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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

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IMAGE INPAINTING BY MSCSWIN TRANSFORMER ADVERSARIAL AUTOENCODER

IMAGE INPAINTING BY MSCSWIN TRANSFORMER ADVERSARIAL AUTOENCO...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Chen, Bo-Wei Liu, Tsung-Jung Liu, Kuan-Hsien Natl Chung Hsing Univ Dept Elect Engn Taichung Taiwan Natl Chung Hsing Univ Grad Inst Commun Engn Taichung Taiwan Natl Taichung Univ Sci & Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781728198354

Image inpainting has been researched for years. From deeper and larger models to models that focus on global information, all of them aim to obtain results closer to reality. In this paper, we combine the stripe window and line-by-line feature shift to modify the vision Transformer (ViT) to reduce the computation cost and obtain global information from the oblique attention. In addition, we design a new loss function to enhance the texture and colors for inpainting. At last, to validate the efficacy of our proposed model, we conduct extensive experiments on commonly seen datasets (Places2 and CelebA) compared with other state-of-the-art methods. The source code and pretrained models are available at https://***/bobo0303/MSCS-Net.

关键词： HSV color space image inpainting multi-shift window vision transformer gated convolution

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2024 12th international conference on Intelligent Control and Information processing, ICICIP 2024

2024 12th International Conference on Intelligent Control an...

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12th international conference on Intelligent Control and Information processing, ICICIP 2024

ISBN: (纸本)9798350308020

The proceedings contain 33 papers. The topics discussed include: DWT-RT: a lightweight image deraining model based on discrete wavelet transforms;data-driven optimal traffic signal control with phase priority and switching cost;sonar object detection based on global context feature fusion and extraction;an image decomposition-based enhancement using a matrix iterative algorithm;tendency coefficient-based weighted distance measure for intuitionistic fuzzy sets with applications;higher-order link prediction based on message passing simplicial networks;short-term power load forecasting based on CEEMDAN-CNN-LSTM hybrid modeling;a method for large scale unconstrained binary quadratic programming problem based on graph neural network;encoding variable stiffness skills with interaction force and motion information for robot-environment interaction;and distributed Nash equilibrium seeking for high-order dynamics with event-triggered communication.

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A decade of DCASE: Achievements, practices, evaluations and future challenges

A decade of DCASE: Achievements, practices, evaluations and ...

引用

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

作者： Mesaros, Annamaria Serizel, Romain Heittola, Toni Virtanen, Tuomas Plumbley, Mark D. Signal Processing Research Center Tampere University Tampere Finland Université de Lorraine CNRS Inria Loria Nancy France Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

ISBN: (纸本)9798350368741

This paper introduces briefly the history and growth of the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, workshop, research area and research community. Created in 2013 as a data evaluation challenge, DCASE has become a major research topic in the Audio and Acoustic signal processing area. Its success comes from a combination of factors: the challenge offers a large variety of tasks that are renewed each year;and the workshop offers a channel for dissemination of related work, engaging a young and dynamic community. At the same time, DCASE faces its own challenges, growing and expanding to different areas. One of the core principles of DCASE is open science and reproducibility: publicly available datasets, baseline systems, technical reports and workshop publications. While the DCASE challenge and workshop are independent of IEEE SPS, the challenge receives annual endorsement from the AASP TC, and the DCASE community contributes significantly to the ICASSP flagship conference and the success of SPS in many of its activities. © 2025 IEEE.

关键词： AASP Challenges DCASE Challenge DCASE Workshop

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The Application of Multi-physics Coupling in Deformation Sensing and Control of Soft robots 4

The Application of Multi-physics Coupling in Deformation Sen...

引用

4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wu, Chengxu College of Mechanical and Power Engineering Nanjing Tech University Nanjing210000 China

ISBN: (纸本)9798331528676

In recent years, the applications of small-scale and soft robots in various tasks have significantly increased. However, traditional control systems have been proven no longer competent for further use due to neglecting material deformation, a vital factor for these new robotic systems. This paper focuses on developing a novel approach for detecting material deformation that can be applied to establish better-suited control systems for soft robots. Using COMSOL Multiphysics simulation software and coding software, a coupling between solid mechanics and electric current fields can be created to investigate the relationship between deformation and voltage changes. Based on the data generated from the simulation, it is aimed at discovering relationships that exist between the deformation and processible electrical signals. The experiment results demonstrate that the proposed approach can successfully reveal the expected relationship and possess the potential to form the conversion between deformation and electrical signals intended for use in control systems. This research provides a novel approach to upgrading the control system for soft robots, with promising applications in control scenarios. ©2024 IEEE.

关键词： robot applications

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GLoG-CSUnet: Enhancing vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation

GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Ra...

