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visual Understanding and Navigation for the visually Impaired Using image Captioning

Visual Understanding and Navigation for the Visually Impaire...

引用

Cognitive Computing in Engineering, communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International conference on

作者： Jitesh Uikey Ishan Undre Urvish Vasani Aman Shaikh Riddhi Mirajkar Jayashree Bagade Department of Information Technology Vishwakarma Institute Of Information Technology Pune India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

visually impaired people face problems with independent navigation due to limited visual information. Facing significant challenges, they often travel with an assistant or their relative. This project introduces an approach, increasing the independence of a blind user by developing new software solutions. The proposed system employs DenseNet201 for feature extraction and Long Short-Term Memory (LSTM) networks for generating accurate, context-aware captions. These captions are converted into real-time auditory descriptions using the gTTS library, enabling users to interpret and navigate their environment confidently. Evaluated on the Flickr8k dataset, the system achieved a BLEU score of 0.721, demonstrating its ability to generate high-quality captions. The system's architecture is designed to balance accuracy, efficiency, and user accessibility. Additionally, the system incorporates a modular architecture optimized for computational efficiency and scalability. Future work includes exploring wearable technology for continuous real-time feedback, integrating advanced natural language processing (NLP) models for richer contextual understanding, and enhancing its applicability in complex indoor and outdoor environments. This approach represents a significant step toward empowering visually impaired individuals with improved mobility and environmental awareness.

关键词： Deep learning visualization Navigation Computer architecture Feature extraction Natural language processing Real-time systems Convolutional neural networks Wearable devices Long short term memory

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Lightweight Low-Light image Enhancement Model Training and Design Considerations

Lightweight Low-Light Image Enhancement Model Training and D...

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Annual ieee Systems conference

作者： Hajira Saleem Reza Malekian Hussan Munir Dept. of computer science Malmö University Malmö Sweden Internet of Things and People Research Centre Malmö University Malmö Sweden

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Low-light conditions significantly degrade the performance of RGB cameras, particularly in applications like visual odometry estimation for autonomous vehicles, where feature visibility is critical for accurate navigation. Traditional image enhancement techniques often require manual tuning and struggle in extremely dark environments. However, deep-learning-based methods, though promising, typically demand high computational resources, making them unsuitable for real-time applications. This paper presents top low-light image enhancement (LLIE) models obtained through our experimentation with designing models to improve the visibility of features crucial for odometry tasks while minimizing computational overhead. We explore design and training strategies for real-time low-light enhancement using U-Net, a CNN architecture, alongside other CNN models with residual blocks and attention mechanisms. Through experimentation with two datasets-LOL-v2 and KITTI datasets, we demonstrate that our models offer significant improvements in feature visibility without compromising real-time performance. We also report that training the model on image patches reduces Graphics processing unit (GPU) memory usage and improves image enhancement quality, though training on full images may sometimes be necessary. Compared to a high-performing baseline model, our approach-despite yielding lower image quality is better suited for real-time applications. Some use cases, such as robotic navigation, can benefit from our lightweight model, where high-resolution details are less critical, as the focus is on real-time performance and general feature visibility. Additionally, we find that using U-Net without residual blocks and attention mechanisms results in degraded image quality. Future work will focus on transfer learning our model for odometryspecific image enhancement and integrating it into autonomous localization systems to optimize computational efficiency and enhance performance.

关键词： Training Location awareness image quality Attention mechanisms Navigation Computational modeling Graphics processing units Real-time systems Odometry image enhancement

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DeepMatch: Navigating the Complexities of Underwater Textures for Enhanced Keypoint Matching

DeepMatch: Navigating the Complexities of Underwater Texture...

引用

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

作者： Sainan Zhang Tong Liu Zhe Wang Zhibin Yu Bing Zheng Faculty of Information Science and Engineering Ocean University of China Qingdao China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya Oceanographic Institution Sanya China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Driven by demands for oceanic exploration, advancements in 3D visual tasks based on video frames are essential. Keypoint matching, essential for camera pose and motion, is hindered by the unique challenges of underwater imagery, such as sparse and repetitive textures. To tackle these issues, we introduce the DeepMatch framework, tailored for aquatic environments. It comprises two main components: a feature extraction network with a multi-level feature fusion module for enhanced keypoint detection in sparse textures, and an attention-based graph neural network with relative position encoding to correct matches in repetitive texture areas. Our experiments demonstrate that DeepMatch surpasses traditional and learning-based methods in underwater image matching, providing a robust solution for accurate camera pose estimation.

