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Map Generation based on improved SIFT Algorithm 3

Map Generation based on improved SIFT Algorithm

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3rd International conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Shi, Xiaotian Xie, Qin Fan, Yajing Li, Zhiqiang Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Hubei Wuhan China Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan China

ISBN: (纸本)9781510688179

Map generation based on Unmanned Aerial Vehicle (UAV) image stitching can fully utilize the convenient image acquisition capabilities of unmanned aerial vehicle (UAV), enabling the rapid acquisition of high-resolution remote sensing maps for large scenes, meeting the needs of applications such as scene reconstruction and map navigation. However, generating large-scale maps requires stitching a large number of images, which incurs significant computational resource costs and is prone to error accumulation. To address this, this paper proposes an image stitching and fusion method that integrates the geographic information inherent in UAV images with an improved Scale-Invariant Feature Transform (SIFT) algorithm. The proposed method uses geographic information to divide a large number of images into strips, then stitches and fuses the images within each strip, ultimately stitching and fusing multiple strips to create the map. When calculating the transformation matrix, geographic information is used to determine the range of overlapping areas to form a mask that reduces the probability of mismatches, thereby improving matching accuracy. Experimental results demonstrate that the algorithm is well-designed and achieves low processing time while ensuring good stitching quality. © The Authors.

关键词： Unmanned aerial vehicles (UAV)

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Foreground Prediction for image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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A blind assistant system utilizing object detection for improved spatial awareness 1st

A blind assistant system utilizing object detection for impr...

引用

1st International conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Eqbal, Shahid Bharadwaj, Prashant Singh, Pratistha Tiwari, Rashmi Department of ECE Galgotias College of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

A prototype of a blind assisting system that utilizes machine learning for real-time object detection and classification to help visually impaired people to navigate independently without relying on external assistance. The program employs image processing and machine learning techniques to accurately detect objects in real-time via the device camera. It subsequently notifies the users about the obstacle and its location through voice output. The prototype is based on SSD-Mobilnet-v2 and provides a viable solution for those with visual impairment, to enhance their quality of life. © 2025 the Author(s).

关键词： image processing Object detection Real time SSD-Mobilenet-v2

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Local Overlapping Attentional Selection with VIT for image Classification in Remote Sensing

Local Overlapping Attentional Selection with VIT for Image C...

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2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Zou, ChangMan Jeon, Wang-Su Rhee, Sang-Yong University of Kyungnam Department of IT Convergence Engineering Changwon51767 Korea Republic of College of Computer Science and Technology Beihua University Jilin132013 China University of Kyungnam Department of Computer Engineering Changwon51767 Korea Republic of

ISBN: (纸本)9798331510756

Remote sensing image classification presents unique challenges due to the complexity of the scenes and the variability in visual features caused by the overhead perspective. Traditional convolutional neural networks (CNNs) struggle with long-range dependencies and extracting sufficient context for accurate classification. To address this, we propose the LSViT model, a novel approach that incorporates a local attention selection module into the Transformer architecture, specifically designed for remote sensing scene classification. LSViT enhances the classification performance by selecting discriminative tokens and removing redundant information. Our model leverages the Transformer's capacity for capturing both global and local features, improving the focus on important image regions. We evaluate LSViT on two benchmark datasets, AID and NWPU-RESISC45, and demonstrate its competitive performance, achieving state-of-the-art accuracy compared to traditional CNN and other Transformer-based methods. Notably, the local attention selection module enhances the model's ability to classify scenes with overlapping objects and complex backgrounds, making it an effective solution for large-scale remote sensing image classification tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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15th EAI International conference on Bio-inspired Information and communications Technologies, BICT 2024

15th EAI International Conference on Bio-inspired Informatio...

