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GLOBAL FOR COARSE AND PART FOR FINE: A HIERARCHICAL ACTION recognition FRAMEWORK

GLOBAL FOR COARSE AND PART FOR FINE: A HIERARCHICAL ACTION R...

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IEEE International conference on Image Processing

作者： Weiwei Liu Chongyang Zhang Jiaying Zhang Zhonghao Wu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

Action recognition is one significant yet challenging task in computer vision. Recent methods mainly model an end-to-end one-stage non-deep or deep learning networks to distinguish different action categories. In this paper we introduce one novel hierarchical action classification framework: Unlike existing one-stage recognition models, the proposed work improves the recognition accuracy by: 1) developing a hierarchical coarse-to-fine action classification framework by dividing the recognition processing into two stages: coarsegrained classification and fine-grained classification, and 2) representing actions in different stages with different granularity features representation: global features are utilized for coarse classifiers while more body parts patterns for fine-grained classifiers are aggregated. Experiments on two widely-tested benchmark datasets show that our method can achieve state-of-the-art or competitive performance compared with existing results using one-stage models, with advantages regarding the recognition accuracy and robustness.

关键词： Feature extraction pattern recognition Measurement Optical imaging Testing Task analysis Computational modeling

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A Comprehensive Data Augmentation Method for Modern Cigarette Packaging Defect Detection

A Comprehensive Data Augmentation Method for Modern Cigarett...

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pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Yanjin Ma Yunbo Rao Junmin Xue Qixue He School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Information Technology of Chinese Academy of Sciences Co. LTD Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

this study addresses the issue of insufficient quantity and incomplete types of defect detection datasets for cigarette packages in modern industrial production line scenarios. We propose a cigarette packaging defect data augmentation method. the method consists of three components: preprocessing for obtaining cigarette package defect data through segmentation and cropping based on Segment Anything Model(SAM), generation of cigarette package defects using Cigarette Box Defect-Improved Denoising Diffusion Probabilistic Model (CBD-IDDPM), and We expanded the original dataset by utilizing 600 generated images and trained different defect detection models using the augmented dataset. the experimental results validate the effectiveness of our dataset augmentation method for cigarette package defect detection. the mAP50-95 of YOLOv8n has increased from 73% to 75.2%.

关键词： Image segmentation Image synthesis Noise reduction Production Packaging Diffusion models Data augmentation Data models pattern recognition Defect detection

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Video-CT MAE: Self-supervised Video-CT Domain Adaptation for Vertebral Fracture Diagnosis 7

Video-CT MAE: Self-supervised Video-CT Domain Adaptation for...

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7th International conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Buess, Lukas Stollenga, Marijn F. Schinz, David Wiestler, Benedikt Kirschke, Jan S. Maier, Andreas Navab, Nassir Keicher, Matthias Technical University of Munich Germany Klinikum Rechts der Isar Technical University of Munich Germany Pattern Recognition Lab. Friedrich-Alexander-Universität Erlangen-Nürnberg Germany ImFusion GmbH Munich Germany

Early and accurate diagnosis of vertebral body anomalies is crucial for effectively treating spinal disorders, but the manual interpretation of CT scans can be time-consuming and error-prone. While deep learning has shown promise in automating vertebral fracture detection, improving the interpretability of existing methods is crucial for building trust and ensuring reliable clinical application. vision Transformers (ViTs) offer inherent interpretability through attention visualizations but are limited in their application to 3D medical images due to reliance on 2D image pretraining. To address this challenge, we propose a novel approach combining the benefits of transfer learning from video-pretrained models and domain adaptation with self-supervised pretraining on a task-specific but unlabeled dataset. Compared to naïve transfer learning from Video MAE, our method shows improved downstream task performance by 8.3 in F1 and a training speedup of factor 2. this closes the gap between videos and medical images, allowing a ViT to learn relevant anatomical features while adapting to the task domain. We demonstrate that our framework enables ViTs to effectively detect vertebral fractures in a low data regime, outperforming CNN-based state-of-the-art methods while providing inherent interpretability. Our task adaptation approach and dataset not only improve the performance of our proposed method but also enhance existing self-supervised pretraining approaches, highlighting the benefits of task-specific self-supervised pretraining for domain adaptation. the code is publicly available at https://***/lbuess/Video-CT_MAE. © 2024 CC-BY 4.0, L. Buess et al.

