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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1671 条记录，以下是1011-1020 订阅

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White Matter Hyperintensities Segmentation in a Few Seconds Using Fully Convolutional Network and Transfer Learning 3rd

引用

3rd international Workshop on Brain-Lesion (BrainLes) held jointly at the conference on Medical image Computing for computer Assisted Intervention (MICCAI)

作者： Xu, Yongchao Geraud, Thierry Puybareau, Elodie Bloch, Isabelle Chazalon, Joseph EPITA Res & Dev Lab LRDE Le Kremlin Bicetre France Univ Paris Saclay Telecom ParisTech LTCI Palaiseau France Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Univ La Rochelle La Rochelle France

ISBN: (纸本)9783319752389;9783319752372

In this paper, we propose a fast automatic method that segments white matter hyperintensities (WMH) in 3D brain MR images, using a fully convolutional network (FCN) and transfer learning. This FCN is the Visual Geometry Group neural network (VGG for short) pre-trained on imageNet for natural image classification, and fine tuned with the training dataset of the MICCAI WMH Challenge. We consider three images for each slice of the volume to segment: the T1 slice, the FLAIR slice, and the result of a morphological operator that emphasizes small bright structures. These three 2D images are assembled to form a 2D color image, that inputs the FCN to obtain the 2D segmentation of the corresponding slice. We process all slices, and stack the results to form the 3D output segmentation. With such a technique, the segmentation of WMH on a 3D brain volume takes about 10 s including pre- processing. Our technique was ranked 6-th over 20 participants at the MICCAI WMH Challenge.

关键词： 3D brain MRI Lesion segmentation White matter hyperintensities Mathematical morphology Fully convolutional network

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Study on Brittle Graphite Surface Roughness Detection Based on Gray-Level Co-occurrence Matrix

Study on Brittle Graphite Surface Roughness Detection Based ...

引用

international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Li Zhou Hanzhang Liu Xiaopeng Zhuang Dawei Liu School of Electromechanical Engineering Guangdong Polytechnic Normal University Guangzhou China

The brittle graphite machined surface quality cannot be completely evaluated only by surface roughness Ra measured by profilometer, owing to its 3D surface defects. A digital image detection method based on gray-level co-occurrence matrix was put forwards to detect the graphite surface quality. The results show that two feature parameters of gray-level co-occurrence matrix, including Secondary moment ASM and Entropy ENT, had good positive relevance with surface roughness Ra. The linear regression functions of ASM and ENT were established to fit Ra, which could be used to predict Ra by image processing method. This method also gives a feasible way to develop a machine vision detection system to automatically detect the graphite surface quality by digital image processing.

关键词： Handheld computers Surface treatment image processing Entropy Correlation

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Classification of Cataract Slit-Lamp image Based on Machine Learning 3

Classification of Cataract Slit-Lamp Image Based on Machine ...

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3rd international Seminar on Application for Technology of Information and Communication, iSemantic 2018

作者： Sigit, Riyanto Kom, M. Bayu Satmoko, Maulana Kurnia Basuki, Dwi Si, S. Kom, M. Informatic and Computer Departement Electronic Engineering Polytechnic Institute of Surabaya Surabaya-East Java Indonesia

ISBN: (纸本)9781538674864

Cataracts are diseases caused by the presence of proteins in the lens that form abnormal and gradually enlarged clumps that will interfere with vision by blocking the light entering through the lens. Identification of cataracts is done by taking the image of the eye with a slit-lamp tool from the front of the eye. Slit-lamp images can provide information about the condition of the pupils that can only be analyzed by the doctor manually based on doctor's observation and doctor's experience that can cause different analysis in determining the actual eye condition. Things that are considered by the doctor in analyzing cataracts are the level of opacity in the eyes and the area covered by the turbid. Identification and classification with slit-lamp images can be performed better and more accurately using image processing techniques. Firstly, the grayscale method, median filter method and canny method is used to preprocess the slit-lamp images. Next, the hough circular method is used to automatically segment pupil from slit-lamp images. After the segmentation process, we use pixel scanning to extract mean intensity and uniformity from the pupil image. After the feature extraction process, classification is done by single perceptron based on the extracted feature. This research is expected to help the doctor to do cataracts classification so that the classification process will be easier and more accurate. Based on the test result show that the accuracy of the system is 96.6%. © 2018 IEEE.