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15th EAI International conference on Bio-inspired Information and communications Technologies, BICT 2024

ISBN: (纸本)9783031815980

The proceedings contain 16 papers. The special focus in this conference is on Bio-inspired Information and Communication Technologies. The topics include: Optimizing Drug Delivery Strategies by Pathway Analysis for Waveform Modulation-Based Molecular Communication;Enhancing DNA-Based IoBNT Throughput and Reducing Congestion with Yin-Yang Coding;a Molecular Communication Model Driven by Magnetic Field Force;CDM Based on Izhikevich Neuron Model;multi-user Diffusive Molecular Communication Systems with A Passive Relay Node: Receiver Design and Performance Analysis;machine Learning-Based Detection Time Estimation for Molecular Communication;taming Signal-Dependent Counting Noise with Machine Learning for Molecular Communication;energy-Efficient Transmitter Creation in Molecular Communication;a Method for Determining Relay Node Location in Molecular Communication;glycemic Oscillation Decomposition-Based Personalized Blood Glucose Prediction with Continuous Glucose Monitoring;A Compact Bio-inspired Microstrip Antenna (Bi-MPA) for Medical Microwave Imaging Applications;image Encryption and Decryption Algorithm Based on DNA Sequence: Performance Analysis of Channel Fusion processing;Optimizing the Classification of SSVEP Signals in Brain-Computer Interfaces: A Novel Sliding Window Data Segmentation Method Based on Weighted Voting Mechanism;spontaneous Motion of Nanorobots Inspired Computational Technology for Tumor Boundary Exploration.

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Clustering-based image-Text Graph Matching for Domain Generalization 27th

Clustering-based Image-Text Graph Matching for Domain Genera...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Park, Nokyung Chae, Daewon Shim, Jeongyong Kim, Sangpil Kim, Eun-Sol Kim, Jinkyu Department of Computer Science and Engineering Korea University Seoul Korea Republic of Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Artificial Intelligence Application Hanyang University Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of

ISBN: (纸本)9783031781919

Learning domain-invariant visual representations is important to train a model that can generalize well to unseen target task domains. Recent works demonstrate that text descriptions contain high-level class-discriminative information and such auxiliary semantic cues can be used as effective pivot embedding for domain generalization problems. However, they use pivot embedding in a global manner (i.e., aligning an image embedding with sentence-level text embedding), which does not fully utilize the semantic cues of given text description. In this work, we advocate for the use of local alignment between image regions and corresponding textual descriptions to get domain-invariant features. To this end, we first represent image and text inputs as graphs. We then cluster nodes within these graphs and match the graph-based image node features to the nodes of textual graphs. This matching process is conducted both globally and locally, tightly aligning visual and textual semantic sub-structures. We experiment with large-scale public datasets, such as CUB-DG and DomainBed, and our model achieves matched or better state-of-the-art performance on these datasets. The code is available at: https://***/noparkee/Graph-Clustering-based-DG. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Towards Adversarial Robustness and Reducing Uncertainty Bias through Expert Regularized Pseudo-Bidirectional Alignment in Transductive Zero Shot Learning 27th

Towards Adversarial Robustness and Reducing Uncertainty Bias...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Kumar Sinha, Abhishek Mishra, Deepak Moorthi, S. Manthira Signal and Image Processing Area Space Applications Center Ahmedabad India Indian Institute of Space Science and Technology Thiruvananthapuram India

ISBN: (纸本)9783031781827

Transductive zero-shot learning (TZSL) aims to minimize the domain shift between the learned and true distribution of the unseen classes by allowing access to the unpaired samples from unseen classes. While many distribution alignment based methods attempt to align both visual and semantic spaces to train the classifier, their performance is still limited by confirmation bias. Additionally, bidirectional alignment approaches are based on the strong assumption that the intrinsic dimensions of visual and semantic spaces are the same, which is rarely true. In this work, we first highlight the limitations of bidirectional alignment in terms of intrinsic dimensionality. We then present a pseudo-bidirectional approach that, without any underlying assumptions on these spaces, utilizes the learned visual-to-attribute mapping to minimize the distribution shift between learned and true unseen visual feature distributions. We further utilize an entangled loss between semantic and visual space to minimize the confirmation or uncertainty bias and improve the adversarial robustness. We, theoretically and empirically, show the performance gain in addition to the adversarial robustness under the proposed setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Act-ChatGPT: Introducing Action Features into Multi-modal Large Language Models for Video Understanding 27th