关键词： Self-supervised learning

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Paracatadioptric camera calibration using sphere images

Paracatadioptric camera calibration using sphere images

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IEEE International conference on Image Processing

作者： Huixian Duan Yihong Wu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

the problem of calibrating paracatadioptric camera from sphere images is still open. In this paper, we propose a calibration method for paracatadioptric camera based on spheres. We notice that, under central catadioptric camera, a sphere is projected to two conies on the image plane, which can also be seen as the projections of two parallel circles on the viewing sphere by a virtue camera. these two conies are called a pair of antipodal sphere images. Firstly, we study properties of K(K >; 3) pairs of antipodal sphere images under paracatadioptric camera. then, they can be estimated using these properties. Finally, paracatadioptric camera can be calibrated by three pairs of antipodal sphere images or more. the method only requires the projected contour of parabolic mirror is visible on the image plane in one view. Experimental results on both simulated and real image data have demonstrated the effectiveness of our method.

关键词： Cameras Calibration Mirrors Equations Robot vision systems computer vision Vectors

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On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation

On the results of the first mobile biometry (MOBIO) face and...

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20th International conference on pattern recognition, ICPR 2010

作者： Marcel, Sébastien McCool, Chris Matějka, Pavel Ahonen, Timo Černocký, Jan Chakraborty, Shayok Balasubramanian, Vineeth Panchanathan, Sethuraman Chan, Chi Ho Kittler, Josef Poh, Norman Fauve, Benoît Glembek, Ondřej Plchot, Oldřich Jančík, Zdeněk Larcher, Anthony Lévy, Christophe Matrouf, Driss Bonastre, Jean-François Lee, Ping-Han Hung, Jui-Yu Wu, Si-Wei Hung, Yi-Ping Machlica, Lukáš Mason, John Mau, Sandra Sanderson, Conrad Monzo, David Albiol, Antonio Nguyen, Hieu V. Bai, Li Wang, Yan Niskanen, Matti Turtinen, Markus Nolazco-Flores, Juan Arturo Garcia-Perera, Leibny Paola Aceves-Lopez, Roberto Villegas, Mauricio Paredes, Roberto Idiap Research Institute Switzerland University of Oulu Finland Brno University of Technology Czech Republic Center for Cognitive Ubiquitous Computing Arizona State University United States Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Validsoft Ltd. United Kingdom University of Avignon LIA France National Taiwan University Taiwan University of West Bohemia Czech Republic Swansea University United Kingdom NICTA Australia iTEAM Universidad Politecnica de Valencia Spain University of Nottingham United Kingdom Visidon Ltd. Finland Tecnologico de Monterrey Mexico Instituto Tecnológico de Informática Universidad Politécnica de Valencia Spain

ISBN: (纸本)3642177107

this paper evaluates the performance of face and speaker verification techniques in the context of a mobile environment. the mobile environment was chosen as it provides a realistic and challenging test-bed for biometric person verification techniques to operate. For instance the audio environment is quite noisy and there is limited control over the illumination conditions and the pose of the subject for the video. To conduct this evaluation, a part of a database captured during the "Mobile Biometry" (MOBIO) European Project was used. In total there were nine participants to the evaluation who submitted a face verification system and five participants who submitted speaker verification systems. the results have shown that the best performing face and speaker verification systems obtained the same level of performance, respectively 10.9% and 10.6% of HTER. © 2010 Springer-Verlag.

关键词： Face recognition

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Online Evaluation of Textile Yarn Evenness Using Fast Exposure Imaging and Image Processing Techniques

Online Evaluation of Textile Yarn Evenness Using Fast Exposu...

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Automation, Control and Robots (ICACR), International conference on

作者： Noman Haleem Matteo Bustreo Alessio Del Bue University of Groningen Leeuwarden The Netherlands Pattern Analysis & Computer Vision Istituto Italiano di Tecnologia Genova Italy

Evenness is regarded as the most important attribute of textile yarn quality as irregular yarns reduce efficiency of spinning process and deteriorate quality of resultant fabrics. In traditional yarn spinning process, evenness is tested offline using laboratory scale testers through sampling from a whole lot of yarns. the lag associated with this approach limits agility in responding to a variety of process variations whereas limited sampling restricts achieving fine grained yarn quality insights. these factors together result in limited control on yarn spinning process and eventually compromise quality of the yarn *** aim to address this problem by developing a new yarn evenness testing system, which can be deployed on an individual spinning position to evaluate yarn quality in an online manner. the proposed system, which is based on a combination of fast exposure imaging and image processing techniques, was first validated using a control set of yarn specimens and later tested in normal yarn production through comparison with an existing offline yarn evenness tester. the results demonstrate promising potential of the proposed system for wider application in yarn spinning industry.

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Human action recognition using topic model

Human action recognition using topic model

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International conference on Information Science and Technology (ICIST)

作者： Qian Xiao Jun Cheng Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong Shenzhen P. R. China Shenzhen Institutes of Advanced Technology CAS Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen P. R. China

In this paper, we propose a novel method to recognize actions by using latent topic model which can be used for fusion of multi-view HOG3D feature and location and recognition of local space-time interest point. the latent topic model describes the probability distribution of spatial-temporal words and the intermediate topics about the action categories so that we can tag each spatial-temporal word with action categories. that means we can easily localize and recognize multiple actions. In our method, we first extract the spatial-temporal interest point and the multi-view HOG3D feature of the cuboid around the interest point. then we construct the vocabulary and utilize the latent topic model to learn the intermediate topics. the proposed approach is tested on publicly available MSRAction3D dataset, revealing the advantages and the state-of-art performance of our method.

