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

共 79607 条记录，以下是31-40 订阅

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SPT: Sequence Prompt Transformer for Interactive image Segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

引用

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

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 ieee.

关键词： computer vision interactive image segmentation

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 ieee.

关键词： image analysis

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Reversible image Data Hiding based on improved Block Histogram Shifting 24

Reversible Image Data Hiding based on improved Block Histogr...

引用

4th international conference on signal processing and Communication Technology, SPCT 2024

作者： Wang, Jiaxin XiDian University Shaanxi Xi’an China

ISBN: (纸本)9798400710636

With the rapid development of network communication, image data hiding technology has occupied an important position in the field of information security. As a branch of image data hiding, reversible image data hiding can recover the original carrier after extracting the embedded information. Reversible image data hiding has potential applications in medical images, military images, legal scanned documents, and other fields. There is a strong demand in many applications to maintain a high PSNR value for stego images while providing high embedding capacity. But this work often has great challenges. In this paper, we propose a new reversible image data hiding algorithm by improving typical block histogram shifting. The pixels corresponding to the peak and sub-peak in the block histogram are selected as the host for information embedding. The proposed technique can improve the embedding capacity and limit image distortion through careful block processing. The intensive experiments using images of different sizes as host media are conducted. The experimental results show that stego images can achieve an average PSNR of over 45dB with a high embedding capacity. © 2024 Copyright held by the owner/author(s).

关键词： image data hiding

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processing and Classification Algorithms of Medical images 6

Processing and Classification Algorithms of Medical Images

引用

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

作者： Seitaj, Oltiana Mechanics University of Roma Tre Department of Industrial Engineering Electronics Rome Italy

ISBN: (纸本)9798331506520

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional Neural Networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation. © 2025 ieee.

关键词： Convolutional neural networks

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CabiNet: A Deep Learning Framework for Multiclass Medical image Segmentation from Multiple Single Class Datasets

CabiNet: A Deep Learning Framework for Multiclass Medical Im...

引用

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

作者： Soni, Aman Maiti, Ishita Ghosh, Nirmalya Electrical Engineering IIT Kharagpur Kharagpur India School of Medical Science and Technology IIT Kharagpur Kharagpur India

ISBN: (纸本)9798350368741

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging for multi-class annotations and segmentation of regions of interest, e.g., multi-organ segmentation that plays a vital role in computer-assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multi-class healthy organ segmentation from partially annotated abdominal MRI data, i.e., multiple single-class datasets. Multiple organ specific expert networks and a Jack of All (JoA) network are trained to generate posterior probability maps of individual organs simultaneously which are accumulated to get the final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%. © 2025 ieee.

关键词： Computer Assisted Diagnosis Distributed learning Multi-Organ Segmentation Sparse annotation

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Simplified one-sided image-to-image Translation with Reconstruction-Constrained Generative Adversarial Networks

Simplified one-sided Image-to-Image Translation with Reconst...

引用

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

作者： Wang, Shuocheng Wu, Qingfeng Ge, Mengyuan Wang, Yingdong Xiamen University Xiamen China

ISBN: (纸本)9798350368741

The utilization of generative adversarial networks (GANs) for image-to-image translation has undergone extensive scholarly exploration. Nonetheless, prevailing unidirectional image translation tasks remain intricate and computationally inefficient due to the employment of complex computational methods and supplementary network modules, thereby imposing a significant computational burden. This study suggests integrating reconstruction constraints as a surrogate for complex ones, reducing computational burden. By skillfully combining these with adversarial losses, the network performs image translation more effectively. To enhance both translational and reconstruction competencies, we propose using the discriminator's encoding mechanism to retain the image's attributes. This approach results in a simplified yet powerful unidirectional image translation model, proven superior through comparative analysis with various GAN-based models. Additionally, the practical efficacy of our model is empirically verified through systematic experimentation. © 2025 ieee.

关键词： Generative Adversarial Networks (GANs) image-to-image Translation Reconstruction Constraints

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Triplet Synthesis for Enhancing Composed image Retrieval via Counterfactual image Generation

Triplet Synthesis for Enhancing Composed Image Retrieval via...