引用

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

作者： Zarch, Niloufar Eghbali Bagher-Ebadian, Hassan Alhanai, Tuka Ghassemi, Mohammad M. Computer Science Department Michigan State University East Lansing United States Department of Radiation Oncology Henry Ford Health Detroit United States Division of Engineering New York University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368741

vision Transformers (ViTs) have shown promise in medical image semantic segmentation (MISS) by capturing long-range correlations. However, ViTs often struggle to model local spatial information effectively, which is essential for accurately segmenting fine anatomical details, particularly when applied to small datasets without extensive pre-training. We introduce Gabor and Laplacian of Gaussian Convolutional Swin Network (GLoG-CSUnet), a novel architecture enhancing Transformer-based models by incorporating learnable radiomic features. This approach integrates dynamically adaptive Gabor and Laplacian of Gaussian (LoG) filters to capture texture, edge, and boundary information, enhancing the feature representation processed by the Transformer model. Our method uniquely combines the long-range dependency modeling of Transformers with the texture analysis capabilities of Gabor and LoG features. Evaluated on the Synapse multi-organ and ACDC cardiac segmentation datasets, GLoG-CSUnet demonstrates significant improvements over state-of-the-art models, achieving a 1.14% increase in Dice score for Synapse and 0.99% for ACDC, with minimal computational overhead (only 15 and 30 additional parameters, respectively). GLoG-CSUnet's flexible design allows integration with various base models, offering a promising approach for incorporating radiomics-inspired feature extraction in Transformer architectures for medical image analysis. The code implementation is available on GitHub at: https://***/HAAIL/GLoGCSUnet. © 2025 IEEE.

关键词： Gabor Filter Laplacian of Gaussian Medical Image Segmentation Radiomics vision Transformer

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A Dynamic Disparity Range Aggregation Method for Multi-baseline Stereo Matching 24

A Dynamic Disparity Range Aggregation Method for Multi-basel...

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6th international conference on Image, Video and signal processing, IVSP 2024

作者： Gangotri, Aniket Arun Kulkarni, Atul Gururaj Sai Nagendran, J. Tripathi, Shikha Department Of Electronics And Communication Engineering Pes University Bengaluru560085 India

ISBN: (纸本)9798400716829

Depth estimation is a pivotal challenge in the realm of signal processing, finding various applications in fields like robotics and autonomous systems. Multiple cameras are used in these applications and are found to be very useful. In this paper we address the problem of obtaining the depth information from images with improved compute complexity and accuracy. The proposed algorithm consists of three major steps, namely (a) Initial cost volume calculation, (b) Iterative calculation of successive cost volumes and (c) Aggregation of cost volumes. We use a fusion of simple cost volumes to get the initial disparity map. To improve compute complexity, we propose a novel algorithm which reduces the search range and functions as an extension tailored to overcome the limitations of the Trinocular Dynamic Disparity Range (TDDR) algorithm. Results are shown to demonstrate the performance of the algorithm with a 61.71% decrease in the computational time, compared with an existing method, on multiple Middlebury Stereo datasets. © 2024 ACM.

关键词： Computer vision

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In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer vision 48

In-Sensor & Neuromorphic Computing Are all You Need for Ener...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Datta, Gourav Liu, Zeyu Kaiser, Md Abdullah-Al Kundu, Souvik Mathai, Joe Yin, Zihan Jacob, Ajey P. Jaiswal, Akhilesh R. Beerel, Peter A. University of Southern California United States Information Sciences Institute United States Intel Labs United States

ISBN: (纸本)9781728163277

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96× and the resulting total energy by 2.32× compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet. © 2023 IEEE.

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Research of the Gesture Control System for a Industrial robot 3

Research of the Gesture Control System for a Industrial Robo...

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3rd international conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

作者： Liu, Tiansong Jiangsu Union Technical Institute Changzhou Liu Guojun Branch Changzhou China

ISBN: (纸本)9798350303698

At present, the programming methods of industrial robots mainly include off-line programming and instructional programming. However, both of the methods are time-consuming and require experienced robotics technicians. In order to improve the interactivity of industrial robot control system, this paper has designed a control system of industrial robot based on hand gestures. The whole system is composed of four parts. The IMU is used in the human hand gesture acquisition, and the signal processing of IMU is analyzed. The relationship between gestures and robot movements in linear motion mode and joint motion mode are analyzed respectively. The ABB IRB 120 robot is used as a test object and its program is designed. Finally, the effectiveness of the method proposed in this paper is validated. © 2023 IEEE.

关键词： Industrial robots

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Indian Sign Language Interpretation using Convolutional Neural Networks 10

Indian Sign Language Interpretation using Convolutional Neur...

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10th international conference on signal processing and Integrated Networks, SPIN 2023

作者： Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Agarwal, Agrima Kumar, Vishal Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9781665490993

Sign language is the most common means of communication among the speech- and hearing-impaired. Just like all other languages, tools are being developed for interlanguage translation from sign language to text;however, the complexity of the sign language makes it difficult to create a computer vision model. The focus of this study is to tackle the problem with a vision-based approach using deep learning. In this work, an 8-layer Convolutional Neural Network (CNN) aimed at sign language recognition has been proposed. The model extracts feature from input images using convolutional filters before passing them to dense layers. A validation accuracy of 99.34% has been achieved on a self-created dataset comprising of 52 classes. The model has also been tested on a publicly available dataset to evaluate its efficacy. © 2023 IEEE.

关键词： Computer vision

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KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driv...

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

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. © 2025 IEEE.

关键词： Prompt Learning vision-Language Models Zero-shot Anomaly Detection

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