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When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo

When Epipolar Constraint Meets Non-local Operators in Multi-...

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international conference on computer Vision (ICCV)

作者： Tianqi Liu Xinyi Ye Weiyue Zhao Zhiyu Pan Min Shi Zhiguo Cao Ministry of Education School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Learning-based multi-view stereo (MVS) method heavily relies on feature matching, which requires distinctive and descriptive representations. An effective solution is to apply non-local feature aggregation, e.g., Transformer. Albeit useful, these techniques introduce heavy computation overheads for MVS. Each pixel densely attends to the whole image. In contrast, we propose to constrain nonlocal feature augmentation within a pair of lines: each point only attends the corresponding pair of epipolar lines. Our idea takes inspiration from the classic epipolar geometry, which shows that one point with different depth hypotheses will be projected to the epipolar line on the other view. This constraint reduces the 2D search space into the epipolar line in stereo matching. Similarly, this suggests that the matching of MVS is to distinguish a series of points lying on the same line. Inspired by this point-toline search, we devise a line-to-point non-local augmentation strategy. We first devise an optimized searching algorithm to split the 2D feature maps into epipolar line pairs. Then, an Epipolar Transformer (ET) performs non-local feature augmentation among epipolar line pairs. We incorporate the ET into a learning-based MVS baseline, named ET-MVSNet. ET-MVSNet achieves state-of-the-art reconstruction performance on both the DTU and Tanks-and-Temples benchmark with high efficiency. Code is available at https://***/TQTQliu/ET-MVSNet.

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ChatGPT: The Curious Case of Attack Vectors' Supply Chain Management Improvement

ChatGPT: The Curious Case of Attack Vectors' Supply Chain Ma...

引用

ieee international conference on Electro-Information Technology

作者： Minhaz Chowdhury Nafiz Rifat Shadman Latif Mostofa Ahsan Md Saifur Rahman Rahul Gomes Department of Computer Science East Stroudsburg University East Stroudsburg PA USA Department of Computer Science North Dakota State University Fargo ND USA Department of Computer Science American International University Bangladesh Dhaka Bangladesh Department of Math Science and Technology University of Minnesota Crookston Crookston MN USA Department of Computer Science University of Wisconsin Eau Claire Eau Claire WI USA

The field of Natural Language processing has observed significant advancements in the development of sophisticated conversational Artificial Intelligence systems. ChatGPT is one such state-of-the-art conversational system that has attracted considerable interest and adoption. It enables developers to create highly interactive and engaging conversational applications using deep neural networks to produce human-like responses to user inputs. Such capabilities have made it popular in the threat actors' world. However, threat actors can abuse this chatbot to generate attack vectors as part of an operation. ChatGPT can be abused to produce practical and realistic communications that can be used in phishing attacks. These communications help the attack vectors distribution, i.e., prompt users to download and set up malware or disclose confidential information. ChatGPT has security measures to prevent malicious queries from generating attack vectors. However, the threat actors can circumvent such security controls through deception. This abusive use of ChatGPT makes the supply chain management of attack vectors effective and efficient. In this study, we presented evidence from various sources, showing how ChatGPT is abused to help the threat actors to improve each step of the attack vectors' supply chain management.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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A Fast Approach for Generating Unidentifiable Adversarial Samples

A Fast Approach for Generating Unidentifiable Adversarial Sa...

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Frontiers Technology of Information and computer (ICFTIC), ieee international conference on

作者： Yunfei Jia Risheng Guo Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

In recent years, Deep Neural Networks (DNN) have been widely used to solve many complicated tasks. However, several literatures have reported small perturbations in the target can obviously injury the performance of DNN system. This hinders the wide applications of DNN system in some safety-critical scenarios. Previous studies on adversarial samples generation usually arbitrarily and blindly to change all pixels to some extent. Unfortunately, the results can be identified by naked eyes although they can fool the DNN system. But in some cases, DNN system is just an auxiliary tool, people will also be employed to validate the identified results. This paper proposed a fast approach to generating adversarial samples with slightly changed pixels, with the purpose of deceiving people’s eyes. Firstly, the important pixels which makes more impact on output are identified by layer-by-layer weight analysis; secondly, these pixels are changed slightly by a fast gradient ascent method to check the output of DNN system; finally, the indicator of imperceptibility is used to valuation the change into an eye unrecognizable extent. Two well-known datasets are employed to validate the effectiveness of our approach.

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Apply machine learning and image processing to detect plant diseases

Apply machine learning and image processing to detect plant ...

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ieee Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Shraddha Chourasiya Aman Ashok Gadpale Paramveer Singh Thethi Pratik D. Nagdeve Pratiksha Wakode Muskan Indorkar Computer science engineering Jhulelal institute of technology Nagpur India

There are a number of issues that farmers are currently facing, including those related to climate change and the production of crops. One problem is that farmers may not be aware of diseases that can affect their plants, leading to poor growth. The Internet of Things (IoT) can help address this issue by connecting physical objects, such as sensors and actuators, to the internet. This allows for better collaboration and monitoring of tasks, as well as improved visibility into the status and growth of plants. IoT devices can be placed on farm fields to detect diseases in plants and alert farmers to any issues. These devices can also provide information about the climate and offer suggestions for addressing problems. A wireless sensor network (WSN) can be used to monitor plants for diseases and gather data about the environment. An automatic learning decision support system can be implemented to manage the disease detection process and provide reliable results. This innovative approach can help farmers make informed decisions about their crops and improve the overall efficiency of their operations This paper highlights the importance of using technology in agriculture to improve efficiency and outcomes. By implementing IoT solutions, farmers can better anticipate and prepare for changes in the climate and monitor the health and growth of their crops. This allows for more informed decision making and can ultimately lead to increased productivity and success in the field.

