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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79605 条记录，以下是61-70 订阅

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18th international Workshop on Design and Architecture for signal and image processing, DASIP 2025

18th International Workshop on Design and Architecture for S...

引用

18th international Workshop on Design and Architecture for signal and image processing, DASIP 2025

ISBN: (纸本)9783031878961

The proceedings contain 10 papers. The special focus in this conference is on Design and Architectures for signal and image processing. The topics include: LiFT: Lightweight, FPGA-Tailored 3D Object Detection Based on LiDAR Data;A Practical HW-Aware NAS Flow for AI Vision applications on Embedded Heterogeneous SoCs;Endoscopy image Classification for Wireless Capsules with CNNs on Microcontroller-Based Platforms;joint Underwater Depth Estimation and Dehazing from a Single image Using Attention U-Net;KD-AHOSVD: Neural Network Compression via Knowledge Distillation and Tensor Decomposition;Novel Scheduling and Shifter Networks for 5G LDPC Decoders;Comparison Between In-Core Hardware IDS, Off-Core Hardware IDS and Software IDS;comparative Study of Memory Optimization Techniques for Dataflow-Modeled applications.

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Unsupervised image-to-image Style Transfer via Dual-Condition Diffusion Models

Unsupervised Image-to-Image Style Transfer via Dual-Conditio...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Fan, Anfei Yang, Jun Li, Wei Zhang, Chiyu College of Computer Science Sichuan Normal University Chengdu China

ISBN: (纸本)9798350368741

Style transfer is an artistic research topic within a series of generative tasks, and it is quite challenging to stably and reliably guide the generation of images with the expected content and style. Early methods followed a predefined style, defined by a reference image, and applied it to another image while preserving the latter's content structure, but the generated effects lacked high fidelity. As diffusion models have evolved, the content guidance for style transfer has transitioned from images to text, potentially leading to instance alterations. Generating with multiple conditions constrained in the latent space is challenging. Within the EDM framework, we have utilized cross-attention to design style and content embedding modules, overcoming instance alterations without the need for paired datasets and achieving high-resolution, high-fidelity results. Our experimental findings demonstrate that our architecture has advanced to the forefront of image-to-image style transfer capabilities. © 2025 ieee.

关键词： diffusion models image generation image-to-image style transfer

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DiffRS: An Extensible Diffusion Model for Remote Sensing image Generation

DiffRS: An Extensible Diffusion Model for Remote Sensing Ima...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Huang, Xinyue Niu, Xin Jiang, Jingfei Pan, Hengyue National University of Defense and Technology Changsha China

ISBN: (纸本)9798350368741

Remote sensing image generation is of great value for virtual environment creation and adversarial learning for fake news detection. It could also address the learning sample shortage in the region of interest. However, most current image generation methods are limited to producing images of fixed sizes, few studies on extensible natural image generation largely focus on the stitching of random contents, lacking effective exploration of contextual information, which weakens the coherence of the extended images. To address this problem, we propose an extensible generation method for remote sensing images with the model DiffRS. This approach allows for sequential extension of arbitrary sizes by exploring the generated neighboring regions. The method is particularly suitable for scenes like remote sensing images where a generation block could cover multiple independent targets, rather than natural image tasks which may stitch across regions to form a completely target. Compared to the state-of-the-art extensible generation methods, DiffRS could improve the large scale image generation with better structure consistency, richer details and higher realism. Experiments showed that DiffRS could improve the FID score by 4.6% and 3.2% respectively in comparison with the MultiDiffusion and Mixture of Diffuser models. © 2025 ieee.

关键词： Diffusion Model image Extensible Generation Remote Sensing image Sequence Generation

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Multi-Model AI-Driven Satellite image Segmentation for Comprehensive Urbanization and Environmental Degradation Analysis in Bangladesh 6

Multi-Model AI-Driven Satellite Image Segmentation for Compr...

