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Depth image Super-resolution via Two-Branch Network 6th

Depth Image Super-resolution via Two-Branch Network

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6th International conference on Cognitive Systems and Signal processing, ICCSIP 2021

作者： Guo, Jiaxin Xiong, Rong Ou, Yongsheng Wang, Lin Liu, Chao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen China

ISBN: (纸本)9789811692468

With the continuous development of sensors, a number of inexpensive and effective depth cameras have shown up, which have greatly contributed to the development of autonomous driving and 3D reconstruction technologies. However, the depth images captured by low-cost depth cameras are low-resolution, which is difficult to meet the needs of practical applications. We propose a two-branch network to achieve depth map super-resolution with high-resolution guidance image, which can be viewed as a prior to guide the low-resolution depth map to restore the missing high-frequency details of structures. To emphasize the guidance role of high-resolution images, we use spatially-variant kernels based on the guidance feature map to replace the original convolution kernels. In addition, in order to extract the feature maps of the depth images more effectively, we add the channel attention mechanism between convolution layers. Our network is trained end-to-end, supporting various sizes of input images because the network backbone uses full convolution and no fully connected layers. the proposal model is trained only on a certain dataset under three super-resolution factors and utilized directly on other datasets without fine-tuning. We show the effectiveness of our model by comparing it with state-of-art methods. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A Framework for Quality Evaluation of Edible Nuts Using computer vision and Soft Computing Techniques 6th

A Framework for Quality Evaluation of Edible Nuts Using Comp...

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6th International conference on Harmony Search, Soft Computing and Applications, ICHSA 2020

作者： Narendra, V.G. Prasad, G. Shiva Pinto, Ancilla J. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

ISBN: (纸本)9789811586026

Generally, humans use edible nuts as food (edible oils, condiments, spices, or drinks) since prehistoric times. the foods are high in protein, minerals, vitamins, and energy so-called as nutrition for human foods. the requirement for precise, quick, and unbiased quality determination of these features increases with increasing demands of decent quality and safety requirements for food products. To satisfy these criteria, digital perception and soft computing provide alternatives to an adaptive, non-destructive, and economical approach. this study has identified a wide range of applications in the food industry, based on image analysis and processing. the consistency study of meat and seafood, bakery food like bread, pizza, cheese, and biscuits was conducted effectively in computer vision and soft computing. this study has been tested on the quality and characteristics of edible nuts. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soft computing

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EMERGING thREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT 22

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT

引用

Proceedings of the 6th International conference on Future Networks & Distributed Systems

作者： Murooj Amer Taha Wijdan Mahood Khudhair Ahmed Mahmood Khudhur Omar Abdulkareem Mahmood Yousif I. Hammadi Riyam Shihab Ahmed Al-husseinawi Ahmed Aziz Department of Computer Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. these procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. this study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.

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BlendPose: A New Linear Parametric Hand Model for High-quality 3D Hand Reconstruction 22

BlendPose: A New Linear Parametric Hand Model for High-quali...

引用

Proceedings of the 2022 6th International conference on Video and image processing

作者： Ruting Rao Yangang Wang School of Automation Southeast University China

ISBN: (纸本)9781450397568

As 3D hand models have attracted extensive attention in human-computer interaction, VR and AR fields, building realistic hand models is becoming a long-term research hotspot. Performances of many existing 3D hand models may be limited due to their low resolution, sparsity and non-linearity. In this paper, we introduce a new linear parametric hand model termed BlendPose for hand reconstruction and prospose a new framework to redefine the relationship between a hand mesh and its 3D joints. BlendPose is a global parametric-driven model, reconstructing hand shape and pose simultaneously. the key idea of BlendPose is a hand mesh can be expressed by the linear combination of several hand posture models and a rest-pose model. Additionally, the new framework first reconstruct personalized hand mesh via BlendPose and then regress 3D joints from mesh vertices using a trainable joints regressor, which is quite different from other skeleton-driven models. We experimentally show that BlendPose can be applied to challenging datasets such as OneHand10k and Freihand while achieving high-quality reconstruction results.

关键词： computer graphics datasets hand reconstruction

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Black Cloud Generation Method for Privacy Protection in Fall Detection 22

Black Cloud Generation Method for Privacy Protection in Fall...