关键词： Productivity Wireless sensor networks Plant diseases Crops Sensors Internet of Things Reliability

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RFD-ECNet: Extreme Underwater image Compression with Reference to Feature Dictionary

RFD-ECNet: Extreme Underwater Image Compression with Referen...

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international conference on computer Vision (ICCV)

作者： Mengyao Li Liquan Shen Peng Ye Guorui Feng Zheyin Wang Shanghai Universit Shanghai China Fudan University Shanghai China

Thriving underwater applications demand efficient extreme compression technology to realize the transmission of underwater images (UWIs) in very narrow underwater bandwidth. However, existing image compression methods achieve inferior performance on UWIs because they do not consider the characteristics of UWIs: (1) Multifarious underwater styles of color shift and distance-dependent clarity, caused by the unique underwater physical imaging; (2) Massive redundancy between different UWIs, caused by the fact that different UWIs contain several common ocean objects, which have plenty of similarities in structures and semantics. To remove redundancy among UWIs, we first construct an exhaustive underwater multi-scale feature dictionary to provide coarse-to-fine reference features for UWI compression. Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet) 1 is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs. To align the multifarious underwater styles and improve the accuracy of feature match, an underwater style normalized block (USNB) is proposed, which utilizes underwater physical priors extracted from the underwater physical imaging model to normalize the underwater styles of dictionary features toward the input. Moreover, a reference feature variant module (RFVM) is designed to adaptively morph the reference features, improving the similarity between the reference and input features. Experimental results on four UWI datasets show that our RFD-ECNet is the first work that achieves a significant BDrate saving of 31% over the most advanced VVC.

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DEdgeNet: Extrinsic Calibration of Camera and LiDAR with Depth-discontinuous Edges

DEdgeNet: Extrinsic Calibration of Camera and LiDAR with Dep...

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ieee international conference on Robotics and Automation (ICRA)

作者： Yiyang Hu Hui Ma Leiping Jie Hui Zhang Faculty of Science and Technology United International College BNU-HKBU Zhuhai China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Department of Computer Science Hong Kong Baptist University Hong Kong China

This paper addresses the problem of calibrating extrinsic parameter matrix between an RGB camera and a LiDAR. Multimodal sensing systems are essential for fully autonomous navigation platforms. A key pre-requisite for such a system is calibration between different sensors. As the two most widely equipped sensors, calibration between RGB cameras and LiDARs remains challenging. Existing methods address this problem without using explicit geometric priors. In this paper, we propose a novel real-time network that utilizes depth-discontinuous edges extracted from a single image to calibrate cameras and LiDARs. Our network consists of two key components: (1) a self-supervised edge extraction network named DEdgeNet, which detects depth-discontinuous edges from a single image and extracts corresponding features; (2) prediction of the extrinsic parameter matrix between the camera and the LiDAR by matching fixed features in RGB images and updating depth features in a coarse-to-fine frame. Specifically, considering that edges are rich and common in natural scenes, DEdgeNet simplifies RGB image encoding and extracts fixed edges for feature matching. We conducted extensive experiments on the KITTI-odometry dataset. The results show that our method achieves an average rotation error of 0.028° and an average translation error of 0.247 cm, which demonstrates the superiority of our method.

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Philippine Coin Detection System Using Mask R-CNN Algorithm

Philippine Coin Detection System Using Mask R-CNN Algorithm

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Isagani V. Villamor Mary Lee D. Apelo David Angelo C. Ascan School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

This paper introduces a Philippine Coin Detection System utilizing the Mask R-CNN Algorithm for accurate classification and denomination of 1, 5, 10, and 20 peso coins. image preprocessing techniques, such as Gaussian smoothing and Canny edge detection, are applied prior to classification. With a 70 % accuracy for multiple coin detection, the evaluation reveals specific coin accuracies: 1-peso (90%), 5-peso (87.5%), 10-peso (85 %), and 20-peso (92.5 %), with an overall accuracy and precision of 77.5 % and 78.33 % respectively. The study signifies a significant advancement in automated coin recognition systems with potential implications for financial automation and related applications.

关键词： Accuracy Smoothing methods Automation image edge detection Humanoid robots Classification algorithms image preprocessing

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Exploring image-Based Approaches for Effective Synthetic Time-Series Data Generation

Exploring Image-Based Approaches for Effective Synthetic Tim...

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7th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 ieee.

关键词： Photointerpretation

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ASSD: Synthetic Speech Detection in the AAC Compressed Domain

ASSD: Synthetic Speech Detection in the AAC Compressed Domai...

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

作者： Amit Kumar Singh Yadav Ziyue Xiang Emily R. Bartusiak Paolo Bestagini Stefano Tubaro Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

Synthetic human speech signals have become very easy to generate given modern text-to-speech methods. When these signals are shared on social media they are often compressed using the Advanced Audio Coding (AAC) standard. Our goal is to study if a small set of coding metadata contained in the AAC compressed bit stream is sufficient to detect synthetic speech. This would avoid decompressing of the speech signals before analysis. We call our proposed method AAC Synthetic Speech Detection (ASSD). ASSD extracts information from the AAC compressed bit stream without decompressing the speech signal. ASSD analyzes the information using a transformer neural network. In our experiments, we compressed the ASVspoof2019 dataset according to the AAC standard using different data rates. We compared the performance of ASSD to a time domain based and a spectrogram based synthetic speech detection methods. We evaluated ASSD on approximately 71k compressed speech signals. The results show that our proposed method typically only requires 1000 bits per speech block/frame from the AAC compressed bit stream to detect synthetic speech. This is much lower than other reported methods. Our method also had a 9.7 percentage points higher detection accuracy compared to existing methods.

关键词： Social networking (online) Audio coding Neural networks Metadata Transformers Speech synthesis Data mining

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