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

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Research on Post-processing System of computer 3D Animation Visual Communication

Research on Post-Processing System of Computer 3D Animation ...

引用

2023 ieee international conference on Sensors, Electronics and computer Engineering, ICSECE 2023

作者： Chen, Zimo Hubei University of Technology Wuhan China

ISBN: (纸本)9798350313734

Aiming at the visual expression effect of 3D animation, a set of optimization methods using computer graphics technology was proposed. The depth camera and image processor suitable for video transmission were selected and set into the original system hardware. 3D moving images were used in the experiment. The depth determination model is established by analyzing a series of images of keyframes. The depth modeling of animated image is improved by using the duality principle. Combining with the principle of visual communication, the steps of animation design are simplified and three-dimensional coordinates of animation are calculated to realize the practical design of animation. The practice shows that this method has high accuracy and can effectively improve the effect of 3D animation production. © 2023 ieee.

关键词： Visual communication

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Optimized NLP Models for Digital Twins in Metaverse 47

Optimized NLP Models for Digital Twins in Metaverse

引用

47th ieee-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Lukaj, Valeria Catalfamo, Alessio Fazio, Maria Celest, Antonio, V Vinare, Massimo Univ Messina Messina Italy

ISBN: (纸本)9798350326970

Digital Twins (DTs) in Metaverse face many challenges such as the lack of optimized AI models to allow the interaction between the user and the virtual environment. In this paper, we propose an optimized model for human language processing based on Convolutional Neural Networks (CCNs) and we present an input processing strategy to meet the realtime requirements of smart applications that integrate DTs oriented to speech-based functionalities for user interaction and Metaverse. In our solution, CNNs are applied for the processing and classification of the human voice, while structured data and MFCC coefficients are used to train the neural networks and generate interference in the models. Similarly, the MFCC algorithm is provided to extract the unique characteristics that specify each generated audio file and to reduce the complexity of the neural network model in order to obtain better performance. Starting from an approach to the problem available in the literature, we have optimized a specific CNN model for Natural Language processing (NLP) in order to increase effective results. The proposed model has demonstrated excellent performance and can be used as a basis for the implementation of software that allows the interaction of DTs with voice commands issued by a user.

关键词： Digital Twins Metaverse Virtual Reality Natural Language processing Automated Speech Recognition Convolutional Neural Networks MFCCs

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Integrating image processing Techniques with Deep Learning Network Models to Detect and Identify Defects on the Surface of Tomato

Integrating Image Processing Techniques with Deep Learning N...

引用

2023 ieee international conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Do Van, Dinh Sao Do University Hai Duong03500 Viet Nam

ISBN: (纸本)9798350327816

Before export, fruit should be classified to improve quality, meet customer requirements and increase product value. This article proposes a method to identify defects on the surface of tomato skin using image processing techniques combined with Deep learning models. The identification method includes the following main steps: (i) data collection (image of tomato: green, ripe, diseased, scratched), (ii) image labeling, (iii) data file division, (iv) model training, (v) selection and using models. The results of using Faster R-CNN model combining Resnet-10l and testing on YOLOv5 to identify and classify tomatoes that met and failed export for high accuracy (95.3 %) and met get real time. © 2023 ieee.

关键词： Deep convolutional neural network deep learning Faster R-CNN tomato export classification YOLO YOLOv5

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Stochastic Optimization of Vector Quantization Methods in Application to Speech and image processing 48

Stochastic Optimization of Vector Quantization Methods in Ap...

引用

48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Vali, Mohammad Hassan Backstrom, Tom Aalto University Department of Signal Processing and Acoustics Finland

ISBN: (纸本)9781728163277

Vector quantization (VQ) methods have been used in a wide range of applications for speech, image, and video data. While classic VQ methods often use expectation maximization, in this paper, we investigate the use of stochastic optimization employing our recently proposed noise substitution in vector quantization technique. We consider three variants of VQ including additive VQ, residual VQ, and product VQ, and evaluate their quality, complexity and bitrate in speech coding, image compression, approximate nearest neighbor search, and a selection of toy examples. Our experimental results demonstrate the trade-offs in accuracy, complexity, and bitrate such that using our open source implementations and complexity calculator, the best vector quantization method can be chosen for a particular problem. © 2023 ieee.

关键词： Vector quantization

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Exploration of Deep Methods for Cross-Domain Aerial image Matching 12

Exploration of Deep Methods for Cross-Domain Aerial Image Ma...

引用

12th international conference on image processing Theory, Tools and applications, IPTA 2023

作者： Shahid, Md. Vk, Prajwal Haq, Inzamam-Ul Faisal, Md. Bangalore India Drdo Ade Bangalore India

ISBN: (纸本)9798350325416

image matching is an attractive area for researchers. This field has been propelled recently due to the advancement of imaging devices with multi-spectral capabilities, compute power, and the evolution of the deep learning era. Cross-domain or cross-platform matching remains challenging due to the multiple type modalities associated and the need for relevant datasets. Learning or training in one domain (e.g., spectral band) and matching across domains are the real challenges for the imaging community today. The wide availability of Google Earth imagery, the development of cross-domain matching algorithms, and high-speed devices encourage addressing this issue. This paper explored deep learning methods for a cross-domain image-matching perspective. We have exploited latent and feature space, particularly for standard deep methods with several preprocessing steps, optimizer settings, and post-processing steps. We evaluated performance over standard cross-platform dataset using metrics available in the literature. © 2023 ieee.

关键词： Antennas

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Prompt Engineering in Medical image Segmentation: An Overview of the Paradigm Shift

Prompt Engineering in Medical Image Segmentation: An Overvie...

