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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451-460 订阅

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Research on Automatic Workpiece Sorting System Based on Machine Vision 3

Research on Automatic Workpiece Sorting System Based on Mach...

引用

2023 ieee 3rd international conference on Data Science and computer Application, ICDSCA 2023

作者： Ji, Linfeng Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machine learning method. image preprocessing, threshold segmentation, edge and contour extraction are used to analyze the features of the image of the inspected part to judge whether the inspected part is qualified or not. Aiming at the attitude error generated when the workpiece is placed, a method for positioning the center of gravity and rotation angle of the workpiece based on boundary detection and contour tracking is proposed. Use the similarity measure to determine the characteristics of the object to be processed. Simulation results show that the method proposed in this paper is effective. © 2023 ieee.

关键词： image processing Machine vision pattern recognition workpiece

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Defect Detection of Wood Grain images with image Gradient Applied to AE-based generation 6

Defect Detection of Wood Grain Images with Image Gradient Ap...

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6th ieee international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Si, Jongwook Kim, Sungyoung Kumoh National Institute of Technology Department of Computer Ai Convergence Engineering Gumi Korea Republic of Kumoh National Institute of Technology Department of Computer Engineering Gumi Korea Republic of

ISBN: (纸本)9798350323535

Detecting and managing various types of defects that occur in the manufacturing process is important for product quality control. Detecting flaws in product presentation is an ongoing research topic in computer vision. Recently, deep learning technology has been used to improve defect detection performance compared to traditional computer vision methods. In this study, we proposed a new method for detecting defects on the surface of wooden boards using image reconstruction with an autoencoder-based model and image gradient filters in image processing using edge detection. Although the reconstruction performance of the autoencoder is significantly inferior to the newest technologies, this characteristic is used to remove noise from input images. We used the publicly available anomaly detection dataset, MV-Tec AD, for training and evaluation and presented experimental results focusing on the "Wood"category. The proposed method in this study demonstrated better detection performance with image processing. © 2023 ieee.

关键词： image reconstruction

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De-identifying Face image Datasets While Retaining Facial Expressions

De-identifying Face Image Datasets While Retaining Facial Ex...

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ieee international Joint conference on Biometrics (IJCB)

作者： Leibl, Andreas Meissner, Andreas Altmann, Stefan Attenberger, Andreas Mayer, Helmut Univ Bundeswehr Neubiberg Germany Zentrale Stelle Informationstechn Sicherheitsbere Munich Germany

ISBN: (纸本)9798350337266

Progress in computer vision, particularly based on machine learning, depends heavily on the availability of appropriate datasets. However, for applications like face recognition or emotion detection, this requires the collection offace images, which comprise especially privacy-sensitive biometric data. The corresponding valid ethical concerns and legal regulations regarding privacy rights limit the creation of new datasets. To reconcile the need for detailed facial image datasets with the right to privacy protection, appropriate anonymization techniques are needed. To this end, we suggest a pipeline to repurpose a face swapping tool for de-identification by combining it with synthetic image generation and a novel procedure to select source images to improve the trade-off between data utility retention and privacy enhancement. A quantitative comparison of our results to other de-identification approaches shows that our method leads to better retention of facial expressions while providing adequate privacy protection. Thus, applying this procedure to face image datasets before publication could help mitigate privacy concerns.

关键词： Face recognition

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Learn How to Prune Pixels for Multi-View Neural image-Based Synthesis

Learn How to Prune Pixels for Multi-View Neural Image-Based ...

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2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Milovanović, Marta Tartaglione, Enzo Cagnazzo, Marco Henry, Felix LTCI Télécom Paris Institut Polytechnique de Paris France DEI University of Padua Italy Orange Labs France

ISBN: (纸本)9798350313154

image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown good performance in terms of objective and subjective quality, the research community devotes great effort to their improvement. However, the large volume of data necessary to render at the receiver's side hinders applications in limited bandwidth environments or prevents their employment in real-time applications. We present LeHoPP, a method for input pixel pruning, where we examine the importance of each input pixel concerning the rendered view, and we avoid the use of irrelevant pixels. Even without retraining the image-based rendering network, our approach shows a good tradeoff between synthesis quality and pixel rate. When tested in the general neural rendering framework, compared to other pruning baselines, LeHoPP gains between 0.9 dB and 3.6 dB on average. © 2023 ieee.

关键词： Pixels

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Disparity Estimation with Deep Feature Parallel Transformation 2

Disparity Estimation with Deep Feature Parallel Transformati...

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

作者： Yue, Xin Lu, Zongqing Tsinghua Shenzhen International Graduate School Division of Information Science and Technology Shenzhen China

ISBN: (纸本)9798350331417

We propose a feature parallel transformation module to strength the matching ability in stereo. We obtain additional information from input images, enhance features from the feature extraction module and design a parallel cascading module to fuse the features, which have rich global information. In addition, our feature transformation modules can be applied to any existing stereo networks in a plug and play manner. Our simple yet effect module can perform outstandingly on stereo benchmarks. © 2023 ieee.

