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5th asian conference on pattern recognition,acpr 2019

5th Asian Conference on Pattern Recognition,ACPR 2019

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5th asian conference on pattern recognition,acpr 2019

ISBN: (纸本)9789811536502

the proceedings contain 23 papers. the special focus in this conference is on pattern recognition. the topics include: vi-MoCoGAN: A Variant of MoCoGAN for Video Generation of Human Hand Gestures Under Different Viewpoints;weakly Supervised Domain Adaptation with Point Supervision in Histopathological Image Segmentation;blood Vessel Enhancement in Liver Region from a Sequence of Angiograms Taken under Free Breathing;real-Time Morphing of the Visible Man Liver with Intrahepatic Vasculatures;development of 3D Physiological Simulation and Education Software for Pregnant Women;resolution Conversion of Volumetric Array Data for Multimodal Medical Image Analysis;learning Spatiotemporal Representation Based on 3D Autoencoder for Anomaly Detection;multi-view Discriminant Analysis for Dynamic Hand Gesture recognition;human Action recognition Based on Dual Correlation Network;Bypass Enhancement RGB Stream Model for Pedestrian Action recognition of Autonomous Vehicles;feature Engineering Workflow for Activity recognition from Synchronized Inertial Measurement Units;Infrared-Image Processing for the DLR FireBIRD Mission;temperature Dependence of Dark Signal for Sentinel-4 Detector;an Extended Stochastic Cloning Method for Fusing Multi-relative Measurements;paved and Unpaved Road Segmentation Using Deep Neural Network;image Scene Conversion Algorithm Based on Generative Adversarial Networks;An Embedded Real-Time Monocular SLAM System Utilizing a Dynamically Reconfigurable Processor;writer Identification Based on Combination of Bag of Words Model and Multiple Classifiers;vehicle-Related Scene Understanding Using Deep Learning;spatiotemporal Saliency Based Multi-stream Networks for Action recognition;attention Guided Unsupervised Image-to-Image Translation with Progressively Growing Strategy.

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5th asian conference on pattern recognition, acpr 2019

5th Asian Conference on Pattern Recognition, ACPR 2019

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5th asian conference on pattern recognition, acpr 2019

ISBN: (纸本)9783030412982

the proceedings contain 123 papers. the special focus in this conference is on pattern recognition. the topics include: Continual Learning of Image Translation Networks Using Task-Dependent Weight Selection Masks;a Real-Time Eye Tracking Method for Detecting Optokinetic Nystagmus;network Structure for Personalized Face-Pose Estimation Using Incrementally Updated Face-Shape Parameters;optimal Rejection Function Meets Character recognition Tasks;comparing the recognition Accuracy of Humans and Deep Learning on a Simple Visual Inspection Task;improved Gamma Corrected Layered Adaptive Background Model;one-Shot Learning-Based Handwritten Word recognition;first-Person View Hand Parameter Estimation Based on Fully Convolutional Neural Network;dual-Attention Graph Convolutional Network;enhancing Open-Set Face recognition by Closing It with Cluster-Inferred Gallery Augmentation;chart-Type Classification Using Convolutional Neural Network for Scholarly Figures;handwritten Digit String recognition for Indian Scripts;spatial-Temporal Graph Attention Network for Video-Based Gait recognition;supervised Interactive Co-segmentation Using Histogram Matching and Bipartite Graph Construction;Using Deep Convolutional LSTM Networks for Learning Spatiotemporal Features;two-Stage Fully Convolutional Networks for Stroke Recovery of Handwritten Chinese Character;text Like Classification of Skeletal Sequences for Human Action recognition;Background Subtraction Based on Encoder-Decoder Structured CNN;multi Facet Face Construction;Automated 2D Fetal Brain Segmentation of MR Images Using a Deep U-Net;action recognition in Untrimmed Videos with Composite Self-attention Two-Stream Framework;EEG Representations of Spatial and Temporal Features in Imagined Speech and Overt Speech;GAN-based Abnormal Detection by Recognizing Ungeneratable patterns;modality-Specific Learning Rate Control for Multimodal Classification;3D Multi-frequency Fully Correlated Causal Random Field Texture Model;meaning Guided