引用

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

作者： Uesugi, Kenta Saito, Naoki Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido University Japan

ISBN: (纸本)9798350368741

Composed image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference image, modification text describing desired changes, and a target image that reflects these changes. For effectively training CIR models, extensive manual annotation to construct high-quality training datasets, which can be time-consuming and labor-intensive, is required. To deal with this problem, this paper proposes a novel triplet synthesis method by leveraging counterfactual image generation. By controlling visual feature modifications via counterfactual image generation, our approach automatically generates diverse training triplets without any manual intervention. This approach facilitates the creation of larger and more expressive datasets, leading to the improvement of CIR model's performance. © 2025 ieee.

关键词： Composed image retrieval counterfactual image generation triplet synthesis

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Medical image Scalar for Portable Medical Devices 1

Medical Image Scalar for Portable Medical Devices

引用

1st international conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Pangaonakar, Mrinalini Joshi Shingare, P.P. College of engineering Pune Dept of E&TC Engineerig Pune India

ISBN: (纸本)9798350355611

The field of image processing is playing a vital role in making technological changes those results in real time applications. image scaling is one of such fundamental method that helps to resolve storage issue and also helps for fast image data transmission. The technique is beneficial in case of real time portable devices and similar applications as usually the scaling required at end user device. This paper proposes medical image scalar for portable medical devices implemented using FPGA which involves creating a compact, efficient, and reliable system capable of adjusting the size and resolution of medical images for display on different devices This medical image scalar can be used for applications such as portable medical devices for remote disease diagnosis, image assisted surgery and other telemedicine applications where image quality, processing power and energy efficiency are of critical concerns. The proposed medical image scalar involves polynomial based enhanced bilinear interpolation technique for image scaling that preserves the image quality and also make VLSI implementation easier. © 2025 ieee.

关键词： Diagnosis

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PsSR: Hybrid Path Selection Mechanism for Efficient image Super-Resolution

PsSR: Hybrid Path Selection Mechanism for Efficient Image Su...

引用

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

作者： Ding, Cheng Zhao, ZhongQiu Zhao, Yang Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350368741

In practical applications, large images are processed in patches, many of which are simple and smooth, making them suitable for lighter network processing. This paper proposes a hybrid path selection mechanism that enables the dynamic routing of patches through the network, achieving a balance between accuracy and computational efficiency. The proposed method develops a dual-branch architecture, in which the two branches keep the same structural design, but differ in the number of channels. An efficient classification module is integrated for directing patches to the appropriate branches within the model. In addition, within each branch, we introduce an early exit strategy to further reduce the calculation cost. Subsequently, we enhance the restoration capabilities of the lightweight branch through channel and spatial feature distillation. Experimental results demonstrate that our Hybrid Path Selection based Super-Resolution (PsSR) mechanism effectively reduces computational costs while preserving the integrity of the backbone performance. © 2025 ieee.

关键词： dual-branch knowledge distillation path selection super-resolution

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Mobile-friendly image de-noising: Hardware Conscious Optimization for Edge Application

Mobile-friendly Image de-noising: Hardware Conscious Optimiz...

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

作者： Miriyala, Srinivas Vajrala, Sowmya Kumar, Hitesh Kodavanti, Sravanth Rajendiran, Vikram On-Device AI Samsung Research Institute Bangalore India

ISBN: (纸本)9798350368741

image enhancement is a critical task in computer vision and photography that is often entangled with noise. This renders the traditional image signal processing (ISP) ineffective compared to the advances in deep learning. However, the success of such methods is increasingly associated with the ease of their deployment on edge devices, such as smartphones. This work presents a novel mobile-friendly network for image de-noising obtained with Entropy-Regularized differentiable Neural Architecture Search (NAS) on a hardware-aware search space for a U-Net architecture, which is first-of-its-kind. The designed model has 12% less parameters, with ~2-fold improvement in on-device latency and 1.5-fold improvement in the memory footprint for a 0.7% drop in PSNR, when deployed and profiled on Samsung Galaxy S24 Ultra. Compared to the SOTA Swin-Transformer for image Restoration, the proposed network had competitive accuracy with ~18-fold reduction in GMACs. Further, the network was tested successfully for Gaussian de-noising with 3 intensities on 4 benchmarks and real-world de-noising on 1 benchmark demonstrating its generalization ability. © 2025 ieee.

关键词： De-Noising Differentiable NAS Hardware aware Search space Smartphone Deployment

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