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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是31-40 订阅

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Generate Vector Graphics of Fine-grained pattern based on the Xception Edge Detection 4

Generate Vector Graphics of Fine-grained Pattern based on th...

引用

4th International conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Chen, Anqi Chen, Hao Peng, Yicui Hu, Jinrong Deng, Linjie Wen, Xiang Wang, Xin Chengdu Technological University Chengdu China Chengdu University of Information Technology Chengdu China Industry Research Institute Chengdu China University at Buffalo United States

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

The innovative generation of vector graphics with fine-grained images using Artificial Intelligence has become an important task in edge extraction. In this paper, we take Qiang embroidery image as an example due to its containing fine-grained edges, which is more suitable for the study of image processing.and pattern recognition. We firstly adopt appropriate pre-processing.methods, improved adaptive median filtering (IAMF) for the image to reduce image noise. Then, the Xception based on convolutional neural networks is used for edge detection and extraction. Results show that Qiang embroidery images, after denoising and edge extraction, can be clearly identified the shape characteristics of the images. Based on this approach, it can be converted into vector graphics for digital preservation and further artistic reinterpretation. The use of the Xception effectively addresses Qiang embroidery extraction in two-dimensional vector images, offering a practical reference for preserving related intangible cultural heritage. © 2025 SPIE.

关键词： Median filters

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MLNet: Mutual Learning Network to Improve Self-Supervised Representation for Fine-Grained Visual recognition

MLNet: Mutual Learning Network to Improve Self-Supervised Re...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Zhao, Peipei Wang, Jiaxuan Lu, Zixiang Miao, Qiguang School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350368741

High-quality annotation of fine-grained visual categorization requires extensive professional knowledge, which is time-consuming and laborious. Therefore, learning fine-grained visual representations from a large number of unlabeled images through self-supervised learning has become a popular alternative solution recently. However, the existing self-supervised learning methods are not effective in fine-grained visual categorization since many features helpful for optimizing self-supervised learning objectives are unsuited to characterize the subtle differences in fine-grained viusal recognition. To deal with this issue, we propose a mutual learning network to enhance the model's attention towards discriminative semantic features. The key idea is to consider semantic consistency between different augmented views within same image and capture discriminative semantic information. For semantic consistency, our research demonstrates that cross-view attention module between different augmented views can guide our model to capture similar semantic features. Based on this, we further build a GradCAM-guided multi-dimension loss that utilize GradCAM to control our model from different dimensions to discriminative semantic information that are beneficial to fine-grained visual recognition. Experiments on CUB-200-2011, Stanford Cars and Aircrafts datasets demonstrate that the mutual learning network outperforms previous self-supervised learning methods in linear probing and image retrieval. © 2025 ieee.

关键词： cross-view attention module fine-grained visual categorization GradCAM-guided multi-dimension loss self-supervised learning

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Gastrointestinal Lung Cancer Disease Classification via Hybrid Deep Architecture Trained with Improved pattern Features 5

Gastrointestinal Lung Cancer Disease Classification via Hybr...

引用

5th International conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Mallick, Pradeep Kumar Rout, Subhashree Padhy, Neelamadhab Mohanty, Jnyana Ranjan Beura, Mamata Du School of Computer Engineering Odisha Bhubaneswar India Giet University Department of Computer Science and Engineering Odisha Gunupur India Du School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331522100

In the initial stage of lung cancer, GI tract is not frequently the position of metastases. It may be challenging to distinguish pulmonary metastases from the gastrointestinal (GI) tract and primary lung cancer. The following stages are followed to classify the gastrointestinal lung cancer disease: image acquisition, preprocessing. feature extraction, and classification. The process of collecting images is called image acquisition. As per the work, the benchmark dataset is under consideration. Gaussian filtering is used to preprocess the input image for further processing. Subsequently, the features that are retrieved in the Feature Extraction step include Shape feature, Multi texton features, Improved median Binary pattern, and Local Gradient Increasing pattern (LGIP). A hybrid model trained with the extracted features will be used for classification, which integrates Squeezenet and LSTM models. The results of the proposed work is compared over the conventional models in terms of different performance measures. © 2025 ieee.

关键词： Lung cancer

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ILDiff: Generate Transparent Animated Stickers by Implicit Layout Distillation

ILDiff: Generate Transparent Animated Stickers by Implicit L...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Zhang, Ting Yuan, Zhiqiang Zhu, Yeshuang Zhou, Jie Zhang, Jinchao Pattern Recognition Center WeChat AI Tencent China

ISBN: (纸本)9798350368741

High-quality animated stickers usually contain transparent channels, which are often ignored by current video generation models. To generate fine-grained animated transparency channels, existing methods can be roughly divided into video matting algorithms and diffusion-based algorithms. The methods based on video matting have poor performance in dealing with semi-open areas in stickers, while diffusion-based methods are often used to model a single image, which will lead to local flicker when modeling animated stickers. In this paper, we firstly propose an ILDiff method to generate animated transparent channels through implicit layout distillation, which solves the problems of semi-open area collapse and no consideration of temporal information in existing methods. Secondly, we create the Transparent Animated Sticker Dataset (TASD), which contains 0.32M high-quality samples with transparent channel, to provide data support for related fields. Extensive experiments demonstrate that ILDiff can produce finer and smoother transparent channels compared to other methods such as Matting Anything and Layer Diffusion. Our code and dataset will be released at link https://***. © 2025 ieee.

关键词： animated sticker generation implicit layout distillation transparent channel generation

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Eye side and orientation detection of iris images using lightweight textural descriptors for embedded systems 6

Eye side and orientation detection of iris images using ligh...

