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检索条件"任意字段=6th International Conference on Computer Vision and Image Processing, CVIP 2021"

共 1236 条记录，以下是1-10 订阅

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6th international conference on computer vision and image processing, cvip 2021

6th International Conference on Computer Vision and Image Pr...

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6th international conference on computer vision and image processing, cvip 2021

ISBN: (纸本)9783031113451

the proceedings contain 97 papers. the special focus in this conference is on computer vision and image processing. the topics include: Automatic Classification of Sedimentary Rocks Towards Oil Reservoirs Detection;signature2Vec - An Algorithm for Reference Frame Agnostic Vectorization of Handwritten Signatures;leaf Segmentation and Counting for Phenotyping of Rosette Plants Using Xception-style U-Net and Watershed Algorithm;fast and Secure Video Encryption Using Divide-and-Conquer and Logistic Tent Infinite Collapse Chaotic Map;visual Localization Using Capsule Networks;detection of Cataract from Fundus images Using Deep Transfer Learning;brain Tumour Segmentation Using Convolution Neural Network;signature Based Authentication: A Multi-label Classification Approach to Detect the Language and Forged Sample in Signature;a Data-Set and a Real-Time Method for Detection of Pointing Gesture from Depth images;Deep-TDRS: An Integrated System for Handwritten Text Detection-Recognition and Conversion to Speech Using Deep Learning;vision HELPER: CNN Based Real Time Navigator for the Visually Impaired;structure-Texture Decomposition-Based Enhancement Framework for Weakly Illuminated images;low Cost Embedded vision System for Location and Tracking of a Color Object;towards Label-Free Few-Shot Learning: How Far Can We Go?;AB-net: Adult- Baby Net;Polarimetric SAR Classification: Fast Learning with k-Maximum Likelihood Estimator;Leveraging Discriminative Cues for Masked Face Recognition in Post COVID World;pretreatment Identification of Oral Leukoplakia and Oral Erythroplakia Metastasis Using Deep Learning Neural Networks;soft Biometric Based Person Retrieval for Burglary Investigation;a Deep Learning Framework for the Classification of Lung Diseases Using Chest X-Ray images;computer Aided Diagnosis of Autism Spectrum Disorder Based on thermal Imaging;scene Graph Generation with Geometric Context;deep Color Spaces for Fingerphoto Presentation Attack Detection in Mobile Devices;can

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6th international conference on computer vision and image processing, cvip 2021

6th International Conference on Computer Vision and Image Pr...

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6th international conference on computer vision and image processing, cvip 2021

ISBN: (纸本)9783031113482

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5th international conference on computer vision and image processing, cvip 2020

5th International Conference on Computer Vision and Image Pr...

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5th international conference on computer vision and image processing, cvip 2020

ISBN: (纸本)9789811610912

the proceedings contain 134 papers. the special focus in this conference is on computer vision and image processing. the topics include: Age and Gender Prediction Using Deep CNNs and Transfer Learning;Text Line Segmentation: A FCN Based Approach;precise Recognition of vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network;human Gait Abnormality Detection Using Low Cost Sensor Technology;Bengali Place Name Recognition - Comparative Analysis Using Different CNN Architectures;action Recognition in Haze Using an Efficient Fusion of Spatial and Temporal Features;face Verification Using Single Sample in Adolescence;evaluation of Deep Learning Networks for Keratoconus Detection Using Corneal Topographic images;deep Facial Emotion Recognition System Under Facial Mask Occlusion;domain Adaptation Based Technique for image Emotion Recognition Using image Captions;gesture Recognition in Sign Language Videos by Tracking the Position and Medial Representation of the Hand Shapes;deepDoT: Deep Framework for Detection of Tables in Document images;Correcting Low Illumination images Using PSO-Based Gamma Correction and image Classifying Method;DeblurRL: image Deblurring with Deep Reinforcement Learning;FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality;enhancement of Region of Interest from a Single Backlit image with Multiple Features;human Action Recognition from 3D Landmark Points of the Performer;real-Time Sign Language Interpreter on Embedded Platform;complex Gradient Function Based Descriptor for Iris Biometrics and Action Recognition;on-Device Language Identification of Text in images Using Diacritic Characters;a Pre-processing Assisted Neural Network for Dynamic Bad Pixel Detection in Bayer images;preface;dynamic User Interface Composition.

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5th international conference on computer vision and image processing, cvip 2020

5th International Conference on Computer Vision and Image Pr...

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5th international conference on computer vision and image processing, cvip 2020

ISBN: (纸本)9789811611025

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5th international conference on computer vision and image processing, cvip 2020

5th International Conference on Computer Vision and Image Pr...

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5th international conference on computer vision and image processing, cvip 2020

ISBN: (纸本)9789811610851

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computer-Generated image Forensics Based on vision Transformer with Forensic Feature Pre-processing Module 6th

Computer-Generated Image Forensics Based on Vision Transfor...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Chen, Yifang Wen, Guanchen Wang, Yong Yang, Jianhua Zhang, Yu Guangdong Polytechnic Normal University GuangDong GuangZhou510665 China

ISBN: (纸本)9783031736988

the correct distinction between highly realistic computer-generated (CG) images and photographic (PG) images has become an important area of research. In recent years, most of the CG image forensics methods are proposed based on deep learning, but the detection performances of these methods still need to be improved, especially in terms of robustness and generalization. To tackle these issues, we leverage the vision Transformer (ViT) model, which excels in capturing the global features of images, and design a Forensic Feature Pre-processing (FFP) module to further improve the detection performance. Experiments are conducted on a large-scale CG image benchmark (LSCGB), which is a challenging dataset for CG image detection. the proposed approach can achieve high detection accuracy. Extensive experiments on different public datasets and common post-processing operations demonstrate our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： computer forensics

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Multichannel Object Detection with Event Camera 6

Multichannel Object Detection with Event Camera

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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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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

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6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. these methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. these features include both semantic and high-level representations essential for accurate object detection. this paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 IEEE.

关键词： Convolutional neural networks

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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...

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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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Wide Line Segments Detection in Grey-Level images via Guided Scale Space Radon Transform 6

Wide Line Segments Detection in Grey-Level Images via Guided...

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6th IEEE international conference on image processing, Applications and Systems, IPAS 2025

作者： Goumeidane, Aicha Baya Ziou, Djemel Nacereddine, Nafaa Yala, Nawal CRTI Research Center in Industrial Technologies Algiers Algeria Université de Sherbrooke Département D'Informatique Québec Canada

ISBN: (纸本)9798331506520

Line segment detection is a fundamental procedure in computer vision, pattern recognition, and image analysis applications. the paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. the method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. the method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising. © 2025 IEEE.

关键词： image segmentation

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