关键词： component Deep learning,Feature Transformation Parallel propagation Stereo matching

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image Labeling Using Convolutional Neural Network

Image Labeling Using Convolutional Neural Network

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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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Fusing IP vendor Palmprint Biometric with Encoded Hash for Hardware IP Core Protection of image processing Filters

Fusing IP vendor Palmprint Biometric with Encoded Hash for H...

引用

2023 international conference on Microelectronics, ICM 2023

作者： Sengupta, Anirban Anshul, Aditya Thakur, Sumer Kothari, Chirag Indian Institute of Technology Indore Indian Statistical Institute Kolkata Computer Science and Engg. India Indian Institute of Technology Indore Computer Science and Engg. India Indian Institute of Technology Indore India

ISBN: (纸本)9798350380828

image processing filters offer several significant applications, making them a crucial component of various consumer electronics and multimedia systems. These image filters are designed as dedicated reusable intellectual property (IP) cores using high level synthesis (HLS) framework. These image processing hardware IPs are essential components of several system-on-chips (SoCs) used in mission-critical applications. Therefore, protecting these image filter hardware IPs from an IP vendor's perspective is crucial against an adversary's false IP ownership claim in the context of globalized design supply chain. This paper presents a novel hardware IP protection (IPP) technique by fusing the IP vendor's palmprint biometric and his/her encoded hash to generate secret security constraints for embedding. This embedded security mark acts as a detective countermeasure during the IP conflict resolution process. The proposed approach outperforms recent IPP techniques, as evident by a lower probability of coincidence, stronger tamper tolerance, and stronger entropy value. © 2023 ieee.

关键词： Anthropometry

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HumanSD: A Native Skeleton-Guided Diffusion Model for Human image Generation

HumanSD: A Native Skeleton-Guided Diffusion Model for Human ...

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ieee/CVF international conference on computer Vision (ICCV)

作者： Ju, Xuan Zeng, Ailing Zhao, Chenchen Wang, Jianan Zhang, Lei Xu, Qiang Chinese Univ Hong Kong Hong Kong Peoples R China Int Digital Econ Acad Hong Kong Peoples R China

ISBN: (纸本)9798350307184

Controllable human image generation (HIG) has numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable branch on top of the frozen pre-trained stable diffusion (SD) model, which can enforce various conditions, including skeleton guidance of HIG. While such a plugand-play approach is appealing, the inevitable and uncertain conflicts between the original images produced from the frozen SD branch and the given condition incur significant challenges for the learnable branch, which essentially conducts image feature editing for condition enforcement. In this work, we propose a native skeleton-guided diffusion model for controllable HIG called HumanSD. Instead of performing image editing with dual-branch diffusion, we fine-tune the original SD model using a novel heatmap-guided denoising loss. This strategy effectively and efficiently strengthens the given skeleton condition during model training while mitigating the catastrophic forgetting effects. HumanSD is fine-tuned on the assembly of three large-scale human-centric datasets with textimage-pose information, two of which are established in this work. Experimental results show that HumanSD outperforms ControlNet in terms of pose control and image quality, particularly when the given skeleton guidance is sophisticated.

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Research on Intelligent Graphic image Model Based on computer Digital Media Creation Technology

Research on Intelligent Graphic Image Model Based on Compute...

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

作者： Lin, Huiyu Guangzhou Huashang Vocational College School of Information Engineering Guangdong Guangzhou China

ISBN: (纸本)9798350313734

In this paper, the nonlinear filtering algorithm for mixed noise is proposed based on the probabilistic statistical model of local texture direction of computer digital media images. The Radon transform is used to analyze the main texture direction of the image, and the probability density distribution of local texture direction is obtained. A probabilistic assumption density filtering algorithm based on untrace transformation is proposed in the nonlinear Gaussian condition to achieve multi-target tracking in the strong clutter environment. This algorithm makes full use of the local features of the image and has good denoising ability. The algorithm and the median filtering algorithm with the best filtering performance are used in the image filtering experiment. It is found that the algorithm is better than other filtering algorithms in processing mixed noise images infected with both pulse noise and Gaussian noise. © 2023 ieee.

关键词： Digital storage

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image to Text: Comprehensive Review on Deep Learning Based Unsupervised image Captioning 2

Image to Text: Comprehensive Review on Deep Learning Based U...

引用

2nd ieee international conference on Futuristic Technologies, INCOFT 2023

作者： Shetty, Aishwarya D Shetty, Jyothi Department of Cse Nitte India Visvesveraya Technological University Belagavi590018 India

ISBN: (纸本)9798350308846

The task of image captioning has seen considerable success using deep neural networks. This assessment offers a thorough overview of the most cutting-edge approaches for deep learning-based unsupervised image captioning. In order to bridge the gap between computer vision and natural language processing, the effort of creating meaningful textual captions for images is known as image captioning. Without using annotated image-caption pairings for training, unsupervised approaches try to provide captions. The review examines a variety of techniques, including generative adversarial networks (GANs), pre-training, transfer learning, and convolutional neural networks (CNNs) for extracting visual features, as well as recurrent neural networks (RNNs) or transformer models for language modelling. Researchers and practitioners can benefit from this survey's observations, classification of methodologies, and recommendations for the future of deep learning-based unsupervised image captioning. © 2023 ieee.

关键词： computer Vision deep learning Natural Language processing Unsupervised image captioning

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