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5th asian conference on pattern recognition, acpr 2019

5th Asian Conference on Pattern Recognition, ACPR 2019

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5th asian conference on pattern recognition, acpr 2019

ISBN: (纸本)9783030414030

the proceedings contain 123 papers. the special focus in this conference is on pattern recognition. the topics include: A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation;robust Pedestrian Detection: Faster Deployments with Fusion of Models;perceptual Image Anomaly Detection;deep Similarity Fusion Networks for One-Shot Semantic Segmentation;seeing things in Random-Dot Videos;boundary Extraction of Planar Segments from Clouds of Unorganised Points;real-Time Multi-class Instance Segmentation with One-Time Deep Embedding Clustering;multi-person Pose Estimation with Mid-Points for Human Detection under Real-World Surveillance;gaze from Head: Gaze Estimation Without Observing Eye;interaction recognition through Body Parts Relation Reasoning;label-Smooth Learning for Fine-Grained Visual Categorization;DeepHuMS: Deep Human Motion Signature for 3D Skeletal Sequences;towards a Universal Appearance for Domain Generalization via Adversarial Learning;pre-trained and Shared Encoder in Cycle-Consistent Adversarial Networks to Improve Image Quality;MobileGAN: Compact Network Architecture for Generative Adversarial Network;denoising and Inpainting of Sea Surface Temperature Image with Adversarial Physical Model Loss;confidence Map Based 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching;optical Flow Assisted Monocular Visual Odometry;coarse-to-Fine Deep Orientation Estimator for Local Image Matching;geometric Total Variation for Image Vectorization, Zooming and Pixel Art Depixelizing;information theory-Based Curriculum Learning Factory to Optimize Training;forestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees;A Factorization Strategy for Tensor Robust PCA;speeding up of the Nelder-Mead Method by Data-Driven Speculative Execution;efficient Bayesian Optimization Based on Parallel Sequential Random Embeddings.

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Dewarping Document Image in Complex Scene by Geometric Control Points 7th

Dewarping Document Image in Complex Scene by Geometric Con...

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7th asian conference on pattern recognition, acpr 2023

作者： Li, Run-Xi Yin, Fei Huang, Lin-Lin Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031476648

In the process of document digitization, document images captured by mobile devices suffer from physical distortion, which is detrimental to subsequent document processing. Geometric information of the distorted document images provide global and local constraints that can assist in document dewarping. In this paper, we propose a novel document dewarping method which focus on utilizing the geometric control points such as document boundaries and textlines. Specifically, our method first extracts the boundary source control points and textline source control points and predicts their corresponding forward mapping as target control points. Eventually the sparse mapping between control points is converted into a dense backward mapping by thin Plate Splines interpolation. Our method can obtain the backward mapping directly and explicitly by interpolation between control points, without solving the time-consuming optimization problem. Quantitative and qualitative evaluation show that our method can dewarp document images with various distortion types, and improve the inference speed by a factor of three over the existing geometric element based rectification methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Interpolation

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Joint Semantic Segmentation and Edge Detection for Waterline Extraction 6th

Joint Semantic Segmentation and Edge Detection for Waterline...

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6th asian conference on pattern recognition (acpr)

作者： Chen, Yuhang Ni, Bolin Meng, Gaofeng Sha, Baoyin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HK Inst Sci & Innovat Beijing Peoples R China Coal Sci & Technol Res Inst Co Ltd Beijing 100013 Peoples R China

ISBN: (纸本)9783031023750;9783031023743

Automatic water gauge reading is very important for cargo weighting in ocean transportation. In this process, accurate waterline extraction is an important yet challenging step. Waterline extraction is subjected to many environmental interference factors, e.g., bad illumination, bad weather conditions, ambiguous contours of water stain, etc. In this paper, we propose a joint multitask based deep model for accurate waterline extraction. the proposed model consists of two main branches. One branch is used to extract high-level contextual information. the other branch rooted in shallow layers is used to extract low-level detail features. the two branches are later coupled with each other to co-supervise the estimation of waterline. Our model works well on various conditions, such as uneven light, serious reflections, etc. We also introduce a new benchmark dataset for waterline extraction. this dataset consists of 360 pictures extracted from 69 videos collected in several actual ports. Furthermore, sufficient experiments show that our model is effective on the introduced dataset and outperforms the state-of-the-art methods.