关键词： pattern recognition Hidden Markov models three-dimensional displays computer vision Visualization conferences Training

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kNN Hashing with Factorized Neighborhood Representation

kNN Hashing with Factorized Neighborhood Representation

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IEEE International conference on computer vision

作者： Kun Ding Chunlei Huo Bin Fan Chunhong Pan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467383929

Hashing is very effective for many tasks in reducing the processing time and in compressing massive databases. Although lots of approaches have been developed to learn data-dependent hash functions in recent years, how to learn hash functions to yield good performance with acceptable computational and memory cost is still a challenging problem. Based on the observation that retrieval precision is highly related to the kNN classification accuracy, this paper proposes a novel kNN-based supervised hashing method, which learns hash functions by directly maximizing the kNN accuracy of the Hamming-embedded training data. To make it scalable well to large problem, we propose a factorized neighborhood representation to parsimoniously model the neighborhood relationships inherent in training data. Considering that real-world data are often linearly inseparable, we further kernelize this basic model to improve its performance. As a result, the proposed method is able to learn accurate hashing functions with tolerable computation and storage cost. Experiments on four benchmarks demonstrate that our method outperforms the state-of-the-arts.

关键词： hashing Retrieval precision processing time Neighborhood basic model Training data Arts cryptography hashing method

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Dual Convolutional Neural Network with Explainability Attention Mechanism for Diabetic Retinopathy Classification

Dual Convolutional Neural Network with Explainability Attent...

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pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Xiang Feng Zixuan Chen Keliang Wu Jincheng Peng Happy Nkanta Monday Grace Ugochi Nneji Chengdu University of Technology Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Diabetic retinopathy (DR) is a worldwide chronic eye disease which is a complication due to long-term diabetes. DR can gradually reduce a patient's vision and even lead to blindness. therefore, in order to effectively prevent potential retinopathy, early diagnosis and timely intervention are particularly important. In recent years, with the rapid development of deep learning technology, convolutional neural network (CNN) has become an important analysis tool in the medical field. In this study, we propose a novel dual convolutional neural network model that combines the Convolutional Block Attention Module (CBAM) to enhance the feature extraction capability. Specifically, we fused the network “PD-NET” based on null convolution and pyramid pooling and the network “ResDSCM-NET” based on deeply separable convolution and multi-scale convolution to construct the final ensemble model. In order to verify the effectiveness of the proposed model, we conducted experiments using the DR dataset. the experimental results show that the model achieves 96.38% accuracy in detecting DR, which is significantly better than the existing methods. the results indicate that the proposed model has high accuracy and potential for early diagnosis of DR and is expected to play an important role in clinical applications.

关键词： Deep learning Diabetic retinopathy Accuracy Attention mechanisms Convolution Blindness Feature extraction pattern recognition Convolutional neural networks Medical diagnostic imaging

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Precise Image Editing with Multimodal Agents

Precise Image Editing with Multimodal Agents

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pattern recognition and Artificial Intelligence (PRAI), International conference on

作者： Bin Fu Chi Zhang Fukun Yin Pei Cheng Zebiao Huang School of Information Science and Technology Fudan University China PCG Tencent Shanghai China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

the rapid advancements in large language models (LLMs) have revolutionized the field of artificial intelligence, enabling the development of sophisticated agents capable of performing complex tasks. the emergence of multimodal LLMs, such as GPT-4 vision, has further expanded the possibilities by allowing agents to process and understand visual data directly. However, current end-to-end solutions for image content editing often fall short in terms of stability, precision, and interpretability. Motivated by these limitations, we propose a novel framework that leverages the capabilities of multimodal agents to execute precise image editing tasks in a sequential and logical manner. Our approach integrates a comprehensive suite of advanced image editing tools into the action space of the agent, enabling it to interact directly with these tools through their APIs. Additionally, we introduce a reflection mechanism that empowers the agent to critically assess its own editing output and refine its actions accordingly. through extensive experimental validation across diverse image editing scenarios, we demonstrate the effectiveness and superiority of our approach over existing end-to-end methodologies. Our contributions include the development of a versatile and flexible multimodal agent framework for image editing, the establishment of a new benchmark for precision and reliability in AI-driven image editing, and the laying of groundwork for future research in the integration of cognitive capabilities and visual perception in AI.

关键词： Visualization Image synthesis Large language models Refining Reflection Stability analysis pattern recognition Reliability thermal stability Visual perception

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