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6th ieee International conference on image processing. Applications and Systems, IPAS 2025

作者： Tran, Chung Nguyen Carrabina, Jordi Castells-Rufas, David Nguyen, Minh Son Tran, Le-Anh Dang, Nhan Cach Universitat Autònoma de Barcelona Departament de MiSE Spain University of Information Technology Dept. of Computer Engineering Ho Chi Minh City Viet Nam Mindintech Inc. Dept. of Research and Development Seoul Korea Republic of HCMC University of Transport IITEEE Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331506520

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, these systems are vulnerable to spoofing attacks, which can occur by rotating an iris image or an iris scanner during image acquisition. Additionally, correctly recognizing the eye side significantly reduces computational load and decreases the likelihood of false positives in biometric systems. This paper introduces a novel method for automatically detecting the correct left/right and upright/upside-down orientation of an iris image. The proposed method employs a lightweight feature extraction algorithm utilizing Local Binary pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to extract features from the iris image. A Support Vector Machine (SVM) classifier is then used to determine the eye side or orientation of the iris images. LBP captures the local texture details, whereas GLCM describes the statistical features of the iris images. By combining these textural features, the proposed method improves the ability to classify eye side or orientation. The efficient and precise texture descriptors allow for implementation on embedded systems, including IoT or mobile devices. Experimental results on benchmark datasets demonstrate that the proposed method outperforms existing baseline methods in both performance and speed. © 2025 ieee.

关键词： Support vector machines

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FPGA-based image signal processing.CEPST module design 2

FPGA-based image signal processing CEPST module design

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2nd International conference on Frontiers of Applied Optics and computer Engineering, AOCE 2025

作者： Tsai, Lifen Chen, Chinta Yang, Chihchung Tang, Lijie Wang, Xiahui Xue, Wuyang Ma, Huan Zhaoqing University Guangdong China Zhaoqing University Guangdong China

ISBN: (纸本)9781510689404

CEPST algorithm is an important step in image processing. which is widely used in computer vision, pattern recognition, and machine learning. In order to improve the efficiency and performance of CEPST algorithm, more and more studies have implemented the task of CEPST algorithm on FPGA boards in recent years. FPGA boards offer parallel computing capabilities, low power consumption, and low latency, making them suitable for high-performance computing and real-time applications. In this paper, the Vivado tool is used to implement the method of CEPST algorithm. Vivado has a complete toolchain and debugging environment, supports a variety of hardware description languages, and provides a rich set of IP cores and acceleration libraries to facilitate FPGA design and development. This paper details the steps of CEPST algorithm using Vivado, from algorithm design to simulation. The CEPST algorithm was designed to improve the accuracy and speed, and further improve the performance of the overall system. © The Authors.

关键词： image enhancement

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 ieee.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 ieee.

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Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking Neural Networks with Dynamic Time Steps for Vision Transformers

Dynamic SpikFormer: Low-Latency & Energy-Efficient Spiking N...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Datta, Gourav Liu, Zeyu Li, Anni Beerel, Peter A. Dept. of Electrical Computer & Systems Engineering Case Western Reserve University Cleveland United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los Angeles United States

ISBN: (纸本)9798350368741

Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly reduced the number of time steps (down to 1) for improved latency and energy efficiency, however, they target only convolutional neural networks (CNN). These algorithms, when applied to the recently spotlighted vision transformers (ViT), either require a large number of time steps or fail to converge. Based on the analysis of the histograms of the ANN and SNN activation maps, we hypothesize that each ViT block has a different sensitivity to the number of time steps. We propose a novel training framework that dynamically allocates the number of time steps to each ViT module depending on a trainable score assigned to each timestep. In particular, we generate a scalar binary time step mask that filters spikes emitted by each neuron in a leaky-integrate-and-fire (LIF) layer. The resulting SNNs have high activation sparsity and require only accumulate operations (AC), except for the input embedding layer, in contrast to expensive multiply-and-accumulates (MAC) needed in traditional ViTs. This yields significant improvements in energy efficiency. We evaluate our training framework and resulting SNNs on image recognition tasks including CIFAR10, CIFAR100, and imageNet with different ViT architectures. We obtain a test accuracy of 95.97% with 4.97 time steps with direct encoding on CIFAR10. © 2025 ieee.

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A New Model for Prototype-based Continual Learning in Hyperspherical Space

A New Model for Prototype-based Continual Learning in Hypers...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Ren, Yixin Xia, Yewei Ke, Li Huang, Longtao Xue, Hui Zhou, Shuigeng School of Computer Science Fudan University Shanghai200438 China Alibaba Group Hangzhou311121 China

ISBN: (纸本)9798350368741

The continuous emergence of new objects in the visual world poses a serious challenge to deep object recognition methods, which sparks the increasing study on continual or incremental learning. However, learning new tasks faces the tough catastrophic forgetting problem, i.e., dramatic performance degradation on old tasks. A good continual learning model should be robustly adapted to the upcoming tasks while effectively handling catastrophic forgetting. In this paper, we focus on the class-incremental learning (CIL) task, and propose a novel prototype-based continual learning model C-HPN that projects the visual features into a hypersphere geometric space, where continual learning is conducted. C-HPN features two-fold contributions. On the one hand, instead of using the popular cross-entropy loss, we develop an instance-prototype compact loss to obtain well-clustered hyperspherical embeddings and a prototype-prototype separability loss to boost the model's generalization by introducing large angle distance inductive bias between prototypes in the hyperspherical space. On the other hand, prototype construction and adaptation strategies are designed for effectively adapting new classes, and an instance-prototype relationship preservation distillation mechanism is introduced to overcome catastrophic forgetting. Extensive experiments on several image datasets validate the effectiveness of the proposed method. © 2025 ieee.

关键词： class-incremental learning continual learning hypersphere geometry prototype networks

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