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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4813 条记录，以下是521-530 订阅

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A Novel Virtual Reality (VR) based Intelligent Guiding System

A Novel Virtual Reality (VR) based Intelligent Guiding Syste...

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2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Fuxiong, Chen Marx College of GuangXi Talent International College GuangXi QinZhou China

ISBN: (纸本)9781665474511

A novel VR intelligent guiding system with image processing and programming methods is designed in this study. The fundamental goal of VR technology is to achieve realistic experiences and also natural technology-based human-computer interaction. human-computer interaction based on the natural technologies, so a system that achieves or partially achieves such a goal can be referred to as virtual reality systems. In the designed system, the novel contains the image processing and programming methods. (1) The novel image virtualization method is designed to provide the novel idea of the image pre-processing. (2) The suitable programming framework is designed to make the system efficient. Through the experiment on the UI and the systematic implementation, the performance is tested. © 2023 ieee.

关键词： Virtual reality

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Construction of image Recognition Model Based on Second-Order Local Feature Aggregation 3

Construction of Image Recognition Model Based on Second-Orde...

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3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Min, Gang Shu, Yajie Zhang, Qian Zhao, Huaju Shu, Zhong Jingmen Mobile Media Co. Ltd Jingmen Daily Hubei Jingmen China Jingchu University of Technology School of Electronic Information Engineering Hubei Jingmen China

ISBN: (纸本)9781665472784

This paper analyzes the mathematical model constructed by the Net VLAD method, and proposes a second-order function based on the Net VLAD method to solve the problem that the obtained image feature encoding is a first-order function of the local information of the image. image feature encoding learning dictionary. Use the Net VLAD classifier as the fully connected layer of the CNN model, design the reconstruction residual weight, and redesign the image feature extraction module for the control of the reconstruction residual value range;when the image feature encoding in the Net VLAD learning dictionary is described by a first-order vector, there is a problem that it is difficult to derive and calculate the minimum value of image features. In the image dimensionality reduction transformation kernel, the first-order linear function is promoted to a second-order nonlinear function, and the image dimensionality reduction transformation kernel is used as a breakthrough, and the second-order nonlinear transformation kernel is used. The activation function has the characteristic of being derivable, and the local features of the image samples are subjected to dimensionality reduction processing to achieve more accurate dimensionality reduction calculations for image features. In the experiment, the accuracy of image feature classification, the recognition rate of image samples, and the training and testing efficiency of image samples are used as verification data. By using the CNN deep convolutional neural network model, the Net VLAD learning dictionary and the improved learning dictionary proposed in this paper, the Multiple image sample sets are compared for training and testing, which verifies that the improved learning dictionary based on Net VLAD proposed in this paper has obvious advantages. © 2023 ieee.

关键词： image enhancement

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Vision Transformer (ViT)-based applications in image Classification 9

Vision Transformer (ViT)-based Applications in Image Classif...

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9th ieee international conference on Big Data Security on Cloud (BigDataSecurity) / 9th ieee international conference on High Performance and Smart Computing (ieee HPSC) / 9th ieee international conference on Intelligent Data and Security (ieee IDS)

作者： Huo, Yingzi Jin, Kai Cai, Jiahong Xiong, Huixuan Pang, Jiacheng Hunan Univ Sci & Technol Sch Comp Sci & Engn Hunan Key Lab Serv Comp & Novel Software Technol Xiangtan 411201 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410002 Peoples R China

ISBN: (纸本)9798350312935

In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification implementation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN -based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.

关键词： CNN image classification token vision transformer Vision Reservoir

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Proceedings - 2023 ieee 19th international conference on Intelligent computer Communication and processing conference, ICCP 2023

Proceedings - 2023 IEEE 19th International Conference on Int...

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19th ieee international conference on Intelligent computer Communication and processing conference, ICCP 2023

ISBN: (纸本)9798350370355

The proceedings contain 54 papers. The topics discussed include: a machine learning based platform for remote management of heart failure patients;optical flow with semantic guidance and uncertainty estimation for robust video perception;using artificial intelligence to fight clickbait in Romanian news articles;symbolic analysis based pipeline for EEG data;MITRE tactics inference from Splunk queries;Im2Vide0: a zero-shot approach using diffusion models for natural language conditioned image-to-video;extracting appliance behavior from heterogeneous data;and modeling the deformation behavior of wind turbine blades using artificial neural networks.

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Fibonet: A Light-weight and Efficient Neural Network for image Segmentation 30

Fibonet: A Light-weight and Efficient Neural Network for Ima...

