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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16443 条记录，以下是1181-1190 订阅

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An Efficient Workload Distribution Mechanism for Tightly Coupled Heterogeneous Hardware 6th

An Efficient Workload Distribution Mechanism for Tightly Co...

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6th World conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Rivera-Alvarado, Ernesto Torres-Rojas, Francisco J. Computer Science Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (纸本)9789811976599

The Accelerated processing Units (APUs) are tightly integrated heterogeneous computing platforms that contain a CPU and GPU in a single integrated circuit. As their internal computing resources use the same memory (RAM), they can efficiently share data structures. This opens up the opportunity for creating new types of specialized algorithms for the APU. In this research, we designed a specialized load distribution mechanism for the APU and used image rendering as our test load as it is a highly expensive computational task. We found out that our workload distribution mechanism provides better performance than conventional workload mechanisms for GPUs and multicore CPUs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： APU Computer architecture Heterogeneous computing image rendering SoC System-on-chip Tightly coupled heterogeneous hardware Workload distribution

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Real-time image Enhancement for Emergency Rescue Scenarios in Smart Grids

Real-time Image Enhancement for Emergency Rescue Scenarios i...

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Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Zeng Dou Jingyi Tao Ning Lv Li Cong Chengbin Huang Tao Su State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China School of Electronic Engineering Xidian Hangzhou Institute of Technology Xidian University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

image enhancement technology plays an important role in the practical application of detecting underwater tunnels. By improving image quality, it enhances the accuracy and reliability of detection results. However, in deep-sea scenes, the problem of image color distortion and detail loss is common. To address this issue, a real-time underwater image enhancement algorithm based on weighted multi-branch correction has been proposed in this paper. The algorithm uses a combination of multi-scale and multi-branch techniques to process the image. The multi-branch correction module has been improved by increasing the weighting and downsampling strategy, which not only improves the processing speed but also ensures the processing effect. In the air-frequency domain interactive processing module, zero padding and window functions have been introduced to reduce the ringing effect and block effect. In addition, the smart grid system provides stable power support for underwater tunnel search and rescue, which ensures the safety of the search and rescue environment and also provides a good experimental lighting environment for the image enhancement algorithm. At the same time, the model has been trained and validated using self-constructed real deep-sea datasets. The existing algorithms and the algorithm proposed in this paper have been compared and analyzed comprehensively. The experimental results show that the algorithm proposed in this paper is better than the existing algorithms in terms of processing effect, and the processing speed is improved by about 12.7%.

关键词： image quality image color analysis Lighting Information filters Real-time systems Smart grids Safety

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Joint MLP and Pseudo-label Unsupervised Hash for image Retrieval 5

Joint MLP and Pseudo-label Unsupervised Hash for Image Retri...

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5th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Chao, Lu Liu, Ying Yu, Hao Yang, Jianning Li, Daxiang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

The increasing growth of image data has made the implementation of image retrieval in big data environment a hot research topic. Traditional hashing technique uses hand-designed features for learning, which can't obtain high-level semantic features. Deep hashing has become a powerful tool for solving multimedia data retrieval in recent years with the powerful feature learning capability and the inherent advantage of small hash code storage/fast retrieval. Most of these algorithms rely on manual tagging, which is very expensive. In this paper, we propose the Joint MLP and Pseudo-label unsupervised hash (JMPH). In this approach, object features in the image are extracted with the target detection method as pseudo-labels to train the deep hash. In particular, we additionally devise the MLP network to conduct the encoding process. Experimental results on two datasets demonstrate that our unsupervised hashing method outperforms the state-of-art unsupervised hashing methods. © 2022 ACM.

关键词： image retrieval

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Automatic Classification and Recognition of Tourist Attraction images using k-Means Algorithm and image processing Technology

Automatic Classification and Recognition of Tourist Attracti...

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Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Qian Jiang School of Public Administration Chongqing Vocational College of Transportation China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study investigates the automatic classification and recognition of tourist attraction images through an enhanced K-means algorithm paired with image processing techniques. Given the growing interest in efficient tourist image management and promotion, our research presents a system that significantly advances the processing and categorization of scenic spot images. We implemented a variety of image preprocessing steps—such as grayscale conversion, normalization, Gaussian filtering, and edge detection — to improve the accuracy of feature extraction. Our method involved an optimized K-means algorithm, modified to select initial centroids more effectively and adjust cluster numbers dynamically. Experimental results, based on a dataset of 150 images, demonstrated a classification accuracy of 92%, with the system maintaining robust performance even as dataset sizes increased, confirming the algorithm's efficiency and stability. The processing time for 5000 images was recorded at 12.3 seconds, showcasing a linear relationship between data size and processing time. These findings highlight the potential of our proposed system in enhancing tourism management and marketing strategies by providing a reliable and scalable image classification solution.

