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AFMA-Track: Adaptive Fusion of Motion and Appearance for Robust Multi-object Tracking 27th

AFMA-Track: Adaptive Fusion of Motion and Appearance for ...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Liao, Wei Luo, Lei Zhang, Chunyuan College of Computer Science and Technology National University of Defence Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9783031784439

Motion and appearance cues play a crucial role in Multi-object Tracking (MOT) algorithms for associating objects across consecutive frames. While most MOT methods prioritize accurate motion modeling and distinctive appearance representations, the use of appearance and motion cues is often confined to simplistic association techniques. For instance, fixed weights are commonly employed to combine the intersection-over-union (IoU) matrix and appearance similarity matrix, yielding an association cost matrix. To harness the full potential of motion and appearance cues across diverse scenarios, we propose an innovative approach that dynamically balances motion and appearance cues based on scene and object information during the association process. Furthermore, we introduce a new mechanism for updating appearance representations, effectively mitigating noise introduced by occlusion. Our method demonstrates state-of-the-art performance on the MOT17 and MOT20 test sets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object detection

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High-Resolution image Generation Using Artificial Intelligence and Diffusion Modelling

High-Resolution Image Generation Using Artificial Intelligen...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Amanda Scoles Giulia Sionis Beatriz Otero Gladys Utrera Escola Tecnica Superior d'Enginyeria de Telecomunicacio de Barcelona Universitat Politecnica de Catalunya BarcelonaTECH Barcelona Spain Computer Architecture Department Universitat Politecnica de Catalunya. BarcelonaTECH Barcelona Spain

Super-Resolution via Repeated Refinement (SR3) is a state-of-the-art super-resolution algorithm based on diffusion model that can enhance the resolution of images. This method is used to pre-trained models on large datasets and can be used for various tasks without requiring training from scratch. Training SR3 from scratch using the imageNet dataset involves a complex process that requires substantial computational resources and expertise. The idea is applied the trained SR3 model to new images by feeding the low-resolution inputs and obtaining the high-resolution outputs. It's important to note that training SR3 from scratch is a resource-intensive process that requires powerful GPUs and significant computation time. If you do not have access to such resources, an alternative is to use pre-trained models that are already available and fine-tune them on specific datasets or tasks. The paper shows the result of comparing the resolution of the preprocessed images using a significantly smaller number of images to perform the training with those obtained using the pre-trained model. The results obtained show acceptable results without having to perform on large datasets minimizing the computation time to obtain the resolution of images.

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Blocks of Cell-Hierarchical Identification and Optimization of Complex Spatially distributed Production Systems 5

Blocks of Cell-Hierarchical Identification and Optimization ...

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5th International conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Korneev, Andrey Lavrukhina, Tamara Smetannikova, Tatiana Pantyushin, Mikhail Lipetsk State Technical University Institute of Mechanical Engineering and Transport Lipetsk Russia Lipetsk State Technical University Faculty of Automation and Informatics Lipetsk Russia

ISBN: (纸本)9798350309126

Complexly structured production systems can be represented in the form of a cellular hierarchical structure. Individual stages of processing create certain impacts on the quality indicators of the finished product. The task of creating new methods for search optimization of complex processes and developing control algorithms is due to the complexity of finding exact dependencies between technological quantities and properties regulated by standards. Identification of multi-stage production processes can be carried out in the form of parallel and sequential flows. Since fulfilling the requirements of a certain trajectory does not allow obtain a single combination of output alphabets, it is necessary for each technological chain to describe its own probabilistic automaton. A separate processing stage can be described as a cell without memory and is a lower-level automaton. Each of the aggregates, in turn, is described in the form of a higher-level automaton (aggregate automaton), which includes a chain of cells formed by operation automata. This chain forms a linear iterative chain. The problem of discrete optimization comes down to determining such a technological subset for which the objective function (a criterion for assessing the effectiveness of the operating modes of complex systems) takes on the maximum value. The design of processing modes for industrial systems, taking into account many quality criteria, has the nature of a heuristic iterative process. © 2023 IEEE.

关键词： Optimization

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Enhance image-to-image Generation with LLaVA-generated Prompts

Enhance Image-to-Image Generation with LLaVA-generated Promp...

