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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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NovaNet-SSL: A Self-Supervised DDPM Model for Underwater image Enhancement

NovaNet-SSL: A Self-Supervised DDPM Model for Underwater Ima...

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Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Priya Reka S Senthilkumar Aandi Dept. of Electronics and Communication Advanced Communication Technology Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Marine images suffer from severe tint of color and blurry feature caused by distortion in water. Enhancement of such images is necessary to be developed for the improvement of quality vision tasks underwater. This paper proposes a self-supervised diffusion model, called NovaNet, for real-time enhancement of underwater images (UIE). This technique uses the power of DDPM and optimized the process using self-supervised learning. NovaNet accelerates the inference process without a compromise on enhancement quality; it performs faster and more accurately than any other generative model. Unlike conventional DDPM models, which suffer from slow processing and color bias while used for UIE, this approach integrates self-supervised learning to effectively guide the model, improving the quality of image restoration. The novelty in this approach is self-supervised learning with diffusion-based image restoration to produce results of high quality, that is, enhanced quality output and fast processing speed. NovaNet demonstrates a 20.34% improvement in PSNR, a 0.5351% gain in SSIM, and achieves a 0.036% faster processing speed compared to existing models. This proposed model is tested against several challenging real-world underwater datasets, which are characterized to have superior performance regarding enhanced quality and real-time process capabilities.

关键词： Adaptation models image color analysis Surveillance Noise reduction Self-supervised learning Turbidity Diffusion models Real-time systems Robustness image restoration

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Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using Deep Learning Deployment in Effective Application

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

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computer Communication and Artificial Intelligence (CCAI), international conference on

作者： Md Abrar Hamim Jeba Tahseen Kazi Md Istiyak Hossain Nasrin Akter Umme Fatema Tuj Asha Department of Computer Science Daffodil International University Dhaka Bangladesh

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent.

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Research on Building Crack Width Monitoring Method Based on computer vision

Research on Building Crack Width Monitoring Method Based on ...

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Signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Houyan Wang Shuoting Xiao Ningbo Youzuo Construction Technology Company Ningbo Zhejiang China Institute of Civil Engineering and Architecture Ural Federal University Ekaterinburg Russia

As construction projects enter a new era, building crack width monitoring has entered a stage of high-quality development. It is necessary to propose image processing propositions that better meet the needs of crack width monitoring for building safety by focusing on the deep learning idea with image segmentation as the core. Based on the dynamic evolution of image processing development, according to the internal logic of the convolutional neural network, a theoretical analysis framework for the development of image segmentation is constructed. It can explain the image segmentation development mechanism jointly generated by the image processing mechanism and the optimization loop mechanism involved in image sampling and segmentation. From the perspective of quality change and practical deduction of image segmentation development, the possibility of moving towards the high-quality development goal of building crack width monitoring is further explored. The purpose of developing building crack width monitoring is to provide crack width estimates that meet expected standards for construction projects, which is committed to continuously improving the quality of crack width and improving the satisfaction of crack width monitoring. Therefore, it is necessary to strengthen the image segmentation control based on the quality of the inner loop of the convolutional neural network. Establishing an interaction and feedback mechanism between image segmentation and perception of the quality of a construction project, as well as establishing an evaluation system for image segmentation and monitoring of the crack width of a building, which will achieve high-quality development of building crack width monitoring, promote construction project safety, and truly meet the needs of construction projects.

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Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single image Super Resolution Quality Enhancement

Comparative Study of Implementation of Very Deep Super Resol...

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international conference on Applied Electromagnetics, Signal processing and Communication (AESPC)

作者： Ganesan P M. Ravichandran B.S. Sathish L.M.I.Leo Joseph G. Sajiv R. Murugesan Dept. of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India Dept. of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of Computer Science Engineering AI Sri Venkateshwara College of Engineering Bengaluru India Dept. of Electronics and Communication Engineering SR University Warangal Telangana India Saveetha School of Engineering SIMATS Chennai India Dept. of Electronics and Communication Engineering Narshima Reddy Engineering College Secunderabad India

To improve the resolution and quality of a low level image, single image super resolution (SISR) is a challenging endeavor in the turf of computer vision. It is essential to many applications, including image editing, security systems, medical imaging, and others. In order to increase the spatial quality of a low level image, SISR attempts to approximate the high frequency details that are missing from the image. The field was formerly based on manually created features and interpolation techniques, but current developments in deep learning have completely changed it. The suggested work compares the effectiveness of bicubic interpolation and neural network based very deep super resolution for single image super resolution image quality improvement. Utilizing blind and complete reference picture quality measures, the effectiveness of the two approaches is evaluated. The effectiveness of the very deep super resolution for single image is clearly demonstrated by the experimental results.

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Large Scale image Data processing and Content Recognition Based on Deep Learning

Large Scale Image Data Processing and Content Recognition Ba...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Ruoyan Wan Xiuzhuo Wei Rui Ma Huinan Zhao Feng Wu Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology.

关键词： Deep learning image recognition Accuracy Face recognition Training data Artificial neural networks Solids Data models Security Artificial intelligence

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Intelligent Surveillance Systems for Smart Cities: A Systematic Literature Review 3rd

Intelligent Surveillance Systems for Smart Cities: A Systema...