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30th ieee international conference on image processing (ICIP)

作者： Wu, Ruohao Xiao, Xi Hu, Guangwu Zhao, Hanqing Zhang, Han Peng, Yongqing

ISBN: (纸本)9781728198354

In recent years, accurate models for image segmentation have become larger and more complex. However, it is hard to apply them into embedded devices which usually have limited space for data, energy and computing. Meanwhile, since many embedded devices do not support modifications of computing units, simple models with such requirements cannot be adapted to these devices. To achieve high accuracy for image segmentation in embedded devices, we propose a light-weight and efficient neural network, named Fibonet. Fibonet is constructed by cascading two Fiboblocks, with the Fibonacci structure which adjusts the combination of basic computing units through skip connections and feature reusing so that it is less computationally intensive and dataset-demanding. The experiments demonstrate that Fibonet can be embedded to the mobile terminal for real-time segmentation and can effectively balance accuracy and computing resources. Compared with Resnet18, Fibonet achieves 10.8% higher accuracy performance (mIoU: 0.533 vs. 0.481) with 84.4% fewer parameters (0.441M vs. 2.82M) using similar model width and depth.

关键词： Fibonacci Structure Computing Resources image Segmentation

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Partial differential equations for image processing applications 5

Partial differential equations for image processing applicat...

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5th international Academic Exchange conference on Science and Technology Innovation, IAECST 2023

作者： Lv, Linlin Heilongjiang University of Finance and Economics College of General Education Harbin China

ISBN: (纸本)9798350357738

image edge detection is an important step in image processing. The current edge detection methods have low quality in extracting weak edges, which affects the efficiency and quality of detection. Therefore, this paper utilizes partial differential equations, gradient information of edges, and reconstructed stop functions to improve the quality of weak edge detection. Moreover, it can adaptively select different thresholds for each pixel based on the gradient of the image. This method achieved good results through experimental simulation and improved the accuracy of edge detection. © 2023 ieee.

关键词： Partial differential equations

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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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MEDICAL WASTE SORTING: A computer VISION APPROACH FOR ASSISTED PRIMARY SORTING

MEDICAL WASTE SORTING: A COMPUTER VISION APPROACH FOR ASSIST...

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ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bruno, A. Caudai, C. Leone, G. R. Martinelli, M. Moroni, D. Crotti, F. CNR Inst Informat Sci & Technol Pisa Italy CISA Lucca Italy

ISBN: (纸本)9798350302615

Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community, with which we have trained a model that achieves 100% accuracy, and a new dataset on which the trained model exhibits good generalization.

关键词： Medical waste management computer vision image classification Annotated dataset Automated sorting

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Advancements in image Forgery Detection: A Comprehensive Survey 7

Advancements in Image Forgery Detection: A Comprehensive Sur...

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7th international conference on image Information processing, ICIIP 2023

作者： Parmar, Parth Diwan, Anjali Mohan, Monisha Marwadi University Computer Engineering Depaertment Rajkot India Marwadi University Computer Engineering Depaertment-AI Rajkot India

ISBN: (纸本)9798350371406

The alteration, editing, or creation of digital photographs for deceit, misrepresentation, or fraud is called 'Digital image Forgery.' image manipulation entails utilizing numerous methods and tools to change an image's content or metadata to produce a convincing fake. Digital picture alteration may be malevolent, enjoyable, informative, or propaganda. The ability to identify and prevent digital picture fraud is essential to the dependability of visual data in the digital age. In this article, we discuss the many forms of Digital image Forgery, its origins, the image forging life cycle, and its stages. Here, we compare several methods of image forgery in an effort to assist upcoming researchers. © 2023 ieee.

关键词： Life cycle

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Lightwight image Super-Resolution: The Shift Ladder Transformer Approach 2

Lightwight Image Super-Resolution: The Shift Ladder Transfor...

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2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Jian Wu, Yujie Ma, Jingbin Ling, Siyao Li, Zhaoyan School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350331417

In lightweight Super Resolution (SR) tasks, balancing performance and efficiency is a challenge for CNN-based models. The Transformer architecture has been introduced into super-resolution tasks due to its global self-attention mechanism. However, existing lightweight image super-resolution networks based on Transformers typically employ localized attention to sparsify their attention, thereby limiting their receptive field and uniformity of pixel weight distribution. To address these challenges, this paper introduces the Shift Ladder Transformer for Super Resolution (slTSR), a lightweight model that melds lightweight convolutional layers with a distinct residual Shift Ladder Transformer block. With the integration of the Shift Ladder Transformer Layer (SLTL) strategy, slTSR effectively amalgamates information from diverse spatial windows, enhancing information exchange. Experiments on diverse datasets consistently highlight the exceptional performance and efficiency of slTSR. © 2023 ieee.

关键词： Lightweight image super-resolution Localized attention Shift Ladder Transformer

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