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Navigating Beyond GPS: Horizon Detection for Autonomous Navigation

Navigating Beyond GPS: Horizon Detection for Autonomous Navi...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Alastair Finlinson Sotiris Moschoyiannis Björn Holmberg Department of Computer Science University of Surrey Surrey UK Space and Defence SAAB Defence Sweden Upsala Sweden

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In a scenario where a GPS signal is unavailable, navigation becomes more challenging. To enable precise navigation, visual features of the surroundings can be interpreted to direct vessels. We propose to use the visible horizon line as a novel solution. The visible horizon line is constructed from features at the intersection between the sky, land masses, and the waterline. Accurate extraction relies on a robust model operating in adverse weather and equipment conditions. We propose to tackle this new problem with an approach based on semantic boundary prediction with a framework that primarily looks at deep supervision for feature conditioning and differential edge detection to incorporate strong priors early in the training process. We also use semantic segmentation as a support task to provide a strong supervision signal and features to the semantic boundaries prediction task. We conduct experiments on the dataset to evaluate the model in various scenarios. Our results show that our framework predicts high-quality boundaries and segmentation masks over various datasets and domains with the ability to perform in low-data scenarios. We demonstrate that the combination of the labelling and the edge priors improve over the baseline by 32-44% on the semantic boundary estimation task.

关键词： Training visualization Navigation Semantic segmentation image edge detection Semantics Feature extraction Labeling Global Positioning System Autonomous robots

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SSRMamba: Efficient visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： visualization Computational modeling Superresolution Information retrieval Transformers Computational efficiency State-space methods Speech processing image reconstruction Hyperspectral imaging

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VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis

VoiceDiT: Dual-Condition Diffusion Transformer for Environme...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jaemin Jung Junseok Ahn Chaeyoung Jung Tan Dat Nguyen Youngjoon Jang Joon Son Chung Korea Advanced Institute of Science and Technology South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We present VoiceDiT, a multi-modal generative model for producing environment-aware speech and audio from text and visual prompts. While aligning speech with text is crucial for intelligible speech, achieving this alignment in noisy conditions remains a significant and underexplored challenge in the field. To address this, we present a novel audio generation pipeline named VoiceDiT. This pipeline includes three key components: (1) the creation of a large-scale synthetic speech dataset for pre-training and a refined real-world speech dataset for fine-tuning, (2) the Dual-DiT, a model designed to efficiently preserve aligned speech information while accurately reflecting environmental conditions, and (3) a diffusion-based image-to-Audio Translator that allows the model to bridge the gap between audio and image, facilitating the generation of environmental sound that aligns with the multi-modal prompts. Extensive experimental results demonstrate that VoiceDiT outperforms previous models on real-world datasets, showcasing significant improvements in both audio quality and modality integration. Synthesized samples are available on our demo page: https://***/projects/voicedit/

关键词： Adaptation models visualization Translation Pipelines Production Speech enhancement Signal processing Transformers Acoustics Text to speech

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Occlusion-aware Data Augmentation and Reconstruction for Improving Student Engagement in Online Learning Environments

Occlusion-aware Data Augmentation and Reconstruction for Imp...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Vincent Pham Sonit Singh School of Computer Science and Engineering University of New South Wales Sydney Australia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The demand for adaptive learning in education is an important application for Computer Vision (CV)-based detection models which extract learners’ faces to classify engagement. However, a loss in visual data due to the partial occlusion of learners’ faces imposes challenge for practical use-cases. In this paper, we propose an occlusion-aware framework to improve the robustness of non-occlusion-aware models in engagement detection. Firstly, our framework consists of an occlusion-aware data augmentation pipeline that aims to simulate the types of partially occluded faces in-the-wild. Secondly, we investigate the application of masked auto-encoders (MAE) for occlusion recovery. Extensive experiments have been performed to measure the effectiveness of the proposed occlusion-aware framework. Specifically, on the challenging FER-2013 dataset our occlusion-aware data augmentation achieves a 5.24% improvement in accuracy on baseline models and a 1.54% improvement against models with augmentation methods used in the state-of-the-art. Alongside the application of MAE, our proposed framework improves baseline classification accuracy by 13.3% and 4.59% on the FER-2013, and DAiSEE dataset for engagement detection. Our investigation into occlusion-aware engagement detection using the DAiSEE dataset provides important insight into the limitations of occlusion-awareness in engagement detection.

关键词： Computer vision visualization Accuracy Computational modeling Pipelines Data augmentation Robustness Data models Faces image reconstruction

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GPU-Accelerated Feature Extraction for Real-Time Vision AI and LLM Systems Efficiency: Autonomous image Segmentation, Unsupervised Clustering, and Smart Pattern Recognition for Scalable AI processing with 6.6× Faster Performance, 2.5× Higher Accuracy, and UX-Centric UI Boosting Human-in-the-Loop Productivity

GPU-Accelerated Feature Extraction for Real-Time Vision AI a...