关键词： Waterline extraction Joint multitask Co-supervise Benchmark dataset

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DCT-DWT-FFT Based Method for Text Detection in Underwater Images 1

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6th asian conference on pattern recognition (acpr)

作者： Banerjee, Ayan Shivakumara, Palaiahnakote Pal, Soumyajit Pal, Umapada Liu, Cheng-Lin Indian Stat Inst Comp Vis & Pattern Recognit Unit Kolkata India Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur Malaysia Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9783031024443

ISBN: (纸本)9783031024443;9783031024436

Text detection in underwater images is an open challenge because of the distortions caused by refraction, absorption of light, particles, and variations depending on depth, color, and nature of water. Unlike existing methods aimed at text detection in natural scene images, in this paper, we have proposed a novel method for text detection in underwater images through a new enhancement model. Based on observations that fine details of text in image share with high energy, spatial resolution, and brightness, we consider Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Fast Fourier Transform (FFT) for image enhancement to highlight the text features. the enhanced image is fed to a modified Character Region Awareness for Text Detection (CRAFT) model to detect text in underwater images. To explore enhancement methods, we evaluate six combinations of image enhancement techniques, namely, DCT-DWT-FFT, DCT-FFT-DWT, DWT-DCT-FFT, DWT-FFT-DCT, FFT-DCT-DWT, FFT-DWTDCT. Experimental results on our dataset of underwater images and benchmark datasets of natural scene text detection, namely, MSRA-TD500, ICDAR 2019 MLT, ICDAR 2019 ArT, Total-Text, CTW1500, and COCO Text show that the proposed method performs well for both underwater and natural scene images and outperforms the existing methods on all the datasets.

关键词： Under water images Text detection in underwater images Image enhancement Discrete cosine transform Wavelet transform Fourier transform Modified Character Region Awareness for Text Detection (CRAFT)

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Multi-Zone Transformer Based on Self-Distillation for Facial Attribute recognition 17

Multi-Zone Transformer Based on Self-Distillation for Facial...

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17th IEEE International conference on Automatic Face and Gesture recognition, FG 2023

作者： Chen, Si Zhu, Xueyan Wang, Da-Han Zhu, Shunzhi Wu, Yun Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9798350345445

Recently, transformers have shown great promising performance in various computer vision tasks. However, the current transformer based methods ignore the information exchanges between transformer blocks, and they have not been applied in the facial attribute recognition task. In this paper, we propose a multi-zone transformer based on self-distillation for FAR, termed MZTS, to predict the facial attributes. A multi-zone transformer encoder is firstly presented to achieve the interactions of the different transformer encoder blocks, thus avoiding forgetting the effective information between the transformer encoder block groups during the iteration process. Furthermore, we introduce a new self-distillation mechanism based on class tokens, which distills the class tokens obtained from the last transformer encoder block group to the other shallow groups by interacting with the significant information between the different transformer blocks through attention. Extensive experiments on the challenging CelebA and LFWA datasets have demonstrated the excellent performance of the proposed method for FAR. © 2023 IEEE.

关键词： Face recognition

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Semantic-Aware Non-local Network for Handwritten Mathematical Expression recognition 5th

Semantic-Aware Non-local Network for Handwritten Mathematica...

