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Real-time Facial Features Tracker Based on Topologic Information and Similarity Analysis

电子学报(英文版) 2008年第2期17卷 205-209页

作者： LIANG Ronghua FANG Jinglong MAO Jianfei ZHOU Delong College of Information Engineering Zhejiang University of Technology Hangzhou 310014 China State Key Laboratory of Audiovisual Information Processing Peking University Beijing 100871 China Institute of Graphics and Image Hangzhou Dianzi Universtity Hangzhou 310018 China

Real-time facial features tracking of video can be widely used in face recognition, video surveillance, face animation and Human-Computer Interaction. We present a fast tracking approach, and our method only requires simple device - a digital camera and a PC, and our approach needs limited user interactions. We first use eigenface and topologic information to detect the position and the size of face from the first frame, and facial features of the first frame are acquired automatically. The successor of the first frame can be tracked by using similarity analysis and motion estimation, the automatic tracker of first frame is also used to resolve the features occlusion problem when the tracked features disappear which is a difficult issue for tracking. Experimental results show that our approach is easily implemented, and the analysis also shows the high robustness of our method.

关键词：拓扑信息相似分析特征跟踪运动评估

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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

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Computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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A fast pavement location approach for autonomous car navigation 19

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19th Iberoamerican Congress on Pattern Recognition, CIARP 2014

作者： Rateke, Thiago Justen, Karla A. Chiarella, Vito F. Linhares, Rodrigo T. F. Sobieranski, Antonio Carlos Comunello, Eros von Wangenheim, Aldo Federal University of Santa Catarina Brazil Laboratory of Image Processing and Computer Graphics National Brazilian Institute for Digital Convergence Brazil Federal University of Parana Department of Informatics Brazil

ISBN: (纸本)9783319125671

This paper describes a fast image segmentation approach designed for pavement detection in a moving camera. The method is based on a graph-oriented segmentation approach where gradient information is used temporally as a system of discontinuities to control merging between adjacent regions. The method presumes that the navigable path usually is located at specific positions on the scene, and a predefined set of seed points is used to locate the region of interest. The obtained results shown the proposed approach is able to accurately detect in an inexpensive computation manner the navigable path even in non-optimum scenarios such as miss-painted or unpaved dirt roads. Validation was conducted using a dataset with 701 samples of navigable paths, presenting a very high precision for real-time applications. © Springer International Publishing Switzerland 2014.

关键词： Pavements

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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Computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Computer-aided diagnosis of pneumonia based on deep learning is a research ***,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this *** main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale *** MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features ***,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature ***,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different *** verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried *** the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,*** the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,*** model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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Color skin segmentation based on non-linear distance metrics 19

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19th Iberoamerican Congress on Pattern Recognition, CIARP 2014

作者： Sobieranski, Antonio Carlos Chiarella, Vito F. Barreto-Alexandre, Eduardo Linhares, Rodrigo T. F. Comunello, Eros von Wangenheim, Aldo Federal University of Santa Catarina Laboratory of Image Processing and Computer Graphics Brazil Federal University of Parana Department of Informatics Brazil University of Sao Paulo Department of Computer Science Brazil

ISBN: (纸本)9783319125671

In this paper we present a semiautomatic method for skin identification in video sequences. The user trains the system by selecting in a frame some typical positive skin pixels, that will be used as a reference for the construction of a nonlinear 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 skin pattern distributions. These nonlinear features are then used to classify the frames captured from the camera, identifying all skin and non-skin regions on the scene. We adopt an estrategy which enables this method to run in real-time after some iteractions. We also compare our classification method against vector norm (L2) and Mahalanobis distance, showing a better classification for the skin patterns. © Springer International Publishing Switzerland 2014.

