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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2186 条记录，以下是551-560 订阅

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Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch 24

Learning Cross-Modal Deep Embeddings for Multi-Object Image ...

引用

24th International conference on pattern recognition (ICPR)

作者： Dey, Sounak Dutta, Anjan Ghosh, Suman K. Valveny, Ernest Llados, Josep Pal, Umapada Autonomous Univ Barcelona Comp Vis Ctr Dept Comp Sci Barcelona Spain Indian Stat Inst CVPR Unit Kolkata India

ISBN: (纸本)9781538637883

In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.

关键词： Image retrieval Image representation Visualization Feature extraction Image edge detection computer vision Standards

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Automatic Facial Attractiveness Prediction by Deep Multi-Task Learning 24

Automatic Facial Attractiveness Prediction by Deep Multi-Tas...

引用

24th International conference on pattern recognition (ICPR)

作者： Gao, Lian Li, Weixin Huang, Zehua Huang, Di Wang, Yunhong Beihang Univ Sch Comp Sci & Engn IRIP Lab Beijing 100191 Peoples R China Beihang Univ Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China TuSimple San Diego CA USA

ISBN: (纸本)9781538637883

Facial Attractiveness Prediction (FAP) is a useful yet challenging problem in the domain of computer vision. In this paper, we propose a deep learning based approach. Different from the existing deep methods, the proposed one models both the texture and shape clues within a multi-task learning framework consisting of attractiveness score prediction and fiducial landmark localization, thus highlighting both of their roles in assessing attractiveness of faces. Considering that the training data are not extensive, a lightweight CNN is designed to jointly learn the facial representation, landmark location, and facial attractiveness score. the proposed method is evaluated on the SCUT-FBP database, and a prediction correlation 0.92, is delivered, which shows the effectiveness of our method. Furthermore, two additional experiments in terms of comparison between facial images before and after make-up or beautification are conducted. the results also prove the advantage of the proposed method.

关键词： facial attractiveness prediction facial beauty analysis deep learning multi-task learning landmark detection

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Research on intelligent insect species identification based on computer vision 7

Research on intelligent insect species identification based ...

引用

7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Huang, Bin Nanchang Institute of Technology Jiangxi Nanchang330044 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

Image-based insect species identification is a comprehensive application of computer vision technology, image processing technology and pattern recognition technology to realize insect species identification. It is of great significance to find and identify crop insects in time and quickly, so as to take corresponding measures to prevent and control pests in time and reduce the losses caused by pests. In this paper, intelligent insect species identification based on computer vision is studied. For insect images, using feature fusion technology will obtain more comprehensive insect image features, which will greatly improve the recognition accuracy and improve the recognition efficiency of the system. In the learning process, firstly, a certain number of insect images are selected as training samples, and the feature vectors of insects are extracted as the input vectors of SVM(support vector machine) training, and the input vectors are learned to get the best classification model. the experimental results show that the average recognition rate of insects by feature fusion is 94.747%. the experimental results show that the research results of this paper provide theoretical and practical basis for intelligent insect species recognition technology based on computer vision. © 2023 SPIE. All rights reserved.

关键词： Support vector machines

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HardMo: A Large-Scale Hardcase Dataset for Motion Capture

HardMo: A Large-Scale Hardcase Dataset for Motion Capture

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liao, Jiaqi Luo, Chuanchen Du, Yinuo Wang, Yuxi Yin, Xucheng Zhang, Man Zhang, Zhaoxiang Peng, Junran Beijing Univ Posts & Telecommun Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China Qinghai Univ Sci & Technol Xining Qinghai Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent years have witnessed rapid progress in monocular human mesh recovery. Despite their impressive performance on public benchmarks, existing methods are vulnerable to unusual poses, which prevents them from deploying to challenging scenarios such as dance and martial arts. this issue is mainly attributed to the domain gap induced by the data scarcity in relevant cases. Most existing datasets are captured in constrained scenarios and lack samples of such complex movements. For this reason, we propose a data collection pipeline comprising automatic crawling, precise annotation, and hardcase mining. Based on this pipeline, we establish a large dataset in a short time. the dataset, named HardMo, contains 7M images along with precise annotations covering 15 categories of dance and 14 categories of martial arts. Empirically, we find that the prediction failure in dance and martial arts is mainly characterized by the misalignment of hand-wrist and foot-ankle. To dig deeper into the two hardcases, we leverage the proposed automatic pipeline to filter collected data and construct two subsets named HardMo-Hand and HardMo-Foot. Extensive experiments demonstrate the effectiveness of the annotation pipeline and the data-driven solution to failure cases. Specifically, after being trained on HardMo, HMR, an early pioneering method, can even outperform the current state of the art, 4DHumans, on our benchmarks. Dataset will be publicly available at https://***/ ***.

关键词： Hardcase scenes Human Mesh Recovery

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STATISTICAL pattern recognition FOR REAL-TIME IMAGE EDGE DETECTION ON FPGA 12

STATISTICAL PATTERN RECOGNITION FOR REAL-TIME IMAGE EDGE DET...

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12th IEEE International conference on Signal Processing (ICSP)

作者： Liu, Ziyan Qi, Jia Feng, Liang Feng, Li Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China State Grid Chongqing Elect Power Co Chongqing 400014 Peoples R China

ISBN: (纸本)9781479921867

Image edge detection is a fundamental process in computer vision. Image edges represent the major fraction of information in an image. Traditional edge-detection techniques focus on the gradient calculation method. In this paper, for the first time, the statistical pattern recognition method is used to detect the edge after the real-time image was processed via the median filtering method and implemented on FPGA. In comparison to the Sobel algorithm, the proposed method has superior anti-noise capability.