关键词： Learning systems visualization Three-dimensional displays Navigation image matching Pose estimation Speech enhancement Signal processing Feature extraction Cameras

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Multi-Prototype Grouping for Continual Learning in visual Question Answering

Multi-Prototype Grouping for Continual Learning in Visual Qu...

引用

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

作者： Licheng Zhang Zhendong Mao Yixing Peng Zheren Fu Yongdong Zhang School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual Question Answering (VQA) aims to answer questions utilizing information from both textual and visual modalities. New data categories and novel combinations of the two modalities will continuously emerge in practical applications, necessitating continual learning. For this unique compositional generalization challenge, existing work adopts prototype learning to separately capture sample-invariant features of question types and image objects, retrieving the most similar prototype from two modalities as generalizable representations. However, for semantic fuzzy samples that deviate from the corresponding prototype, relying on a single prototype may lead to error-prone representation learning. Additionally, for the catastrophic forgetting challenge, existing methods employ memory to store past samples but lack an effective selection of samples to be stored, merely updating the memory through random sampling. In this paper, we propose ProtoGroup, a multi-prototype grouping continual learning framework for VQA, which groups prototypes based on their similarity to obtain more accurate and stable sample-invariant features. We also devise a memory sample selection method based on the correlation with prototype groups from old tasks. Experimental results demonstrate our framework outperforms previous methods significantly across multiple datasets and settings.

关键词： Continuing education Representation learning visualization Accuracy Semantics Prototypes Signal processing Data aggregation Question answering (information retrieval) Speech processing

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ALIC: Adaptive Fusion Entropy Model for Learned image Compression

ALIC: Adaptive Fusion Entropy Model for Learned Image Compre...

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

作者： Lingxue Li Meiqin Liu Yifan Zhang Qi Tang Chao Yao Yao Zhao Institute of Information Science Beijing Jiaotong University Beijing China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned image compression (ALIC). To explore the correlation between channel and global spatial features, an adaptive fusion entropy model (AFEM) is designed. AFEM first slices the latent representation along the channels and leverages the adaptive channel fusion context module (ACFC) to capture correlations between the decoded and current slices. Subsequently, AFEM uses the adaptive spatial fusion context module (ASFC) to further divide the current slice into encoding pixels and reference pixels, thus improving the accuracy of probability estimation. The attention map and modulation parameter are introduced in ACFC and ASFC to interact with channel and spatial features. In addition, the variable-rate residual transformer (VResFormer) is proposed to control dynamic bit-rate by selectively modulating the high-frequency component according to coefficient weight and bias. Experimental results indicate that our ALIC outperforms other learned image compression algorithms. Our ALIC saves 5.89% bit-rate compared with VVC (4:4:4) on Kodak dataset.

关键词： Adaptation models image coding Correlation Signal processing algorithms Rate-distortion Modulation Transformers Entropy Probability distribution Speech processing

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

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

引用

International image processing, Applications and Systems conference (IPAS)

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

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

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 2 2870 . 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.

关键词： Performance evaluation Gold image segmentation Internet of Medical Things Extraterrestrial measurements Entropy Robustness Encryption Biomedical imaging Immune system

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Semantic Prior-Guided Scalable image Coding

Semantic Prior-Guided Scalable Image Coding

引用

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

作者： Wuzhen Shi Wennan Yin Fei Tao Yang Wen Guangdong Province Engineering Laboratory for Digital Creative Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We propose a semantic priori-guided scalable image coding method for simultaneously supporting fast machine vision analysis and high quality human visual experience. To obtain high-performance machine vision analysis results with a more compact base layer bitrate, our base layer directly encodes intermediate semantic features of the pre-trained machine vision task network, which effectively reduces the impact of the information needed for the human vision task. To improve model performance while quickly supporting machine vision tasks, we use structural re-parameterization technology to optimize the model. Considering that the base layer’s semantic features can effectively reflect the regions of important image content, we use the semantic prior provided by the base layer to guide the enhancement layer encoding and decoding, which allows us to pay more attention to the reconstruction of semantically important regions. In addition, we use the base layer features to predict the enhancement layer features for performing feature-domain residual coding, which further reduces the bitrate and also reduces the effect of noise compared to pixel-domain residual coding. Extensive experimental results show that our method achieves significant advantages in both object detection performance and image reconstruction tasks compared with BPG and state-of-the-art deep learning-based scalable image coding methods.