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Proceedings of the 2022 6th International conference on Video and image processing

作者： Runqing Zhang Haoran Yan Dunbo Cai Ling Qian Center for Technology Research & Innovation China Mobile (Suzhou) Software Technology Co. Ltd. China Mobile (Suzhou) Software Technology Co. Ltd. China

ISBN: (纸本)9781450397568

Fall detection is an important application in computer vision. However, the image data for fall detection contain unprotected privacy. For example, the silhouette of a person who falls may divulge information about whether the person is disabled. In addition, privacy, such as the age and height of the fallen person, will also be apparent in the image. To tackle these issues, this paper proposes a novel approach, called Black Cloud for Falling Detection (BCFD), to protect the privacy information of the fallen person. We introduce a new image segmentation method to estimate the contour of the human body image, and we generate 'black clouds' to accurately cover the complete human images. the detector was conducted using the contour information to obtain the fallen person. We also propose a method to construct a privacy-protected fall detection dataset and provide a sample dataset. the proposed method can protect the privacy information precisely compared with the state-of-the-art methods. We have validated our method on the proposed PPFD-sample dataset, observing 90.2% precision and 81.8% recall. Experimental results demonstrate that our approach can protect image privacy effectively in the fallen person detection task.

关键词： Privacy Protection Deep Learning Fall Detection image Segmentation

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Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification 6th

Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-B...

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Murmu, Anita Kumar, Piyush Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna800005 India

ISBN: (纸本)9783031530845

Diabetic Retinopathy (DR) is the main cause of blindness and harms the retina due to the accumulation of glucose in the blood. therefore, early DR detection, diagnosis, segmentation, and classification prevent patients with diabetes from losing their vision. However, the main challenge is the test takes time, lots of procedure, and money for proliferative stage of DR and identifying the proper glucose level that is present in the blood vessel. To address this problem, a unique and hybrid method, namely Deep Convolutional Neural Network (CNN) with EFficientNetB0 (Deep-CNNEF-Net), is used for earlier detection and classification of DR. Furthermore, the proposed scheme follows the pre-processing, input feature extraction, and classification phases. the pre-processing stage improves the presence of abnormalities and segmentation of DR by using the CNN with mean orientation technique. Moreover, the input feature extraction step uses CNN to obtain features that are important for training purposes. Furthermore, a novel modification to the VGG16 pooling layer uses the Global Average Pooling Layer (GAP) without a fattening layer for the boundary box of infected retinal blood vessels. the experimental results are obtained using two databases, namely the Digital Retinal images for Vessel Extraction (DRIVE) and the STructured Analysis of the Retina (STARE) datasets, for the classification and detection of DR. the simulation results have been examined by evaluation metrics, such as accuracy, precision, recall, F1-score, specificity, and Mean Process Time per Frame (MPTF) for proper validation. the evaluation analysis outperforms the existing State-Of-the-Art (SOTA) models on the same eye retinal DR datasets. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Intelligent video editing: Incorporating modern talking face generation algorithms in a video editor 21

Intelligent video editing: Incorporating modern talking face...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Gupta, Anchit Khan, Faizan Farooq Mukhopadhyay, Rudrabha Namboodiri, Vinay P. Jawahar, C.V. IIIT Telangana Hyderabad India University of Bath United Kingdom

ISBN: (纸本)9781450391276

this paper proposes a video editor based on OpenShot with several state-of-the-art facial video editing algorithms as added functionalities. Our editor provides an easy-to-use interface to apply modern lip-syncing algorithms interactively. Apart from lip-syncing, the editor also uses audio and facial re-enactment to generate expressive talking faces. the manual control improves the overall experience of video editing without missing out on the benefits of modern synthetic video generation algorithms. this control enables us to lip-sync complex dubbed movie scenes, interviews, television shows, and other visual content. Furthermore, our editor provides features that automatically translate lectures from spoken content, lip-sync of the professor, and background content like slides. While doing so, we also tackle the critical aspect of synchronizing background content with the translated speech. We qualitatively evaluate the usefulness of the proposed editor by conducting human evaluations. Our evaluations show a clear improvement in the efficiency of using human editors and an improved video generation quality. We attach demo videos with the supplementary material clearly explaining the tool and also showcasing multiple results. © 2021 ACM.