关键词： Artificial intelligence

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SYSTOLIC ARRAY FOR ALL-NEAREST-NEIGHBORS PROBLEM

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ELECTRONICS LETTERS 1987年第24期23卷 1259-1260页

作者： PETKOV, N International Basic Laboratory for Image Processing & Computer Graphics Central Institute for Cybernetics & Information Processes Berlin East Germany

The all-nearest-neighbours (ANN) problem is a fundamental problem in computational geometry. In the letter a new two-dimensional triangular systolic array with mesh-connected cells is proposed for the ANN problem. This array can process a queue of ANN problems at a throughput of one ANN problem per time period, with an efficiency of 100%.

关键词： Computer architecture computational geometry throughput Other numerical methods ANN problem cellular arrays all-nearest-neighbours problem parallel architectures two-dimensional triangular systolic array efficiency mesh-connected cells

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Named Entity Recognition in Chinese Judicial Domain Based on Self-attention mechanism and IDCNN 8

Named Entity Recognition in Chinese Judicial Domain Based on...

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8th International Conference on Digital Home, ICDH 2020

作者： Huang, Wenming Zhang, Juan Xiao, Yannan Han, Zheng Deng, Zhenrong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin China Guilin Bank Guilin China

ISBN: (纸本)9781728192345

Chinese named entity recognition (CNER) in the judicial domain is an important basic task for intelligent analysis and processing of massive documents. This domain entity has more complicated structure than the common named entity, and its entity category is more abundant. However, the general method can not solve the problem of domain specific identification. In this paper, we combine self-attention mechanism and iteration dilated convolution neural network (IDCNN) for CNER in judicial domain. The bidirectional gate recurrent unit (BiGRU) model is used to automatically learn the context semantic information of the text and solve the long-distance dependence of the sequence. The model introduce the IDCNN to extract the key features of context semantic information, and capture finer-grained semantic information in underlying texts. The self-attention mechanism is used to analyze the relationship between characters, and the problem of long sequence semantic dilution is effectively solved by means of dynamic weight, and the optimal tag sequence is calculated by integrating conditional random fields (CRF), which further improves the recognition ability of the model. Finally, by analyzing the characteristics of legal documents, the new data set is annotated and the fine-grained named entity recognition is realized. The experimental results on our corpus show that the proposed method can effectively identify the entities in legal documents, and improve performance in the judicial field. © 2020 IEEE.

关键词： Semantics

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TSM resilient audio watermarking using IMFs

TSM resilient audio watermarking using IMFs

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Saif AlZahir Md Wahedul Islam Image Processing and Graphics Laboratory Computer Science Department UNBC BC Canada

With widespread of the Internet as well as the numerous applications that involve audio signals and systems, the issue of ownership proof become imperative. In this paper, we present an audio watermark scheme that is based on Empirical Mode Decomposition and Hilbert Huang Transform. The audio signal is decomposed into several mono-component signals or Intrinsic Mode Functions to serve as the addressee for watermark. The watermark is a pseudo random number which is added to the highest and lowest Intrinsic Mode Functions of the signal to generate what we call the modified IMFs which is in turn added to the remaining IMFs to produce the watermarked signal. Our experimental results show that the proposed method is robust against signal processing attacks such as MP3, time scale modification, resizing, and others. The obtained results meet the recommended imperceptibility signal to noise ratio.

关键词： Watermarking Robustness Signal to noise ratio Transforms Digital audio players Detectors

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Generic Visualization and Manipulation Framework for Three-Dimensional Medical Environments

Generic Visualization and Manipulation Framework for Three-D...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： B.S. Tani T. Nobrega T.R. dos Santos A. von Wangenheim Computer Graphics and Image Processing Laboratory Federal University of Santa Catarina Brazil

We present a visualization and manipulation framework for three-dimensional medical environments. The MVC architecture of the. framework allows the use of different API technologies such us OpenGL and DirectSD, different model representations, and controllers. This facilitates the implementation of medical applications offering resources commonly needed by such systems. Two case studies are shown as examples of such applications

关键词： Biomedical imaging Data visualization Computer architecture Engines Medical control systems Computer displays Medical services Biomedical equipment Application software Solid modeling

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