引用

2023 ieee international conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Ali, Hazrat Bulbul, Mohammad Farhad Shah, Zubair Hamad Bin Khalifa University Qatar Foundation College of Science and Engineering Doha Qatar Department of Computer Science and Engineering Pohang37673 Korea Republic of

ISBN: (纸本)9798350322347

Foundation AI models have emerged as powerful pre-trained models on a large scale, capable of seamlessly handling diverse tasks across multiple domains with minimal or no fine-tuning. These models, exemplified by the impressive achievements of GPT-3 and BERT in natural language processing (NLP), as well as CLIP and DALL-E in computer vision, have garnered considerable attention for their exceptional performance. A noteworthy addition to the realm of image segmentation is the Segment Anything Model (SAM), a foundation AI model that revolutionizes image segmentation. With a single click or a natural language prompt, SAM exhibits the remarkable ability to segment any object within an image, marking a significant paradigm shift in medical image segmentation. Unlike conventional approaches that rely on labeled data and domain-specific knowledge, SAM breaks free from these constraints. Deep convolutional neural network (DCNN)-based, SAM comprises an image encoder, a prompt encoder, and a mask decoder, showcasing its efficient and flexible architecture. Medical image segmentation, in particular, benefits from SAM's exceptional speed and high-quality segmentation. In this paper, we delve into the effectiveness of SAM for medical image segmentation shedding light on its capabilities. Moreover, our investigation explores the strengths and limitations of prompt engineering in medical computer vision applications, not only encompassing SAM but also other foundation AI models. Through this exploration, we unravel their immense potential in catalyzing a paradigm shift in the field of medical imaging. © 2023 ieee.

关键词： Medical imaging

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Research on Detection of Moldy Corn Kernels Based on Machine Vision 7

Research on Detection of Moldy Corn Kernels Based on Machine...

引用

7th international conference on Electrical, Mechanical and computer Engineering, ICEMCE 2023

作者： Chen, Yuanqi Zhang, Ning Liu, Ziyang Ye, Xinwei Xijing University Xi'an China

ISBN: (纸本)9798350382877

Corn plays an important role in many fields, but the level of intelligent detection for moldy corn is low. This article proposes a method for identifying moldy corn kernels based on machine vision. First, the image is processed, and then the MSR method with color restoration in the Retinex image enhancement algorithm is used to enhance the image. The threshold is set in the HSV space, and the moldy corn kernels are segmented through morphological processing. The two processing results are combined through logical AND operation to obtain the result. The final detection result shows that the probability of effectively recognizing moldy corn kernels can reach around 95%. This proves that the algorithm proposed in the article can efficiently and quickly detect moldy individuals in corn kernels, providing technical support and theoretical basis for the future development of intelligent detection of moldy individuals in corn. © 2023 ieee.

关键词： image segmentation

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Multi-scale Feature Modification Network for FewShot image Classification 2

Multi-scale Feature Modification Network for FewShot Image C...

引用

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

作者： Chen, Yuxiang Lu, Zongqing Tsinghua University Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9798350331417

Few-shot image classification aims to learn a model that can adopt to unseen classes with few labeled data. This challenging problem requires to overcome the distribution shift of features due to differences between the novel classes and the base classes. Inspired by transformer style self-attention mechanism and feature pyramid, we propose a multi-scale cross-attention module. This module enables the correction of features by interacting with feature information at different scales and obtains the feature set of the sample. In addition, from the perspective of metric learning, we further propose a distance metric module to measure the distance between sets of features, thus better characterizing the similarity between samples. Extensive experiments on two public benchmarks in both 1-shot and 5-shot setting demonstrate the effectiveness of our method, which is a significant performance improvement over most methods. © 2023 ieee.

关键词： cross-attention Few-shot image classification metric learning multi-sacle

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

引用

2023 ieee international conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vasantha Kumari, N. Garimella, Mounika Bhan, Madhu Ramaiah Institute of Technology Dept. of Mca Bengaluru India Ramaiah Institute of Technology Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350300826

Due to the rapid rise in the identification of digital materials, automatic image classification has emerged as the most difficult topic of computer vision. In comparison to human vision, automatic visual understanding and analysis by a system is challenging. The findings of many experiments that were done to fix problems of automatic picture classification with the current categorization method were simple picture primitives. However, those methods don't accurately classify photos. In this research work, a system has been created that makes use of deep learning to deliver the necessary results in the area of computer vision. Our technology automatically categorizes photographs using Convolutional Neural Network, a popular machine learning method. Our algorithm classifies grayscale photos using the Digit of MNIST dataset as a benchmark. The dataset that is employed for the training of the system contains grayscale images, which demand higher processing resources for image categorization. By employing Convolution Neural Network to train the photos, we were able to attain higher accuracy results in the experimental section, demonstrating that our model is highly accurate at classifying images. © 2023 ieee.

关键词： image classification

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Decontamination Transformer For Blind image Inpainting 48

Decontamination Transformer For Blind Image Inpainting

引用

48th ieee international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Chun-Yi Lin, Yen-Yu Chiu, Wei-Chen National Chiao Tung University Department of Computer Science Taiwan

ISBN: (纸本)9781728163277

Blind image inpainting aims at recovering the content from a corrupted image in which the mask indicating the corrupted regions is not available in inference time. Inspired that most existing methods for inpainting suffer from complex contamination, we propose a model that explicitly predicts the realvalued alpha mask and contaminant to eliminate the contamination from the corrupted image, thus improving the inpainting performance. To enhance the overall semantic consistency, the attention mechanism of transformers is exploited and integrated into our inpainting network. We conduct extensive experiments to verify our method against blind and non-blind inpainting models and demonstrate its effectiveness and generalizability to different sources of contaminant. © 2023 ieee.

关键词： Blind image inpainting Transformer

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