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SSRN 2024年

作者： Wang, Hairong Wang, Tong Wang, Yiyan Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

A B S T R A C TTo solve the visual semantic understanding bias and multimodal semantic bias in multimodal named entity recognition, the Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition (CLGLF) method is proposed. This method invokes the BLIP-2 pre-trained model to generate image captions, concatenates them with the input texts, and performs joint coding to achieve multimodal feature fusion. The candidate labels and text labels are obtained after decoding the multimodal representations and text representations. Based on using the KL divergence loss function to align the two groups of labels, the confidence score is calculated to evaluate the quality of the multimodal representation, and a confidence threshold is set to help screen out the biased candidate labels, the text labels in the corresponding positions are used to replace the biased candidate labels, to achieve the label fusion, and finally complete the multimodal named entity recognition. In order to verify the proposed method, experiments are carried out on the Twitter-2015 and Twitter-2017 multimodal datasets, and the experimental results are compared with 7 mainstream methods, such as MSB and UMT. The experimental results show the effectiveness of the CLGLF method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Extraction of Human Body Skeleton Based on Silhouette images

Extraction of Human Body Skeleton Based on Silhouette Images

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International Workshop on Education Technology and Computer Science, ETCS

作者： Jianhao Ding Yigang Wang Lingyun Yu State Key Laboratory of CAD & CG University of Zhejiang Hangzhou China Institute of Computer graphics and Image Processing Hangzhou Dianzi University Hangzhou China Department of Mathematics and Computing Science University of Groningam Netherlands

ISBN: (纸本)9781424463886;9780769539874

Skeleton extraction is essential for general shape representation. A typical skeletonization algorithm should obtain the ability to preserve original object's topological and hierarchical properties. However, most of current methods are high memory cost, computationally intensive, and also require complex data structures. In this paper, we propose an efficient and accurate skeletonization method for the skeleton feature points extracted from human body based on silhouette images. First, the gradient of distance transform is used to detect critical points inside the foreground. Then, we converge and simplify critical points in order to generate the most important and elegant skeleton feature points. Finally, we present an algorithm which connects the skeleton feature points and estimates the position of skeleton joints.

关键词： Humans Skeleton Joints Iterative algorithms Euclidean distance Data mining Motion analysis image converters Shape Feature extraction

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Visual Semantic Enhancement for Multi-Modal Named Entity Recognition

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SSRN 2024年

作者： Wang, Tong Wang, Hairong Xu, Xi Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

In recent years, a series of methods have been proposed to use image semantics to assist in extracting named entities. However, in these multi-modal named entity recognition methods, there are problems of visual semantic missing and weak semantic constraints in multi-modal representation. Therefore, a visual semantic enhancement for multi-modal named entity recognition (EVS-MNER) method is proposed. In this method, multiple visual features are used for collaborative representation to obtain more complete visual semantics. The semantic information in multiple visual features is integrated through the multi-modal features fusion module to generate visual semantic features, and the semantics are passed to the text features to obtain semantically enhanced multi-modal text representation. The visual entity classifier is used to decode visual semantic features, and the semantic consistency constraint of visual features is realized. The multi-task label decoder is called to mine the fine-grained semantics of the multi-modal text representation and text features, and the problem of semantic bias is solved by joint decoding, to further improve the accuracy of named entity recognition. On two multimodal entity recognition datasets of Twitter-2015 and Twitter-2017, the proposed method is compared with 10 methods such as PCEN and M3S. The experimental results show that the proposed method is effective. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Content- and semantic-based progressive multimodal image retrieval

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ICIC Express Letters 2011年第1期5卷 201-206页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Zhang, Xiaoli Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University Economic and Technological Development Zone Liaohe Road Dalian 116600 China

image retrieval has been an active research topic due to its great importance in the field of image management, digital library and web searching. However, the performances of the existing approaches, including text-based or content-based image retrieval, are often unsatisfactory. Inspired by text-based image retrieval, we propose semantic-based approach, whose similarity computation is semantic relevance rather than word matching. In this paper, a novel approach of progressive image retrieval based on content and semantic is proposed. The proposed retrieval model overcomes the drawbacks of pure content-based and text-based image retrieval, which exploits the strengths of both semantic- and content-based methods, minimizing their weaknesses. Experiments demonstrate the effectiveness and efficiency of the proposed method for image retrieval. © 2011 ISSN.

关键词： Semantics

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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： image segmentation

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An effective coaxiality measurement for twist drill based on line structured light sensor

arXiv

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arXiv 2021年

作者： Cheng, Ailing Ye, Jiaojiao Yang, Fei Lu, Shufang Gao, Fei The Laboratory of Graphics & Image Processing College of Computer Science and Technology Zhejiang University of Technology Zhejiang Province Hangzhou310023 China The Research Center of Intelligent Computing Software Zhejiang Lab. Zhejiang Province Hangzhou311121 China

Aiming at the accurate and effective coaxiality measurement for twist drill with irregular surface, an optical measurement mechanism is proposed in this paper. First, A high-precision rotation instrument based on four core units is designed, which can obtain the 3-D point cloud data of full angle for the twist drill. Second, in the data processing stage, an improved robust Gaussian mixture model is established for accurate and rapid blade back segmentation. To improve measurement efficiency, a rapid reconstruction method of the twist drill axis based on orthogonal synthesis is provided to locate the axial position of the maximum deviation from the benchmark by utilizing the extracted blade back data. Finally, by calculating the maximum radial Euclidean distance from the benchmark, the coaxiality error of the twist drill is obtained. Comparing with other measurement methods, experimental results show that our proposed method is effective with high precision of 3 um and high efficiency of less than 3 s/pc. The result demonstrate that the proposed method is effective, robust and automatic, it can be applied in many actual industrial scene. © 2021, CC BY.