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3rd international conference on Smart Systems - Innovations in Computing (SSIC)

作者： Ghoniem, Nour Ahmed Hesham, Samiha Fares, Sandra Hesham, Mariam Shaheen, Lobna Halim, Islam Tharwat Abdel Misr Int Univ MIU Fac Comp Sci Cairo Egypt

ISBN: (纸本)9789811628771;9789811628764

The concept of smart cities is emerging rapidly with the objective of using technology to enhance the efficiency and the quality of services provided to the population in such cities. Due to the importance of having continuous and uninterrupted urban space monitoring, many studies have been carried out to propose intelligent surveillance systems for smart cities. Several research topics have already been explored in such domain However, given the variety of problems and solutions, it is complex to identify research trends and gaps related to intelligent surveillance systems for smart cities without a systematic review. Therefore, this paper presents a systematic literature review of such a topic. The review revealed current research trends, challenges, and future directions in the area of intelligent surveillance systems. This work provides a list of references to find the mapped papers, thus paving the way for enhancing the efficiency, reliability, and robustness of such systems.

关键词： Cloud and edge computing computer vision Digital image and video processing Internet of Things (IoT) Security and privacy

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A Review of Intrinsic image Decomposition

A Review of Intrinsic Image Decomposition

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image processing and Media Computing (ICIPMC), international conference on

作者： Siyuan Liu Xiaoyue Jiang Letian Liu Zhaoqiang Xia Sihang Dang Xiaoyi Feng School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386677

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images, namely shading and reflectance, which is widely applied in computer vision and image processing tasks for the better understanding of the objects and scenes. However, IID is a severely ill-posed problem, extra constraints should be applied to address the decomposition problem. In this paper, we reviewed recent papers about intrinsic image decomposition. The extra cues from lighting, shape, color and textures are used to constrain the ill-posed problem. These papers can be mainly classified into two categories: physics-based methods and learning-based methods. Physics-based methods mainly introduced extra constraints into an optimization framework to solve the IID problem. While learning-based methods mainly depend on an encoder-decoder network with the explicit constraint from the labeled intrinsic images. Additionally, extra priors are introduced into the network by the form of network structure or specific feature. In order to make the IID problem more practical, researchers may explore how to achieve fast and accurate image decomposition with lower computing resources through optimization algorithms and model design.

关键词： Learning systems Accuracy image color analysis Shape Computational modeling image decomposition Real-time systems

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Zernike Moment and Mutual Information Based Methods for Multimodal image Registration 3rd

Zernike Moment and Mutual Information Based Methods for Mult...

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

作者： Kashyap, Suraj Kumar Jat, Dinesh Bhuyan, M. K. Vishwakarma, Amit Gadde, Prathik Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India Indiana Univ Purdue Univ Sch Informat & Comp 535 West Michigan St Indianapolis IN 46202 USA

ISBN: (纸本)9789813292918;9789813292901

image registration enables joint operations between images obtained from diverse sources. However, there have been limited advances in the registration of multichannel images. The accuracy of registration is a significant concern for medical applications, among others. Two methods, PCA-ZM and CED-ZM, have been proposed for registration based on Zernike moment and enhanced mutual information. Edge detection by Zernike moment and identification of common features in multichannel images are used as a foundation to improve accuracy over single-channel registrations. Single-channel registration accuracy for MRI and SPECT brain images is found to surpass the methods compared against. PCA-ZM demonstrates good accuracy for MR-MR registration, while CED-ZM has good accuracy for MR-SPECT registration. These measures improve upon accurate registration for images, especially where many modalities are available, such as in medical diagnosis.

关键词： Multimodal Multichannel MRI SPECT Medical image registration Zernike moment Enhanced mutual information Principal component analysis Canny edge detector Gradient information Edge overlap

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Research on TOF camera-based guided binocular fusion technology 3

Research on TOF camera-based guided binocular fusion technol...

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3rd IEEE international conference on Frontiers Technology of Information and computer, ICFTIC 2021

作者： Gao, Siyuan Fang, Lichao Cui, Xintian Fuyang Normal University Fuyang China Tianjin University of Technology Tianjin China Dalian Jiaotong University Dalian China

ISBN: (纸本)9781665406055

As environment perception becomes increasingly necessary in human-computer interaction tasks, researchers are applying it to specific tasks, such as depth-of-field measurements by depth cameras. In order to improve the accuracy of depth information in-depth maps obtained by current depth cameras, this paper uses the complementary properties of TOF technology and binocular stereo vision technology to fuse TOF and binocular technology, guiding binocular stereo matching by matching TOF parallax maps with binocular parallax map feature points, and discussing the process of guided matching. Among them, the better Census matching algorithm is selected for experiments in the stereo matching process. The binocular matching after TOF guidance by this method significantly improves the matching time and the quality of the matched parallax map. © 2021 IEEE.

关键词： Stereo image processing

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Enhancing Facial Emotion Recognition through Deep Learning and image Pre-processing

Enhancing Facial Emotion Recognition through Deep Learning a...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Ekta Singh Parma Nand Department of Computer Science & Engineering Sharda University Greater Noida India

Humans frequently communicate effectively using their faces in addition to their words. The use of computer vision techniques to categorize face emotion has been extensively studied. Given the difficulties and restrictions of databases, such as the use of static data or facial capture in artificial contexts, these have had different degrees of success. As a result, we think that novel preprocessing methods are necessary to increase the precision of facial expression detection models. In this study, we suggest a novel, yet straightforward, approach to improving the precision of face emotion identification. Our research was done using the FER2013 dataset, which includes static facial photos of 7 basic emotions. Resizing and Normalization image preprocessing techniques were applied on the dataset prior to the splitting into train and test sets. The training images were split into train and test sets in the ratios of 75% and 25% respectively. The facial expressions in the photos were classified using Convolution Neural Networks [CNNs], with an accuracy of 76.75% on the test set.

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