关键词： Accuracy Filtering image processing image edge detection Heuristic algorithms Clustering algorithms Feature extraction Classification algorithms image preprocessing Reliability

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit 14

A Load Balancing Strategy Of "Container Virtual Machine" Clo...

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14th International conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： xia, Hong Liu, MengDi Chen, YanPing Jin, xiaoMin Wang, ZhongMin Wang, FengWei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-tech Road South Shenzhen518057 China

ISBN: (纸本)9781665499781

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine;Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost. © 2022 IEEE

关键词： Containers

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Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks 19

Comparison of algorithms for contrast enhancement based on T...

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conference on Electro-Optical and Infrared systems - Technology and Applications xiX

作者： Wegner, Daniel Kessler, Stefan Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510655461

A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for constrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the constrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

关键词： CNN TOD image contrast enhancement advanced digital signal processing imager assessment method comparison

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Synthetic image Generation With a Fine-Tuned Latent Diffusion Model for Organ on Chip Cell image Classification 26

Synthetic Image Generation With a Fine-Tuned Latent Diffusio...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Ivanovs, Maksims Leja, Laura Zviedris, Karlis Rimsa, Roberts Narbute, Karina Movcana, Valerija Rumnieks, Felikss Strods, Arnis Gillois, Kevin Mozolevskis, Gatis Abols, Arturs Kadikis, Roberts Institute of Electronics and Computer Science Riga Latvia Cellbox Labs Riga Latvia Latvian Biomedical Research and Study Centre Riga Latvia

ISBN: (纸本)9798350304985

Augmentation of the datasets of authentic microscopy images with synthetic images is a promising solution to the problem of the limited availability of biomedical data for training deep neural network (DNN) based classifiers. In the present study, we use a text-to-image latent stable diffusion model fine-tuned by means of low-rank adaptation (LoRA) to augment a small dataset of the images of organ on chip cells. While the resulting synthetic images appear quite similar to the authentic images on which the low-rank adaptation was performed, we find that neither training the EfficientNetB7 DNN model solely on the synthetic data nor augmentation of the real-world dataset with different proportions (10, 25, 50, and 75 percent) of these data leads to the improvement of the accuracy of the model. The findings of our study suggest that a further exploration of the low-rank adaptation options is needed to fully use the capacity of latent diffusion models for the synthesis of biomedical images. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Neural network models

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image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International conference on Computer Vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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DeTrack: In-model Latent Denoising Learning for Visual Object Tracking 38

DeTrack: In-model Latent Denoising Learning for Visual Objec...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhou, xinyu Li, Jinglun Hong, Lingyi Jiang, Kaixun Guo, Pinxue Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering and Technology Fudan University Shanghai China

Previous visual object tracking methods employ image-feature regression models or coordinate autoregression models for bounding box prediction. image-feature regression methods heavily depend on matching results and do not utilize positional prior, while the autoregressive approach can only be trained using bounding boxes available in the training set, potentially resulting in suboptimal performance during testing with unseen data. Inspired by the diffusion model, denoising learning enhances the model's robustness to unseen data. Therefore, We introduce noise to bounding boxes, generating noisy boxes for training, thus enhancing model robustness on testing data. We propose a new paradigm to formulate the visual object tracking problem as a denoising learning process. However, tracking algorithms are usually asked to run in real-time, directly applying the diffusion model to object tracking would severely impair tracking speed. Therefore, we decompose the denoising learning process into every denoising block within a model, not by running the model multiple times, and thus we summarize the proposed paradigm as an in-model latent denoising learning process. Specifically, we propose a denoising Vision Transformer (ViT), which is composed of multiple denoising blocks. In the denoising block, template and search embeddings are projected into every denoising block as conditions. A denoising block is responsible for removing the noise in a predicted bounding box, and multiple stacked denoising blocks cooperate to accomplish the whole denoising process. Subsequently, we utilize image features and trajectory information to refine the denoised bounding box. Besides, we also utilize trajectory memory and visual memory to improve tracking stability. Experimental results validate the effectiveness of our approach, achieving competitive performance on several challenging datasets. The proposed in-model latent denoising tracker achieve real-time speed, rendering denoising learning

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