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6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Rezwan-Ul-Alam Dhrubo, Ahmed Faizul Haque Alam, Md. Iftekharul Iqbal, Nafiz Ruku, Raian Mahmud, Fahim Hasan, Md. Mehedi Abdul Qayum, Mohammad North South University Dept.of ECE Dhaka Bangladesh

ISBN: (纸本)9798331506520

The accelerated urbanization of Bangladesh has hence influenced the structure and systematic composition of landscapes and ecosystems, posing an intricate challenge for managers and urban planners. This study highlights a need for sophisticated monitoring with the application of AI and deep learning for intensive image processing and annotation. We built a massive dataset of satellite imagery from various parts of Bangladesh by collecting, processing, and annotating it to outline the buildings, trees, water bodies, tin shades, and farmlands. This dataset underpins the analyses of landscape temporal transformations, urban sprawl, environmental attrition, and land-grab within defined multiples of years over the preceding decade. Our AI framework incorporates four cutting-edge deep learning models and ensures unparalleled accuracy via robust cross-validation with optimized object detection capabilities. Despite the challenges posed by data annotation, our method still holds immense promise for improving artificial intelligence in satellite image analysis. For future work, we aim to develop a larger dataset, overcome the issues in annotation, and include several models to improve the ranking of our results-an area where this study brings invaluable contributions to urban environments, environmental conservation, and sustainable development. © 2025 ieee.

关键词： Environmental monitoring

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8D Hyperchaotic System and Gold Sequences for Improved Medical image Cube Encryption 6

8D Hyperchaotic System and Gold Sequences for Improved Medic...

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6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Mamdouh, Eyad Gabr, Mohamed Megalli, Youstina Alexan, Wassim German University in Cairo Physics Department Cairo Egypt German University in Cairo Faculty of MET CSEN Department Cairo Egypt German University in Cairo Faculty of IET Communications Department Cairo Egypt

ISBN: (纸本)9798331506520

This paper presents an advanced encryption algorithm specifically designed to enhance the security of volumetric medical image data, crucial for the Internet of Medical Things (IoMT). The algorithm encrypts a stack of 256 images, each with dimensions of 256×256 pixels, through a meticulous multi-stage process. It begins by segmenting an image cube into Red, Green, and Blue channels, which are then encrypted through three phases: XOR operations with keys from an 8D hyperchaotic system, substitution with S-boxes derived from Gold sequences, and a final transformation using Fibonacci Q-matrices. This approach significantly improves security by improving entropy, decreasing cross-correlation, and strengthening resistance to statistical attacks, with various cryptographic seeds used at each stage to enhance the robustness of the encryption. Specific performance evaluation metrics include a pixel cross-correlation of approximately 0, an NPCR of 99.61%, a UACI of 34.45%, an entropy of 7.998, and a key space that exceeds 22870. Extensive cryptographic assessments confirm the effectiveness of this algorithm, making it a vital tool for securing medical image data within IoMT, ensuring safe transmission and storage in healthcare systems. © 2025 ieee.

关键词： Q factor measurement

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Bridging the Machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

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6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 ieee.

关键词： Constrained optimization

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Vision Mamba-Based Approach for Incomplete Boundary Document image Rectification

Vision Mamba-Based Approach for Incomplete Boundary Document...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Weihao Xia, Xin Li, Maopeng Zhao, Yunbo HiThink RoyalFlush Information Network Co. Ltd. Hangzhou China University of Science and Technology of China Hefei China

ISBN: (纸本)9798350368741

Capturing document images using handheld mobile devices often results in geometric deformations, which adversely affect the accuracy of Optical Character Recognition (OCR) and document understanding. However, existing transformer-based methods face significant computational costs when processing document images on resource-constrained devices. This study proposes an enhanced Vision Mamba architecture to learn the structural information of document images, thereby rectifying deformed images while reducing computational resource consumption. Additionally, owing to the relative positioning between the document and the imaging device, captured images may exhibit incomplete boundaries. Conventional learning-based methods are primarily designed for images with complete boundaries, which can diminish correction effectiveness. To address this issue, mask consistency loss and preprocessing techniques are introduced to improve the rectification of document images with incomplete boundaries. Experimental results demonstrate the effectiveness and superiority of this method, highlighting its significant value for intelligent document processing. © 2025 ieee.

