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

共 13626 条记录，以下是281-290 订阅

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image Colorization Enhancement Techniques using Deep Convolutional GANs 15

Image Colorization Enhancement Techniques using Deep Convolu...

引用

15th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Sharma, Piyush Ahmad, Muaz Anees Gupta, Kapil Baniya, Pashupati Sharda University Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350324433

image colorization, which refers to adding color to a grayscale or black-and-white image, is a popular subject in image processing. Usually, it's done by hand using colored pencils, crayons, and paints. It may also be done digitally using a variety of software programs like Adobe Photoshop. The field of image colorization has been greatly impacted by the introduction of AI and deep learning algorithms in image processing. This has reduced the time and effort needed to perform the task manually by enabling automatic colorization of grayscale images with a higher degree of accuracy. Numerous neural networks and deep learning techniques have been applied to this subject;the most widely used is the CNN, which was created especially for processing and identifying images. Moreover, a different kind of deep learning model called a GAN is becoming more and more well-liked for the aforementioned purpose, improving the effectiveness and performance of the standalone neural network models to produce an even better result. The proposed study compares the effectiveness of the CNN model in image processing and colorization with the Deep Convolutional GAN model using a variety of assessment measures such as PSNR, SSIM, SCC, and RASE. It also shows how the latter approach appears to be more appropriate than standalone CNN for the mentioned task. Thus, DCGAN turns out to be a better solution for image colorization because it reduces the need for human labor, enhances colorization results, and retains the structural integrity of the image. © 2023 IEEE.

关键词： Generative adversarial networks

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Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Do We Still Need Non-Maximum Suppression? Accurate Confidenc...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Gilg, Johannes Teepe, Torben Herzog, Fabian Wolters, Philipp Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9798350318920;9798350318937

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the-essentially hand-crafted-non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration;this makes it parallelizable with no hyperparameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The code for all our experiments is publicly available(1).

关键词： accountable algorithms algorithms ethical computer vision Explainable fair image recognition and understanding privacy-preserving

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Deep Learning-based Early Lung Cancer Detection using Multi-Model image Fusion Technique 2

Deep Learning-based Early Lung Cancer Detection using Multi-...

引用

2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Wahengbam, Monita Sriram, M. Singh, Tonjam Gunendra Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Lung cancer, a significant global health concern, continues to take many lives, especially as a result of late-stage diagnoses. Early detection continues to have the most significant impact on patient outcomes and mortality rates. This research examines how modern image-processing methods might transform the early diagnosis of lung cancer. To investigate the application of deep learning algorithms, computer-aided diagnosis systems, and the synergistic integration of multimodal imaging techniques to increase the accuracy and efficiency of lung cancer diagnosis. The article thoroughly examines current approaches, presents a novel framework designed for early detection, and rigorously assesses the efficacy of the suggested system using actual medical imaging data. The research has the potential to radically alter lung cancer diagnosis, encourage a new era in early intervention, and eventually help to significantly lower lung cancer mortality rates by bridging the gap between cutting-edge image processing and clinical practice. © 2023 IEEE.

关键词： Computer Aided Diagnosis Deep Learning image processing Lung Cancer Multimodal Imaging Fusion

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A Survey on Automated Disease Diagnosis and Classification of Herbal Plants Using Digital image processing 6

A Survey on Automated Disease Diagnosis and Classification o...

引用

6th International conference on Information systems and Computer Networks, ISCON 2023

作者： Khandelwal, Ankit Shukla, Aasheesh Sain, Mangal GLA University Electronics and Communication Dept. Mathura India Dongseo University Information Engineering Dept. Busan Korea Republic of

ISBN: (纸本)9798350346961

This paper presents a review on automated disease detection processing. The primary issue in herbal plants is the diagnosis and stratification of its diseases. The conventional process is unpredictable and inconsistent, therefore techniques for classifying plants diseases using digital method are necessary as they speed up the identification process. This literature discusses various digital methods for classifying plant diseases. The first step is image processing of herbal plants, which make use of traditional image processing techniques and algorithms to categories diseases in herbal plants based on attributes like texture, color, and shape. The second step is plant categorization based on deep learning and machine learning, such as CNN, KNN, logistic regression etc. Deep learning applications mainly lessen the reliance on designs and preprocessing techniques by joining the entire process. This review provides a basic understanding and knowledge of the current state-of-the-art researches, as well as some dynamic trends for future work.. © 2023 IEEE.

