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

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image Enhancement of Underwater images Using Deep Learning Techniques

Image Enhancement of Underwater Images Using Deep Learning T...

引用

International conference on Data Analytics and Insights, ICDAI 2023

作者： Dasari, Siva Krishna Sravani, Laveti Kumar, Molleti Upendra Rama Venkata Sai, N. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Vizianagaram Rajam India

ISBN: (纸本)9789819938773

In the present era enhancing the under image quality play a major role for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a number of issues including blurriness, dim lighting, poor contrast, and colour fidelity. Light attenuation, absorption, and scattering lower the quality. As a result, it is essential to enhance underwater images, and different image processing techniques are used for enhancement. The deep learning methodology used in this work to improves image quality and produce the best outcomes when compared to conventional image enhancing techniques. The architecture used is GAN with the combination of image processing algorithms such as histogram equalization as a preprocessing step for implementing deep learning models. The algorithms include histogram equalization and Dark Channel Prior. These methods normalize the contrast of images and removes the hazing or dark parts of the images. The dataset used in this work is EUVP (Paired) which contains about 2186 real-world underwater images. Three parameters, UICM, MSE, and PSNR are used to assess quality of enhanced images. The optimum technique for improving the images is determined by comparing the performance of the models after the dataset has been trained through them. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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On the Computational Complexity of 2D Filtering by Winograd Method 11

On the Computational Complexity of 2D Filtering by Winograd ...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and Internet of Things (CPS and IoT)

作者： Lyakhov, Pavel A. Abdulsalyamova, Albina S. Semyonova, Nataliya F. Nagornov, Nikolay N. Voznesensky, Alexander S. Kaplun, Dmitrii, I North Caucasus Fed Univ North Caucasus Ctr Math Res Stavropol Russia North Caucasus Fed Univ Dept Math Modeling Stavropol Russia St Petersburg Electrotech Univ LETI Dept Automat & Control Proc St Petersburg Russia

ISBN: (纸本)9781665468282

Increasing the speed of digital image processing is an important problem in various fields. This paper proposes a way to increase the speed of 2D image filtering using the Winograd method. New algorithms for image processing based on the Winograd method have been proposed. The theoretical analysis results showed that an increase in the number of pixels in the resulting image fragment leads to a decrease in multiplications and the computational complexity of digital filtering. Winograd methods reduced multiplications to 4 times by increasing additions to 2.88 times using filter 3x3. The results obtained can be used in various image processing fields that require real-time processing using modern microelectronic devices.

关键词： Winograd method 2D filtering digital image processing filtering algorithms computational complexity

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image processing in Retail Marketing: Innovations for Customer Engagement, Sales, and Operational Efficiency 12th

Image Processing in Retail Marketing: Innovations for Cust...

引用

12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Rastogi, Isha Sinha, Utkrishta Gaur, Anubha Department of Information Technology KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789819788354

The retail industry, marked by fierce competition and evolving consumer preferences, demands innovative approaches to boost customer satisfaction, drive sales, and streamline operations. This study presents a comprehensive strategy leveraging cutting-edge image processing technologies such as facial recognition, object detection, and emotional analysis. The methodologies proposed encompass emotional profiling through facial recognition, personalized offline store experiences enabled by facial recognition technology, and automated inventory management via object detection algorithms. Real-world applications and case studies validate the efficacy of these techniques, showcasing their transformative potential in redefining the retail landscape. By integrating advanced image processing solutions, retailers can not only meet the dynamic demands of modern consumers but also gain insights to optimize business processes and deliver unparalleled shopping experiences. This research underscores the pivotal role of technology in shaping the future of retail, offering practical insights for industry stakeholders to navigate and thrive in an increasingly competitive market landscape. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inventory control

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Identification of Counterfeit Indian Currency Note using image processing and Machine Learning Classifiers 3

Identification of Counterfeit Indian Currency Note using Ima...