关键词： Document image rectification image deformation vision mamba

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A New Video Summarization Approach Using Object Density Based image Similarity for Smart City applications 29

A New Video Summarization Approach Using Object Density Base...

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29th international conference on Information Technology, IT 2025

作者： Altundogan, Turan Goktug Karakose, Mehmet Mert, Fatih Manisa Celal Bayar University Computer Engineering Department Manisa Turkey Firat University Computer Engineering Department Elazig Turkey Huawei Telecommunication Foreign Trade Co. Ltd. Istanbul Turkey

ISBN: (纸本)9798331517649

Summarization approaches are currently proposed solutions that focus on meaningfully reducing different types of data such as text, audio, and video. Many techniques such as machine learning, signal processing, image processing, computer vision, and deep learning can be used to develop summarization approaches. In this study, we performed object detection on videos that can be used in smart city applications using a pretrained YOLOv8 model. As a result of the object detection, we created a feature vector for each image frame by using the location information covered by the classes used in the object detection process. Then, we used several different approaches to determine the reference feature vector for the video. Finally, we calculated the cosine similarities of the feature vector for each frame to this reference feature vector using different methods. With the method we developed, we presented a similarity-focused summary created by selecting the video frames expressed with maximum similarity. We also developed an evaluation approach to evaluate the summaries we presented, comparing the overall heat maps of the video with the heat maps of the summary videos. Experimental results demonstrate the efficiency of our summarization approaches. © 2025 ieee.

关键词： image Similarity Object Detection Smart City Video Summarization

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Robust Multilayer Matrix Factorization for Blind Hyperspectral image Unmixing 16

Robust Multilayer Matrix Factorization for Blind Hyperspectr...

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16th international conference on signal processing Systems, ICSPS 2024

作者： Yang, Gang Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9781510689251

Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing applications. In this paper, we propose a novel robust multilayer matrix factorization (RMLMF) approach for hyperspectral image unmixing. Firstly, instead of sampling all observed pixels, the endmembers are modeled as a linear combination of candidate endmembers to reduce the redundant spectra and alleviate heavy computational cost. Secondly, to explore the hidden information, the shallow decomposition is extended into multilayer structure. Furthermore, considering that hyperspectral images (HSIs) still suffer from a variety of noises, L2,1-norm is employed to measure the reconstruction error of the multilayer structure. Experimental results on both synthetic and real data sets demonstrate the superiority of the proposed RMLMF over many state-of-the-art algorithms. © 2025 SPIE.

关键词： Matrix factorization

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Self-Supervised Learning and image-Prompt Fusion for AIGC image Quality Assessment

Self-Supervised Learning and Image-Prompt Fusion for AIGC Im...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhao, Yan Sang, Qingbing Deng, Zhaohong Wu, Xiaojun The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

ISBN: (纸本)9798350368741

With the rapid advancement of artificial intelligence, the field of Artificial Intelligence Generated Content (AIGC) has seen significant growth. As AI-generated images (AIGIs) become increasingly prevalent, the AIGC image quality assessment(AIGCIQA) has gained critical importance. However, traditional image quality assessment methods struggle to account for the complex relationship between generated images and their corresponding text prompts, leading to suboptimal performance in AIGCIQA tasks. Additionally, the scarcity of subjective annotation data within AIGCIQA datasets limits the effectiveness of deep learning models. To address these challenges, we propose a novel self-supervised task that utilizes intermediate image sequences generated during Text-to-image (T2I) model generation as the pre-training data. We use the number of model iterations as pseudo-labels based on the positive correlation between image quality and the number of model iterations. Due to the relative coarseness of this pseudo-label as a supervised signal, we also introduce a linear interpolation method for optimization. Additionally, we designed a framework that effectively fuses image and text features. The framework considers the dynamic fading properties of intermediate image sequences similar to video clips. It utilizes a specialized text prompt template and extracts image features through a cross-attention mechanism, which significantly improves model performance. Experimental results show that the proposed method achieves state-of-the-art performance on three mainstream AIGCIQA datasets. © 2025 ieee.

关键词： AI-generated images image quality assessment self-supervised learning

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