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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3294 条记录，以下是1-10 订阅

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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5th International conference on Data Science, machine Learning and applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption Computer vision Natural Language processing Attention Mechanism Performance

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Annotation Tools for Computer vision Tasks 17

Annotation Tools for Computer Vision Tasks

引用

17th International conference on machine vision, ICMV 2024

作者： Moschidis, Christos Vrochidou, Eleni Papakostas, George A. Department of Informatics Democritus University of Thrace Kavala65404 Greece

ISBN: (纸本)9781510688278

Common computer vision (CV) tasks include image classification, object detection, segmentation, and recognition. To handle such tasks, machine learning (ML) models for image processing require a great amount of annotated training data. While datasets are expanding in size and variety, annotation becomes demanding, since its quality can severely affect the models' performance. Thus, several annotation tools have been developed and designated for specific applications and model requirements. This work aims to provide an overview of the most up-to-date annotation tools for computer vision tasks, including 2D and 3D image data and video, comparatively highlighting their advantages and limitations. The appropriateness of each tool for specific tasks is emphasized, providing a reference map for researchers towards determining the annotation tool best tailored to their needs. Future trends in image annotation are also discussed. © 2025 SPIE.

关键词： image annotation

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Multichannel Object Detection with Event Camera 6

Multichannel Object Detection with Event Camera

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Iliasov, Rafael Golkar, Alessandro Technical University of Munich Spacecraft Systems Munich Germany

ISBN: (纸本)9798331506520

object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5. © 2025 IEEE.

关键词： machine vision

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Blur Patch Classification Approach to Single-image Depth Estimation 17

Blur Patch Classification Approach to Single-Image Depth Est...

引用

17th International conference on machine vision, ICMV 2024

作者： Kim, Huijun Lee, Deokwoo Keimyung University Dalgubeol-daero Dalseo-gu Daegu1095 Korea Republic of

ISBN: (纸本)9781510688278

Depth information is useful in many image processing and computer vision applications, but in photography, depth information is lost in the process of projecting a real-world scene onto a 2D plane. Extracting depth information from such images is a challenging task. In this paper, we propose a method to train a deep neural network to classify an image patch (16x16 in size) into 15 levels based on the level of blur. Blur is related to the distance between the focal plane and the object. The input image is shifted using a sliding window technique at 8 pixel intervals and the trained blur classifier evaluates each blur level. The obtained blur maps are subjected to a refinement process to quantitatively assess their accuracy and impact on the final result, and the final blur maps are compared with the labels of the actual input data to estimate the depth map. The proposed method demonstrates that depth information can be successfully extracted from a single image by classifying the focus levels. © 2025 SPIE.

关键词： image classification

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3D Information in Robot vision System Based on Artificial Neural Network 14th

3D Information in Robot Vision System Based on Artificial Ne...

引用

14th International conference on Frontier Computing, FC 2024

作者： Liu, Xiaoxiao Department of Mechanical and Electrical Engineering Jinan Engineering Vocational Technical College Shandong Jinan China

ISBN: (纸本)9789819627974

In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human visual system, this technology enables robots to more effectively interpret and understand the three-dimensional spatial information of their environment, which has a revolutionary role in robot navigation, object recognition, obstacle avoidance and the execution of complex tasks. And this technology shows great potential in many fields such as industrial automation, autonomous vehicles, drone monitoring, and service robots. Not only that, it also plays a very important role in providing in-depth information, accurate positioning and efficient decision-making applications. According to the experimental data, among the 800 experimental subjects, more than 754 people were satisfied with the recognition accuracy, recognition speed, machine efficiency improvement, image recognition clarity, and overall satisfaction with the system. These findings indicate that with the continuous advancement of technology, 3D vision systems based on artificial neural networks will show more significant performance and value in future applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： machine vision

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control 6

Authentication and Verification in Human-Robot Cooperative R...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

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Realistic and visually-Pleasing 3D Generation of Indoor Scenes from a Single image 7th

Realistic and Visually-Pleasing 3D Generation of Indoor Scen...

引用

7th Chinese conference on Pattern Recognition and Computer vision

作者： Li, Jie Wang, Lei Chen, Gongbin Li, Ang Qiu, Yuhao Wu, Jiaji Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci CAS Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.

关键词： 3D mesh Reconstruction Deep neural networks

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

引用

5th International conference on Data Science, machine Learning and applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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Bridging the machine-Human Gap in Blurred-image Classification via Entropy Maximisation 6

Bridging the Machine-Human Gap in Blurred-Image Classificati...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Sansano-Sansano, Emilio Martinez-Garcia, Marina Portilla, Javier Universitat Jaume I INIT Castellón de la Plana Spain Universitat Jaume I IMAC Castellón de la Plana Spain CSIC Instituto de Óptica Madrid Spain

ISBN: (纸本)9798331506520

Recent studies point to an accuracy gap between humans and Artificial Neural Network (ANN) models when classifying blurred images, with humans outperforming ANNs. To bridge this gap, we introduce a spectral channel-based range-constrained entropy merit function, from which we devise a zero-phase, circular symmetric blind deblurring method. We apply it as a pre-processing step for image classification and test it using pre-trained classification models and images blurred by Gaussian kernels. We compare our method to state-of-the-art restoration methods, showing its superiority, effectively bridging the machine-human gap for most models and blur levels. Our results also rank higher than the competitors in no-reference and full-reference image quality metrics. Notwithstanding the limitation to zero-phase blur, this work shows that, for image pre-processing aimed at visual tasks, it may be advantageous to use merit functions based on vision science and information theory, rather than on the expected error to the latent image. © 2025 IEEE.

关键词： Constrained optimization

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