关键词： Drills

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Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

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Light(Science & Applications) 2015年第1期4卷 152-162页

作者： Antonio C Sobieranski Fatih Inci H Cumhur Tekin Mehmet Yuksekkaya Eros Comunello Daniel Cobra Aldo von Wangenheim Utkan Demirci Demirci Bio-Acoustic-MEMS in Medicine(BAMM)Laboratory Division of Biomedical EngineeringDivision of Renal MedicineDepartment of MedicineBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Demirci Bio-Acoustic-MEMS in Medicine(BAMM)Laboratory Division of Infectious DiseasesBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA INCoD–National Brazilian Institute for Digital Convergence/LAPIX Image Processing and Computer Graphics LabFederal University of Santa CatarinaBrazil Demirci Bio-Acoustic-MEMS in Medicine(BAMM)Laboratory Stanford University School of MedicineCanary Center at Stanford for Cancer Early DetectionPalo AltoCAUSA Present Address:Department of Biomedical Engineering Faculty of Engineering Bas¸kent UniversityAnkaraTurkey VisionLab Master in Applied ComputingUniversity of Itajaı´ValleyBrazil CERTI Foundation FlorianopolisBrazil

In this paper,an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame *** samples are illuminated by a nearly coherent illumination system,where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging *** increase the resolution,a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene,with small planar *** are resolved by a hybrid approach:(i)alignment of the LR images by a fast feature-based registration method,and(ii)fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum *** method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed *** presented approach was evaluated with various biological samples including sperm and platelets,whose dimensions are in the order of a few *** obtained results demonstrate a spatial resolution of 1.55 μm on a field-of-view of<30 mm^(2).

关键词： digital in-line holography image registration lensless imaging point-of-care platform pixel super-resolution

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Improved cross-media relevance model for automatic image annotation

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ICIC Express Letters, Part B: Applications 2012年第5期3卷 1231-1237页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Liu, Yongkui Xu, Lu Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Economic and Technological Development Zone-Dalian 116600 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China

Automatic image annotation has been an active research topic in the last decade due to its potentially large impact on image retrieval, object recognition and image understanding. Many approaches have been proposed for image annotation in recent years. The most influential annotation model is relevance model, whose representative algorithm is Cross-Media Relevance Model (CMRM). In this paper, to overcome the shortcomings of the original CMRM, we propose improved CMRM. In the improved CMRM, the annotation word distribution is estimated using binary probability, the significance of blob is discriminately depicted using TF-IDF (term frequency-inverse document frequency) schemes, and the similarity measurement of blob is based on blob and continuousvalued feature vector. The experimental results on the standard Corel5K dataset show that our proposed approach outperforms the original CMRM by about 111% and 73% in terms of F1 and N+ measures respectively. The improved CMRM can get better performance in specific domains such as cultural heritage and special necessities digitization for ethnic minorities. © 2012 ICIC International.

关键词： image retrieval

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Learning a nonlinear color distance metric for the identification of skin immunohistochemical staining

Learning a nonlinear color distance metric for the identific...

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2009 22nd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2009

作者： Sobieranski, Antonio Carlos Neto, Sylvio Luiz Mantelli Coser, Leandro Comunello, Eros Von Wangenheim, Aldo Cargnin-Ferreira, Eduardo Di Giunta, Gabriella LAPIX Laboratory of Image Processing and Computer Graphics UFSC Federal University of Santa Catarina Brazil CPTEC Center for Weather Forecasts and Environment Studies INPE - Brazilian Institute for Space Research Brazil EGC Knowledge Engineering Department UFSC - Federal University of Santa Catarina Brazil MCA Graduate Program in Applied Computer Science UNIVALI - Itajai Valley University Brazil Laboratory of Histological Markers Department of Biology Embriology and Genetics UFSC - Federal University of Santa Catarina Brazil Pathology Service UFSC - Federal University of Santa Catarina Brazil

ISBN: (纸本)9781424448784

This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained regions that will be used as a reference for the construction of a distance metric. In this learning process, the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the IHC pattern distributions. The results of the proposed method showed a high correlation to a pathologist's manual analysis, which was used as a golden standard, presenting a more robust discrimination between stained and non-stained areas with little bias. © 2009 IEEE.

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M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

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Computers in Biology and Medicine 2024年 173卷 108291-108291页

作者： Zhou, Tao Wang, Hongwei Du, Yuhu Liu, Fengzhen Guo, Yujie Lu, Huiling School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

Background: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. Methods: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. Results: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. Conclusion: The M3YOLOv5 model can effectiv

关键词： Fracture

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