关键词： Video signal processing

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A Lightweight 3D Segmentation Network for Abdominal Liver in CT image 22

A Lightweight 3D Segmentation Network for Abdominal Liver in...

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Proceedings of the 6th International conference on Advances in image processing

作者： Junlin Zhao Tianlin Zhang Ling Gao Wenbo Wan Jian Wang School of Information Science and Engineering Shandong Normal University China

ISBN: (纸本)9781450397155

Abnormal liver function is linked to a variety of disorders. Precise and quick automatic liver segmentation can help clinicians make better diagnosis and treatment decisions. With the development of computer vision and deep learning approaches, there are more solutions for biomedical image segmentation tasks. In recent years, the U-Net architecture is by far the most widely used backbone architecture for biomedical image segmentation. Deep convolutional neural networks-based semantic segmentation has achieved sufficient accuracy. However, the scale of high-precision networks is growing, requiring an increasing amount of storage and computational resources. Furthermore, the deep neural network's operating time is lengthy, making it difficult to satisfy practical needs. As a result, the lightweight convolutional neural network design is used to the semantic segmentation task. As a consequence, in this article, a lightweight convolutional neural network is proposed to solve the aforementioned problems in the task of biomedical image segmentation. 3D U-Net is used as the backbone architecture and a modification of the Ghost module from GhostNet is introduced to boost up the effectiveness and the learning efficiency. the experimental results demonstrate that the proposed network improved the segmentation performance with fewer network parameters and requiring less floating-point computation.

关键词： Ghost module 3D U-Net CT image Liver segmentation

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Local image Analysis of Malaysian Herbs Leaves Using Canny Edge Detection Algorithm 6th

Local Image Analysis of Malaysian Herbs Leaves Using Canny E...

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6th International conference on Soft Computing in Data Science (SCDS)

作者： Othman, Zuraini Ahmad, Sharifah Sakinah Syed Kasmin, Fauziah Abdullah, Azizi Shari, Nur Hajar Zamah Univ Tekn Malaysia Melaka Fak Teknol Maklumat & Komunikasi Durian Tunggal 76100 Melaka Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Darul Malaysia Forest Res Inst Malaysia FRIM Forestry & Environm Div Kepong 52109 Malaysia

ISBN: (纸本)9789811673344;9789811673337

Machine vision helps a lot with the latest recognition technology. To get the best recognition results, the initial phase of image processing should be done as best as possible. this phase involves the production of an image map of the resulting image using edge detection. the Canny method is often used because of its performance of producing meticulous strong edges but this method is sensitive to changes in image intensity when involving complex images such as image leaves and results in a lot of noise at the edges of the resulting image. this is because in this method the threshold value is selected empirically on the image globally. In this study, local image analysis will be discussed along with its impact on the resulting image edge results. In addition, a set of data from herbal leaves in Malaysia has also been produced by containing ground truth images for each herbal image available. the results from this study found that the locally analysing image approach has outperform the global approach of the conventional Canny method. Findings from this study may help the identification system in the future.

关键词： Machine vision Edge detection Canny method Local image analysis Malaysian herbs leaves images

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A Comprehensive Review on Quality Prediction of Fruits and Vegetables using Feature Extraction and Machine Learning Techniques

A Comprehensive Review on Quality Prediction of Fruits and V...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Anly Antony M R Satheeshkumar Dept. of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kerala

ISBN: (纸本)9781665482721

the quality estimation of fruits and vegetables plays a vital role in the field of agriculture. this paper reviews the latest improvements in estimating the quality of fruits and vegetables as well as grading them using machine learning techniques. As fruits and vegetables have high nutritional value, their sales are on high demand. the prime importance is given to the supply of toxin-free, premium quality products to the end-users. Quality of a fruits and vegetables highly affected by detecting the defects on them. Keeping the spoiled foods along with good food may contaminate the whole collection. Features of interest are needed for proper identification of food product. After extracting and refining features of interest, the images can be trained to error free categorization. this paper presents an elaborated description of various feature extraction and machine learning techniques to identify and grade different kinds of fruits and vegetables. this research study has reviewed many articles to sort out the problems in estimating the quality and classifying them according to the need. the results of this review show that incorporating image processing and computer vision techniques with machine learning techniques surpasses the traditional methods.

关键词： computer vision Refining Estimation Machine learning Aerospace electronics Feature extraction Agriculture

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