引用

3rd International conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sharan, Vivek Kaur, Amandeep Singh, Parvinder Central University of Punjab Dept. of Computer Science and Technology Punjab Bathinda India

ISBN: (纸本)9781665462167

Technology is continuously changing our life. Day by day, it makes our life easy, but some challenges and issues exist. Counterfeit currency is one of them. It happens because of the production and circulation of currency without the permission of an authorized system. Some people use scanning and printing technology to produce such notes and circulate them around us, which is a kind of *** leads to personal loss and degrades the Country's economy. Such notes are very similar to the original, which becomes a problem for ordinary people to identify the authenticity of the currency, especially for visually impaired people. However, most researchers proposed different methods to differentiate real notes from fake ones based on the currency's shape, colors, and size. The notes are easily detected when the condition of the note is good but difficult when it deteriorates over time. Extracting features from such notes is another challenging task. Some systems are available to only specific sectors and not easily available to common people. Therefore, a system that can distinguish between real and fake notes is required. This article classifies Indian Currency notes as real or fake with four supervised Machine Learning algorithms i.e., Support Vector Classifier, K-Nearest Neighbor, Decision Tree, and Logistic Regression followed by image processing techniques. This study has implemented these algorithms on the dataset of 1372 currency image samples, out of which 762 are real, and the remaining are fake, available on the UCI machine learning repository. Further, the performance of all the algorithms is measured in terms of accuracy, recall, Precision, and F1-score and after analyzing, it is observed that the K-Nearest Neighbor leverage outstanding results compared to other algorithms. © 2023 IEEE.

关键词： Decision trees

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Baker’s Map Based Chaotic image Encryption in Military Surveillance systems 1

引用

International conference on Internet of Everything and Quantum Information processing, IEQIP 2023

作者： Rajendiran, Nithya Paranthaman Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

One of the primary problems in military surveillance systems is the safety of the visual data gathered by the sensor nodes installed in the different areas of the Visual sensor network and the remotely operated devices. There are several cryptographic algorithms available to secure the visual information. Several chaotic schemes worked well because they were more resistant to threats and had greater entropy levels. But, it takes a lot of time to finish the encryption step. Traditional approaches had the drawback that increasing encryption strength slowed calculation speed. This paper presents a scheme called "baker’s map chaotic method based image encryption" to accelerate the encryption process in a network of visual sensors without weakening the encryption method. The findings indicate that the system’s functionality is improved when the computation time is reduced to sixty seconds and the PSNR is increased to 15 dB. The performance of the chaotic method is compared by using key space analysis, key sensitivity analysis, correlation coefficient analysis, noise analysis, Entropy analysis, and Speed analysis. The IBM SPSS statistical tool was also used to do statistical analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sensitivity analysis

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Study on Hyperspectral Remote Sensing images of GF-5 De-Blurring Based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

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5th International Symposium on Signal processing systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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Advancements in image Deblurring and Performance Metrics Using Deep Learning Technique 12th

Advancements in Image Deblurring and Performance Metrics Usi...

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12th International conference on Recent Trends in Computing, ICRTC 2024

作者： Dhavalagimath, Shankramma S. Rajesh, T.M. Singh, Rakesh Kumar Department of Mathematics Dayananda Sagar University Bangalore562112 India Department of Computer Science and Engineering Dayananda Sagar University Bangalore562112 India

ISBN: (纸本)9789819788354

In computer vision and image processing, image deblurring is a crucial phase that attempts to restore the sharpness of the image and clarity of images that have been damaged due to motion blur, defocus, or other factors. The multiple image deblurring methods are thoroughly investigated in this work, along with the performance evaluation metrics that were employed to evaluate each method's efficiency. We started by examining the most recent deblurring techniques, which include both deep learning-based and traditional algorithms. Every technique's advantages, disadvantages, and suitability are examined in different scenarios. Furthermore, we investigate the finer details of performance metrics that are frequently used to rate the quality of deblurred images. These metrics include subjective assessments from user research &objective measurements like PSNR and SSIM. This study aims to elucidate the benefits and drawbacks of current deblurring methods in diverse contexts through extensive experimentation. Moreover, we examine the applicability and consistency of performance evaluation metrics for evaluating the accuracy and perceived quality of deblurred images. By supporting researchers and professionals in identifying suitable techniques and assessment standards for their particular applications, the knowledge gained from this study improves the field of image deblurring. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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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...

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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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