes the utilization of a 3-stage image encryption scheme. The Tent chaotic map is used in the first stage, followed by a legacy cellular automata S-box in the second stage, and finally a Memristor chaotic system in the third stage. Performance evaluation shows superior entropy performance and comparable values to the literature in terms of other metrics. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Memristors

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A Certain Investigation on Undersea Water image Object Detection and Classification Using Artificial Intelligence algorithms 3rd

A Certain Investigation on Undersea Water Image Object Detec...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

作者： Sandhya, Kaipa Arumugam, Jayachandran Presidency University Bangalore India

ISBN: (纸本)9789819708918

The increase in the usage of marine resources in recent years has drawn attention toward the underwater image processing research field;however, underwater images face some of the challenges like severe absorption and scattering of water, low contrast, monotonous color, etc., and make a complicated process. In this paper, we made a comprehensive study related to underwater image processing in recent years. The study includes both machine learning and deep learning approaches used for the detection of underwater objects. This study also presents the current scenarios along with future trends and provides better insights into the research direction in underwater image processing. Recently, artificial intelligence (AI) techniques are widely used for the detection and classification process of undersea water object detection and they demonstrated good outcomes. This study categorizes AI into machine learning and deep learning techniques. This study analyzed 31 papers related to underwater image object detection from various publishers like IEEE, Springer, Elsevier, Taylor and Francis, etc. More survey analysis is based on the study of the deep learning model. Hence, this study presents the advantages and the disadvantages of each model with open research challenges of undersea water object detection and classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water absorption

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Synergistic Integration of MRI Prior for Enhanced Photoacoustic Tomography: Advancing Biomedical Imaging Quality 12th

Synergistic Integration of MRI Prior for Enhanced Photoacous...

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12th International conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Krishna Satya Varma, M. Yamini Priyanka, S. Department of IT SRKR Engineering College Chinaamiram Andhra Pradesh Bhimavaram India

ISBN: (纸本)9789819601424

In this research endeavor, we introduce a pioneering strategy to enhance the fidelity of photoacoustic tomography (PAT) images, addressing prevalent artifacts and distortions stemming from acoustic and optical properties of biological tissues. Our approach capitalizes on the synergy between PAT and magnetic resonance imaging (MRI), exploiting MRI's wealth of information on tissue anatomy and composition to refine PAT reconstructions. Through a meticulously designed framework, we integrate advanced image processing methodologies, spanning registration, fusion, and deep learning-based algorithms, to seamlessly merge MRI priors into the PAT reconstruction pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image fusion

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Localization of Dengue virus Likelihood Using image processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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