引用

ieee/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Kiarash Ahi Stewart Wu Satya Sriram Germain Fenger Siemens EDA USA Siemens EDA Belgium Siemens EDA India

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

The high computational cost of digital image processing, requiring high-performance hardware and extensive resources, severely limits real-time applications. While advancements in algorithm design and GPU acceleration have significantly improved efficiency, modern AI-driven applications such as large language models (LLMs), Generative AI (GenAI), medical imaging, autonomous vehicle perception, photography, advanced nano-scale semiconductor metrology, satellite image analysis, high-precision manufacturing, robotics, and real-time anomaly detection, still demand further optimization to reduce computational overhead and improve *** this paper, we introduce GPU-Accelerated Feature Extraction to enhance runtime and efficiency in edge-based simulations. Our approach leverages AI-driven clustering, grouping images with similar visual and pattern characteristics to enable adaptive tuning on a small subset before generalizing across the full dataset. This method achieves a 3.78× reduction in ***, rather than processing an entire image, we recognize and extract a single representative pattern or region of interest (ROI) per image, removing redundant data and background noise. This refinement results in an additional 1.74× runtime improvement, culminating in an overall 6.6× speed boost, enhancing Scalable Real-Time AI processing. We also demonstrated that with a similar runtime, the accuracy achieved is 2.5× *** solution, integrated into Calibre SEMSuite™, supports multicloud and real-time deployment for enhanced scalability, usability, and performance, providing users with a powerful tool for fully automated, AI-driven image classification, making high-throughput image review feasible even at the scale required for cutting-edge *** performance gains, this approach introduces autonomous data cleaning, anomaly detection and defect identification mechanism, allowing failed patterns and defective images to be identified without h

关键词： Accuracy Runtime image edge detection Clustering algorithms Feature extraction Real-time systems Computational efficiency Artificial intelligence Anomaly detection Tuning

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Chitrarth: Bridging Vision and Language for a Billion People

Chitrarth: Bridging Vision and Language for a Billion People

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shaharukh Khan Ayush Tarun Abhinav Ravi Ali Faraz Praveen Kumar Pokala Anagha Bhangare Raja Kolla Chandra Khatri Shubham Agarwal Krutrim AI Bangalore India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent multimodal foundation models are primarily trained on English or high resource European language data, which limits their applicability to other medium and low-resource languages, such as the Indian languages. To address this limitation, we introduce Chitrarth (Chitra: image; Artha: Meaning), an inclusive Vision-Language Model (VLM), specifically targeting the rich linguistic diversity and visual reasoning across 10 prominent Indian languages. Our model effectively integrates a state-of-the-art (SOTA) multilingual Large Language Model (LLM) with a vision module, primarily trained on multilingual image-text data. Furthermore, we also introduce BharatBench, a comprehensive framework for evaluating VLMs across various low resource languages, ultimately contributing to more diverse and effective AI systems. Our model presents SOTA results for benchmarks across Indian languages while retaining its efficiency in English. Through our research, we aim to set new benchmarks in multilingual-multimodal capabilities, offering substantial improvements over existing models and establishing a foundation for facilitating future advancements in this arena.

关键词： Training Representation learning visualization Pipelines Training data Benchmark testing Data models Multilingual Speech processing Signal resolution

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Low-Power Approximate Computing-based VLSI Architecture for Biomedical Signal processing

Low-Power Approximate Computing-based VLSI Architecture for ...

引用

2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Sathiya, A. Prabhavathy, B. Balasupramani, A. Amutha, A. Kavitha, L. Saravanakumar, R. Department of Electronics and Communication Engineering Karur Thalavapalayam639113 India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai600124 India Dhanalakshmi Srinivasan College of Engineering Department of Electrical and Electronics Engineering Coimbatore641105 India Thalavapalayam639113 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331521394

The analysis of ECG, EEG and EMG signals requires biomedical signal processing which needs low-power high-performance VLSI architectures to achieve real-time processing. This paper introduces an optimized VLSI architecture which enables biomedical signal processing through energy-efficient digital filtering along with feature extraction as well as pipeline-based clock gating. The proposed architecture combines a distributed arithmetic FIR filter without multipliers to reduce complexity and it integrates a low-power feature extraction system that detects ECG R-peaks and EEG spectral energy and EMG muscle activity effectively. The system uses dynamic clock gating together with power gating to cut down both dynamic power usage and leakage power. The experimental findings show that the developed VLSI architecture both reduces power usage through low hardware complexity while keeping high accuracy levels for biomedical signal processing. The system delivers its performance measurement through power dissipation and area efficiency along with processing speed which enables its use in wearable and implantable medical devices. © 2025 ieee.

关键词： Medical image processing

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Pre-training with Synthetic Patterns for Audio

Pre-training with Synthetic Patterns for Audio

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuchi Ishikawa Tatsuya Komatsu Yoshimitsu Aoki LY Corporation Keio University Tokyo Japan LY Corporation Tokyo Japan Keio University Kanagawa Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose to pre-train audio encoders using synthetic patterns instead of real audio data. Our proposed framework consists of two key elements. The first one is Masked Autoencoder (MAE), a self-supervised learning framework that learns from reconstructing data from randomly masked counterparts. MAEs tend to focus on low-level information such as visual patterns and regularities within data. Therefore, it is unimportant what is portrayed in the input, whether it be images, audio mel-spectrograms, or even synthetic patterns. This leads to the second key element, which is synthetic data. Synthetic data, unlike real audio, is free from privacy and licensing infringement issues. By combining MAEs and synthetic patterns, our framework enables the model to learn generalized feature representations without real data, while addressing the issues related to real audio. To evaluate the efficacy of our framework, we conduct extensive experiments across a total of 13 audio tasks and 17 synthetic datasets. The experiments provide insights into which types of synthetic patterns are effective for audio. Our results demonstrate that our framework achieves performance comparable to models pre-trained on AudioSet-2M and partially outperforms image-based pre-training methods.

关键词： Data privacy visualization Autoencoders Self-supervised learning Signal processing Licenses Data models Speech processing image reconstruction Synthetic data

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy image edge detection visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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