关键词： Computer vision

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Design of Cloud Platform for Efficient Remote Sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi Xi'an China Xi'an Technical School Shaanxi Xi'an China

ISBN: (纸本)9798350315455

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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An image processing Based Approach Towards Recognition of Underground Cable Defects 2

An Image Processing Based Approach Towards Recognition of Un...

引用

2nd International conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Thampi, Aparna Mahendran, N. Abraham, Linu Mariam Sidhartha, M.S. Apj Abdul Kalam Technological University Department of Electrical and Electronics Engineering Saintgits College of Engineering Kottayam Kerala India Tata Power Delhi Distribution Ltd India Digital Core Technology Kochi India

ISBN: (纸本)9798350383041

This paper presents a novel approach for detecting possible faults in underground cables at the manufacturing industry using image processing techniques. With the increasing adoption of underground cables to minimize faults and outages in power transmission systems, ensuring the quality of these cables becomes crucial. Detecting defects at the manufacturing stage can save significant costs and effort by identifying faulty cables before they are deployed in the field. We propose a system that utilizes image processing algorithms to analyze cable images and identify potential faults, such as unbalanced currents, cable sheath faults, and short circuits. The system also calculates the area of each core to ensure equal current flow, and estimates the total cross-sectional area of the cable to assess the adequacy of insulation. The proposed approach offers a non-destructive and efficient method to enhance quality control in the manufacturing process of underground cables. © 2024 IEEE.

关键词： Underground cables

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Coding Improvement in the DAC Algorithm 13

Coding Improvement in the DAC Algorithm

引用

13th International conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Makarichev, Viktor Lukin, Vladimir Brysina, Iryna National Aerospace University KhAI Dept. of Information-Communication Technologies Kharkiv Ukraine National Aerospace University KhAI Dept. of Higher Mathematics and System Analysis Kharkiv Ukraine

ISBN: (纸本)9798350396119

Digital images are a particular type of data. They have a lot of applications. Huge image datasets have been collected. Their processing, storing, analyzing, and transferring via networks require great expenses. For this reason, a development of low resource intensive image processing algorithms is of particular importance. Discrete atomic compression (DAC) is an image compression algorithm based on atomic functions. It provides a combination of data compression and protection features with machine learning oriented data representation. Also, DAC has low time and spatial complexity, which makes its application promising. In this paper, we improve a compression performance of this algorithm. It is shown that, by applying the proposed coding scheme a compression ratio can be increased and, hence, greater memory savings can be obtained. © 2023 IEEE.

关键词： image enhancement

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Enhancing Surveillance systems with YOLO Algorithm for Real-Time Object Detection and Tracking 2

Enhancing Surveillance Systems with YOLO Algorithm for Real-...

引用

2nd International conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Anish, A. Sharan, R. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

A Virtually Impaired Person (VIP) is unable to identify objects when they cannot recognize where the object is placed. The researchers are working on it to enhance object detection and help VIP. The challenges faced by researchers are performing detection under low-resolution images, insufficient sensors, portability, and cost. Making a compact device and alerting them is required. By considering the above-mentioned difficulties, an innovative solution is described in this research work. The growth of image processing and deep learning techniqueshas simplifiedthe complexity of processing data and provided accurate results within a limited time period. The suggested technique presented is a deep learning algorithm calledthe YOLO algorithm, which is combined with the web to predict objects accurately. For this approach, a dataset with a total of 500 images was chosen and trained. The proposed classifier result is satisfactory, and it achieved an overall accuracy of 94%. Furthermore, this proposed technique provides enough output in comparison with several other machine learning and image processing algorithms. © 2023 IEEE.

关键词： Deep Learning image processing Object Detection Visually Impaired Person (VIP) YOLO Algorithm

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

引用

2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Syahda, Tabina Shafa Nabila Ma'ady, Mochamad Nizar Palefi Lidiawaty, Berlian Rahmy Telkom University Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331513320

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377;0.9150;0.9143;0.9129;and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing image Denoising Performance through a Family of algorithms 2

Enhancing Image Denoising Performance through a Family of Al...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Gupta, Omkar Kalapala, Irika Nikhita Bhurchandi, Kishor M. Visvesvaraya National Institute of Technology Department of Electronics and Communication Engineering Nagpur India

ISBN: (纸本)9798350310719

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising. © 2023 IEEE.

关键词： image denoising

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