Act-ChatGPT: Introducing Action Features into Multi-modal La...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Nakamizo, Yuto Yanai, Keiji The University of Electro-Communications Tokyo Chohu Japan

ISBN: (纸本)9783031783463

In the last few years, the advancement of GPT-4 and similar extensive large language models has significantly influenced video comprehension fields, models have been developed to exploit these advances to enhance interactive video comprehension. However, existing models generally encode video using image language models or video language models with sparse sampling, overlooking the vital action features present in each video segment. To address this gap, we propose Act-ChatGPT, an innovative interactive video comprehension model that integrates action features. Act-ChatGPT incorporates a dense sampling-based action recognition model as an additional visual encoder, enabling it to generate responses that consider the action in each video segment. Comparative analysis reveals Act-ChatGPT superiority over a base model, with qualitative evidence highlighting its adeptness at recognizing actions and responding based on them. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image segmentation

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Banana Variety Classification and Grading at Bunch Level 17th

Banana Variety Classification and Grading at Bunch Level

引用

17th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Guru, D.S. Saritha, N. Mahesha, M.C. Department of Studies in Computer Science University of Mysore Karnataka Manasagangotri Mysuru570006 India

ISBN: (纸本)9789819606917

Sorting bananas based on their visual features helps meet consumer requirements in various markets and food processing industries. Many banana varieties look similar in terms of their size and shape, making differentiation challenging and leading to misclassification without detailed visual data. Our pro-posed system focusses on distinguishing these visually similar varieties, especially the closely sized Elakki-bale and Rasbale. We employ transfer learning techniques for banana variety classification and grading to achieve high accuracy. Our bunch-level banana image dataset addresses the gap in existing dataset that are smaller segments mainly hand-level or finger-level, serving the large-scale food processing industry needs. This study also comprises a system for grading bananas into low, medium, and high quality. In our proposed work, during classification, deep features were extracted and various classifiers were tested. The study revealed that the Linear SVM classifier works better for Integrated Model. For Convolutional Model MobileNet features lead in accuracy and most time-efficient in terms of training. EfficientNetB7 gives high accuracy but requiring the longest training duration. In this study, a hierarchical approach was employed for variety classification and grading. The study revealed that the accuracy of our proposed hierarchical classification approach using Convolutional Models is 96.85% which is higher than the conventional single shot multi-class classification approach (94.87%). Our proposed hierarchical approach, focused on bunch-level classification, is compared against state-of-the-art single shot multi-class classification, which primarily targets hand or finger-level classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Banana Grading Banana Variety Classification image processing Techniques

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SteDM: Efficient image Steganography with Diffusion Models 24th

SteDM: Efficient Image Steganography with Diffusion Models

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24th International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Wang, Changguang Shi, Haoyi Li, Qingru Zhao, Dongmei Wang, Fangwei Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9789819615506

Existing deep learning-based image steganography schemes mostly neglect the latent space of images. These schemes merely adopt simple concatenation of image feature vectors, resulting in a low utilization rate of features, low steganographic image quality, and poor image robustness. This paper introduces the latent diffusion model into an image steganography scheme, namely SteDM. The SteDM firstly uses an encoder to transform the cover image and the secret image into the latent space, then employing a cross-attention mechanism to fuse them during the inverse diffusion process. Then we use a decoder to obtain a steganographic image containing secret image features. During the extraction process, the latent space-based diffusion model is similarly employed. Training loss is defined as a joint optimization of the autoencoder and diffusion model during the training process. Experimental results demonstrate that the SteDM outperforms existing steganography schemes in some aspects such as visual effects, security, and robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Steganography

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