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Information Science, parallel and distributed Systems (ISPDS), International conference on

作者： Zhicheng Ding Panfeng Li Qikai Yang Siyang Li Fu Foundation School of Engineering and Applied Science Columbia University New York USA Department of Electrical and Computer Engineering University of Michigan Ann Arbor USA Department of Computer Science University of Illinois Urbana-Champaign Urbana USA Lubin School of Business Pace University New York USA

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a framework where LLaVA analyzes input images and generates textual descriptions, hereinafter LLaVA-generated prompts. These prompts, along with the original image, are fed into the image-to-image generation pipeline. This enriched representation guides the generation process towards outputs that exhibit a stronger resemblance to the input image. Extensive experiments demonstrate the effectiveness of LLaVA-generated prompts in promoting image similarity. We observe a significant improvement in the visual coherence between the generated and input images compared to traditional methods. Future work will explore fine-tuning LLaVA prompts for increased control over the creative process. By providing more specific details within the prompts, we aim to achieve a delicate balance between faithfulness to the original image and artistic expression in the generated outputs.

关键词： Visualization Information science Pipelines Process control Coherence Creativity

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Relevance-Based Multi-User Data Compression for Fronthaul Rate Reduction in Cell-Free Massive MIMO Systems 14

Relevance-Based Multi-User Data Compression for Fronthaul Ra...

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14th International ITG conference on Systems, Communications and Coding, SCC 2025

作者： Danaee, Alireza Hassanpour, Shayan Wubben, Dirk Dekorsy, Armin University of Bremen Department of Communications Engineering Germany

ISBN: (纸本)9798331522896

In the Cell-Free massive Multiple-Input MultipleOutput (CF-mMIMO) systems, a large number of distributed users are simultaneously served by multiple Radio Access Points (RAPs). In the uplink, each RAP receives noisy observations from several users and must locally compress these signals before forwarding them to the corresponding Central processing Unit (CPU) via multiple fronthaul channels, each subject to a rate limitation. The challenge is to design the compressed signals at the RAPs such that the received signals at the CPU retain as much information as possible about the users (to be retrieved). To address this, we adopt compression techniques based on the Information Bottleneck (IB) principle to design local quantizers at the RAPs by ensuring an efficient balance between the informativity and compactness of the compressed signals. We discuss here two different compression schemes: one that processes the signals independently across fronthaul links and another that leverages the side information from previously retrieved signals at the CPU. By using side information, the latter generally provides a better tradeoff between the compression efficiency and performance, albeit with an increased complexity. Through numerical simulations, we demonstrate the effectiveness of both IB-based schemes compared to the conventional compression methods, showing their potential for improving fronthaul rate efficiency and overall system performance in typical digital transmission scenarios. © 2025 IEEE.

关键词： parallel processing systems

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A Smart System for Binary image Denoising using DCNN on FPGA 6

A Smart System for Binary Image Denoising using DCNN on FPGA

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6th International conference on Inventive Computation Technologies, ICICT 2023

作者： Vignesh, O. Sivarajakrishnan, P. Sneha, V.S. Department of ECE Easwari Engineering College Chennai India

ISBN: (纸本)9798350398496

Integrated circuits based on digital image processing have been increasingly important in recent years due to their usefulness in many different fields, including manufacturing, agriculture, remote sensing, and medicine. The development of low-area, low-power, and low-delay processors is a significant challenge. The majority of these efforts have targeted efficient noise removal filtering by minimizing the resources required to accomplish it. Existing filtering approaches require a lot of space, a lot of power, and a lot of time since noise is dynamic and distributed randomly throughout images. Finally, this research work is extended by incorporating QCA technology to reduce the space, power, and time needed to build an efficient filtering method with altered noise deviation features. This research study proposes a majority logic gates technology to create DCNN-based FBF on a field-programmable gate array (FPGA). By using the vast majority of logic gates to design the MCSA, considerable gains can be achieved in terms of speed, execution time, area, power, and latency over the RCA used in traditional CNN. The proposed DCNN-FBF is also used in the implementation of an image denoising application. The proposed methodology has been shown to improve hardware performance relative to state-of-the-art methodologies, as evidenced by both hardware and extensive simulation findings. In addition, as compared to traditional filtering methods, the proposed image denoising solution has achieved higher values of structural similarity (SSIM) and PSNR. © 2023 IEEE.

关键词： Logic gates

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MonkeyGPT: Generative AI in Network Anomaly Detection of Video conference Applications

MonkeyGPT: Generative AI in Network Anomaly Detection of Vid...