关键词： Statistical pattern recognition Median Filtering FPGA Real-Time Image Edge Detection

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A Video Face recognition Leveraging Temporal Information Based on vision Transformer 6th

A Video Face Recognition Leveraging Temporal Information Bas...

引用

6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Zhang, Hui Yang, Jiewen Dong, Xingbo Lv, Xingguo Jia, Wei Jin, Zhe Li, Xuejun Anhui Univ Sch Comp Sci & Technol Anhui Prov Int Joint Res Ctr Adv Technol Med Imag Hefei 230093 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Anhui Univ Sch Artificial Intelligence Anhui Prov Key Lab Secure Artificial Intelligence Hefei 230093 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Hefei 230009 Peoples R China

ISBN: (纸本)9789819984688;9789819984695

Video face recognition (VFR) has gained significant attention as a promising field combining computer vision and artificial intelligence, revolutionizing identity authentication and verification. Unlike traditional image-based methods, VFR leverages the temporal dimension of video footage to extract comprehensive and accurate facial information. However, VFR heavily relies on robust computing power and advanced noise processing capabilities to ensure optimal recognition performance. this paper introduces a novel length-adaptive VFR framework based on a recurrent-mechanism-driven vision Transformer, termed TempoViT. TempoViT efficiently captures spatial and temporal information from face videos, enabling accurate and reliable face recognition while mitigating the high GPU memory requirements associated with video processing. By leveraging the reuse of hidden states from previous frames, the framework establishes recurring links between frames, allowing the modeling of long-term dependencies. Experimental results validate the effectiveness of TempoViT, demonstrating its state-of-the-art performance in video face recognition tasks on benchmark datasets including iQIYI-ViD, YTF, IJB-C, and Honda/UCSD.

关键词： Video face recognition vision Transformer Temporal information

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A Fast Local Analysis by thresholding applied to image matching 24

A Fast Local Analysis by Thresholding applied to image match...

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24th International conference on pattern recognition (ICPR)

作者： Faula, Yannick Bres, Stephane Eglin, Veronique Univ Lyon INSA Lyon LIRIS UMR5205 F-69621 Lyon France

ISBN: (纸本)9781538637883

Key structures extraction and matching are key steps in computer vision. Many fields of application need large image acquisition and fast extraction of fine structures. In this study, we focus on situations where existing local feature extractors give not enough satisfying results concerning both accuracy and time processing. Among good illustrations, we can quote short-line extraction in local weakly-contrasted images. We propose a new Fast Local Analysis by threSHolding (FLASH) designed to process large images under hard time constraints. We use "micro-line" points as key feature. these are used for shape reconstruction (like lines) and local signature design. We apply FLASH on the field of concrete infrastructure monitoring where robots and UAVs are more and more used for automated defect detection (like cracks). For large concrete surfaces, there are several hard constraints such as the computational time and the reliability. Results show us that the computations are faster than several existing algorithms in image matching and FLASH has invariance to rotation, partial occlusion, and scale range from 0.7 to 1.4 without scale-space exploration.

关键词： Image matching

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2022 7th International conference on Image, vision and Computing, ICIVC 2022

2022 7th International Conference on Image, Vision and Compu...

引用

7th International conference on Image, vision and Computing, ICIVC 2022

ISBN: (纸本)9781665467346

the proceedings contain 153 papers. the topics discussed include: robust facial expression recognition based on dual branch multi-feature learning;review of human violence recognition algorithms;interlaced perception for person re-identification based on Swin transformer;skeleton-based dumbbell fitness action recognition using two-stream LSTM network;human action recognition based on three-stream network with frame sequence features;combining attention mechanism and dual-stream 3d convolutional neural network for micro-expression recognition;a robust approach for smile recognition via deep convolutional neural networks;a named entity recognition method based on deep learning for chinese legal documents;modality-independent regression and training for improving multispectral pedestrian detection;textile defect detection algorithm based on unsupervised learning;an optimization scheme of object detection model based on CNN feature visualization method;and a crosswalk stripe detection model based on gradient similarity tags.

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Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements 24

Fast Motion Deblurring for Feature Detection and Matching Us...

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24th International conference on pattern recognition (ICPR)

作者： Mustaniemi, Janne Kannala, Juho Sarkka, Simo Matas, Jiri Heikkila, Janne Univ Oulu Ctr Machine Vis & Signal Anal Oulu Finland Aalto Univ Helsinki Finland Czech Tech Univ Dept Cybernet Ctr Machine Percept Prague Czech Republic

ISBN: (纸本)9781538637883

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. the limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. the evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.

关键词： Feature extraction

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A Novel Model for Multi-label Image Annotation 24

A Novel Model for Multi-label Image Annotation

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24th International conference on pattern recognition (ICPR)

作者： Wu, Xinjian Zhang, Li Li, Fanzhang Wang, Bangjun Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Soochow Univ Joint Int Res Lab Machine Learning & Neuromorph C Prov Key Lab Comp Informat Proc Technol Suzhou Peoples R China

ISBN: (纸本)9781538637883

Multi-label image annotation is one of the most important open problems in machine learning and computer vision. In this paper, we propose a novel model for image annotation. Unlike existing works that usually use conventional visual features to annotate images, this paper adopts features based on convolutional neural network (CNN), which have shown potential to achieve outstanding performance. In particular, we use CNN to extract image features with higher semantic meaning and apply them to the image annotation method - Tag Propagation (TagProp). Experimental results on four challenging datasets indicate that our model makes a marked improvement as compared to the current state-of-the-art.

关键词： image annotation convolutional neural network Tag Propagation Multi-label learning

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