关键词： Deep learning visualization image coding Machine vision Semantics Bit rate Noise Object detection Speech processing image reconstruction

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BIAWDiff: Enhancing Low-Light images with Bio-Inspired Attention and Wavelet Diffusion

BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Atten...

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

作者： Zeyu Li Sheng Yang Hanxiang Yang Xiongxin Tang Fengge Wu Fanjiang Xu Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggle with balancing brightness enhancement and detail preservation, leading to issues such as overexposure, artifacts, and loss of high-frequency details. To address these challenges, we propose a novel method, Bio-Inspired Attention and Wavelet Diffusion (BIAWDiff), that integrates Retinex theory with bio-inspired attention and wavelet-based diffusion models to enhance low-light images. BIAWDiff consists of three key modules: the Initial Light Restoration (ILR) module for brightness enhancement and noise reduction, the Rod Cell-Inspired Attention Refinement (RCAR) module for luminance refinement, and the Detail Refinement (DR) module for restoring high-frequency details. Experimental results demonstrate that BIAWDiff outperforms existing techniques, achieving superior results in brightness enhancement, noise reduction, and detail preservation, with an average PSNR increase of 5.1% and SSIM improvement of 3.2% on paired datasets, and a reduction in NIQE and BRISQUE by 7.4% and 8.6% on unpaired datasets.

关键词： visualization Brightness Noise reduction Refining Signal processing algorithms Lighting Speech enhancement Signal processing image restoration Biomedical imaging

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Knowledge-Guided Prompt Learning for Deepfake Facial image Detection

Knowledge-Guided Prompt Learning for Deepfake Facial Image D...

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

作者： Hao Wang Cheng Deng Zhidong Zhao School of Cyberspace Security Hangzhou Dianzi University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent generative models demonstrate impressive performance on synthesizing photographic images, which makes humans hardly to distinguish them from pristine ones, especially on realistic-looking synthetic facial images. Previous works mostly focus on mining discriminative artifacts from vast amount of visual data. However, they usually lack the exploration of prior knowledge and rarely pay attention to the domain shift between training categories (e.g., natural and indoor objects) and testing ones (e.g., fine-grained human facial images), resulting in unsatisfactory detection performance. To address these issues, we propose a novel knowledge-guided prompt learning method for deepfake facial image detection. Specifically, we retrieve forgery-related prompts from large language models as expert knowledge to guide the optimization of learnable prompts. Besides, we elaborate test-time prompt tuning to alleviate the domain shift, achieving significant performance improvement and boosting the application in real-world scenarios. Extensive experiments on DeepFakeFaceForensics dataset show that our proposed approach notably outperforms state-of-the-art methods.

关键词： Training Representation learning Deepfakes visualization Large language models Signal processing Speech processing Tuning Optimization Testing

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Toward visual Pronunciation Learning: A Speech-to-Articulatory Animation Pipeline Leveraging wav2vec 2.0 and rtMRI Landmarks

Toward Visual Pronunciation Learning: A Speech-to-Articulato...

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

作者： Mushaffa Rasyid Ridha Shinobu Hasegawa Sakriani Sakti Grad. School of Adv. Science & Tech. Japan Adv. Inst. of Science & Tech. Nomi Japan Center of IDER Japan Adv. Inst. of Science & Tech. Nomi Japan Grad. School of Science & Tech. Nara Inst. of Science & Tech. Ikoma Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Most computer-assisted pronunciation training (CAPT) systems for second language (L2) learners focus on detecting mispronunciation based on predefined phonemes and assigning pronunciation scores. However, these systems often lack visual feedback or detailed corrective guidance, limiting learners’ opportunities for significant improvement. This paper presents a key advance toward developing a CAPT system that offers detailed visual feedback on articulatory movements using real-time magnetic resonance imaging (rtMRI) articulatory landmarks. The limited availability of paired speech and articulatory landmark data, typically involving only a few speakers, poses a challenge for generalizing across diverse speech patterns. To address this, we propose leveraging pretrained wav2vec 2.0 embeddings, fine-tuned to generate articulatory contours mapped to xy coordinates based on rtMRI landmark data. As evaluated with the rtMRI USC-TIMIT dataset, our system effectively reconstructs visual articulatory movements from speech, marking a significant step toward enhanced visual pronunciation learning.

关键词： Training visualization Limiting Magnetic resonance imaging Pipelines Neural networks Speech enhancement Feature extraction Real-time systems image reconstruction

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