引用

International Symposium on parallel and distributed processing with Applications, ISPA

作者： Junhao Xu Dongbiao He Chen Zhang Xu Zhou Zhongxing Ming Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Sangfor Technologies Inc Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI’s capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability.

关键词： Accuracy Generative AI Large language models Telecommunication traffic Streaming media Real-time systems Data models Anomaly detection Videoconferences Text processing

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parallel Coregistration Algorithm For Sar images Based On Hadoop

Parallel Coregistration Algorithm For Sar Images Based On Ha...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Li Guobing Zeng Huaping Xu School of Electronic and Information Engineering Beihang University Beijing China

As the availability of SAR images continues to grow, efficient coregistration of massive SAR images presents a greater challenge. Traditional serial coregistration methods impose an unbearable time overhead. To reduce this overhead and make full use of computing resources, a parallel coregistration strategy based on Hadoop is proposed for SAR images. The Hadoop distributed File System (HDFS) is used to store SAR image data in chunks, and Hadoop's distributed computing strategy MapReduce is used to realize distributed parallel processing of SAR images. Two distributed parallel coregistration methods are presented with the proposed parallel strategy: one based on the maximum correlation method and the other on the DEM-assisted coregistration method. These methods are evaluated through coregistration experiments on the same dataset, and they are verified by comparing the coregistration results and processing time.

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Human Tissue Cell image Segmentation optimization Algorithm Based on Improved U-net Network 6

Human Tissue Cell Image Segmentation optimization Algorithm ...

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6th International conference on Information and Computer Technologies, ICICT 2023

作者： Ying, Jie Jing, Xin Qin, Chenyang Huang, Wei University of Shanghai for Science and Technology School of Optical Electrical and Computer Engineering Shanghai200093 China

ISBN: (纸本)9798350300956

In medical testing and diagnosis, cells as the basic structure of human body, have received extensive attention in pathological research. The detection and segmentation of cells or nuclei play an important role in describing molecular morphological information. The accuracy of human tissue cells segmentation still needs to be improved, and the network segmentation results have boundary blurring and noise pixels interference. In this paper, an improved U-net network model is proposed. Aiming at the problem of simple stacking of the same convolution operation, a parallel structure for multi-scale image feature extraction is designed. Through the setting of multiple convolution operations and combining different feature fusion methods, a better convolution block structure is obtained. The network segmentation image is optimized through the secondary segmentation of Otsu method and morphological processing, and the final segmentation result is obtained. Finally, the cell contour and centroid are displayed on the original image, and the nuclear center was calculated by image moment. The experiments were carried out on the human organ H&E cell data set. The experimental results show that the Dice coefficient, Jaccard similarity coefficient, recall and accuracy of the improved U-net network model for H&E cell image segmentation reach 0.8260, 0.60, 0.8380 and 0.8270 respectively. Compared with U-net network, the above parameters of the improved U-net model are increased by 1.7%, 3.9%, 1.2% and 2.2% respectively. Compared with CNN network, the accuracy rate is improved by 3.5%, and cell segmentation is more accurate than other literatures. © 2023 IEEE.

关键词： Convolution

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parallel O(log(n) ) Computation of the Adjacency of Connected Components 3rd

Parallel O(log(n) ) Computation of the Adjacency of Conne...

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3rd International conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Banaeyan, Majid Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9783031092817

Connected Component Labeling (CCL) is a fundamental task in pattern recognition and image processing algorithms. It groups the pixels into regions, such that adjacent pixels have the same label while pixels belonging to distinct regions have different labels. The common linear-time raster scan CCL techniques have a complexity of O(image- size) in a 2D binary image. To speed up the procedure of the CCL, the paper proposes a new irregular graph pyramid. To construct this pyramid, we use a new formalism [1] that introduces an order of the pixels in the base grid to detect the redundant edges through the hierarchical structure. These redundant edges, unlike the usual methods of constructing the irregular pyramid, are removed before contracting the edges. This not only simplifies the construction processes but may decrease memory consumption by approximately half. To perform the CCL task efficiently the proposed parallel algorithm reduces the complexity to O(log(n) ) where the n is the diameter of the largest connected component in the image. In addition, using an efficient combinatorial structure the topological properties of the connected components including adjacency of CCs, multi-boundaries and inclusions are preserved. Finally, the mathematical proofs provide fully parallel implementations and lead to efficient results in comparison with the state-of-the-art. © 2022, Springer Nature Switzerland AG.

关键词： Pixels

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