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Liu, Xiang-Hao Wang, Da-Han Du, Xia Zhu, Shunzhi Xiamen Univ Technol Sch Comp Sci & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9783031189128;9783031189135

Handwritten mathematical expression recognition (HMER) is a challenging task due to its complex two-dimensional structure of mathematical expressions and the high similarity between handwritten texts. Most existing encoder-decoder approaches for HMER mainly depend on local visual features but are seldom studied in explicit global semantic information. Besides, existing works for HMER primarily focus on local information. However, this obtained information is difficult to transmit between distant locations. In this paper, we propose a semantic-aware non-local network to tackle the above problems for HMER. Specifically, we propose to adopt the non-local network to capture long-term dependencies while integrating local and non-local features. Moreover, we customized the FastText language model to our backbone to learn the semantic-aware information. the experimental results illustrate that our design consistently outperforms the state-of-the-art methods on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and 2016 datasets.

关键词： Handwritten mathematical expression recognition FastText language model Non-local neural network

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Critical Radical Analysis Network for Chinese Character recognition 26

Critical Radical Analysis Network for Chinese Character Reco...

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26th International conference on pattern recognition / 8th International Workshop on Image Mining - theory and Applications (IMTA)

作者： Luo, Guo-Feng Yin, Hua-Yi Wang, Da-Han Zhang, Xu-Yao Zhu, Shun-Zhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit NLPR Inst Automat Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Zero-shot learning is a challenging problem in many tasks due to the lack of training samples of the unseen classes. the radical-based zero-shot Chinese character recognition methods treat Chinese characters as a combination of radicals and structures, and recognize Chinese characters by identifying the radicals and structures contained in them. Current approaches generally treat the contribution of all radicals to Chinese character recognition as the same, and the recognition results rely on the network's ability to recognize radicals and their corresponding position information, ignoring the potential value of radicals themselves in eliminating the uncertainty of Chinese characters. In this paper, we model the problem of radical-based Chinese character recognition as an uncertainty elimination problem and propose a Critical Radical Analysis Network (CRAN) to explore the Ideographic Description Sequence (IDS) information for zero-shot Chinese character recognition. Specifically, we propose a novel method to compute the critical values of radicals based on information theory using the predefined Chinese character IDS dictionary. In recognition, we use an iterative approach to translate the predicted radical sequence to target Chinese characters. that is, the radicals of the predicted sequence are sorted in descending order of the critical value, and then the radicals are continuously selected in this order as the information obtained to eliminate the uncertainty of the Chinese character until the character is recognized. We conduct experiments on the CTW, CASIA-AHCDB, and CASIA-HWDB datasets. the experimental results show that the proposed method improves the ability of recognizing unseen Chinese characters, demonstrating the effectiveness of the proposed method.

关键词： Zero-shot learning Chinese character recognition

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Joint Pixel-Level and Feature-Level Unsupervised Domain Adaptation for Surveillance Face recognition 5th

Joint Pixel-Level and Feature-Level Unsupervised Domain Adap...

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5th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Zhu, Huangkai Yi, Huayi Xia, Du Wang, Da-Han Liu, Xianghao Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9783031189128;9783031189135

Face recognition (FR) is one of the most successful image analysis and understanding applications, which has recently received significant attentions. However, despite the remarkable progress in face recognition-related technologies, dependably recognizing surveillance faces is still a big challenge due to different data distributions between commonly used faces and surveillance faces. Although collecting labeled surveillance faces could be direct and helpful, it is infeasible due to privacy and labor cost. In comparison, it is practical to use the unsupervised domain adaptation (UDA) method to regularize the learning of surveillance face representations by utilizing small-scale unlabeled data. In this paper, a joint pixel-level and feature-level UDA framework has been proposed for tacking the above problems. Specifically, we introduce a training method of domain adversarial to align the domain gap in feature space. Additionally, we propose to use a CycleGAN-based image style transformer to capture pixel-level domain shifts. Moreover, we adopt feature identity-consistency loss on the original CycleGAN to alleviate the identity attribute shift caused by unstable image style transfer. the experimental results on the self-built SFace-450 dataset demonstrate that our method consistently outperforms the state-of-the-art FR and UDA methods.

关键词： Surveillance face recognition Unsupervised domain adaptation Image style transform

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