关键词： Feature extraction

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Filter based approach for automated detection of candidate lung nodules in 3D computed tomography images 3rd

Filter based approach for automated detection of candidate l...

引用

3rd international conference on Cognitive Computing and Information processing, CCIP 2017

作者： Bhavanishankar, K. Sudhamani, M.V. RNS Institute of Technology Bengaluru India

ISBN: (纸本)9789811090585

Cancer is considered to be one of the threat causing disease to human health. Amongst the various types of cancers, the one which originates in lung is most fatal. Lung cancer appears in the form of nodules and is caused by anomalous growth of cells in the lung organ. Though detecting and diagnosing lung cancer at the early stages can enhance the survival rate, there are still numerous challenges. computer Aided Diagnosis (CAD) systems which makes use of various imaging modalities like Computed Tomography (CT) and several image processing techniques, assists the domain experts in the process of prognosis and treatment. A novel technique to detect the candidate lung nodule in 3D CT imagery is presented in this paper which employs minimum area filters and layer based filters designed to eliminate the irregularities, blood vessels and other nodules like lung artifacts. Finally the spherical and spiculated structures are retained by considering the diameter of components under observation. The algorithms are tested against 15 cases each having an average of 250 slices, obtained from LIDC. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Blood vessels

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Offline Signature Recognition Using Centroids of Local Binary Vectors 3rd

Offline Signature Recognition Using Centroids of Local Binar...

引用

3rd international conference on New Trends in Information and Communications Technology Applications (NTICT)

作者： Kamal, Nada Najeel George, Loay Edwar Univ Technol Baghdad Comp Sci Dept Baghdad Iraq Univ Baghdad Coll Sci Dept Comp Sci Baghdad Iraq

ISBN: (纸本)9783030016531;9783030016524

The Handwritten Signature is a special sign used by humans and may contain letters, curves, or both. The main usage of handwritten signature is a proof of identification, especially when dealing with official documents and treatments. To recognize a signature means to identify the person who uses this sign. Signature recognition has many applications, such as: transactions and checks in banking systems, forensic caseworks, personal authentication and verification. This work proposes a new method to recognize handwritten signature in an offline manner. The centroid of two local binary vectors, the horizontal vector and the vertical vector are calculated. Three different tests are accomplished for this method. Soft evaluation test, Hard evaluation test, and a Combined test. The gained results from the three of these tests are encouraging. It reached for what is so called the Combined test to 94.8275% of success rate for 928 digital images of handwritten signatures with processing time for single sample reaches to 0.146 in milliseconds.

关键词： Signature recognition Local vector Sub image Centroid Binary

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Residual Encoder and Convolutional Decoder Neural Network for Glioma Segmentation 3rd

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3rd international Workshop on Brain-Lesion (BrainLes) held jointly at the conference on Medical image Computing for computer Assisted Intervention (MICCAI)

作者： Pawar, Kamlesh Chen, Zhaolin Shah, N. Jon Egan, Gary Monash Univ Monash Biomed Imaging Melbourne Vic Australia Monash Univ Sch Psychol Sci Melbourne Vic Australia Monash Univ Elect & Comp Syst Engn Melbourne Vic Australia Res Ctr Juelich Inst Med Julich Germany

ISBN: (纸本)9783319752389;9783319752372

A deep learning approach to glioma segmentation is presented. An encoder and decoder pair deep learning network is designed which takes T1, T2, T1-CE (contrast enhanced) and T2-Flair (f luid attenuation inversion recovery) images as input and outputs the segmented labels. The encoder is a 49 layer deep residual learning architecture that encodes the 240 x 240 x 4 input images into 8 x 8 x 2048 feature maps. The decoder network takes these feature maps and extract the segmented labels. The decoder network is fully convolutional network consisting of convolutional and upsampling layers. Additionally, the input images are downsampled using bilinear interpolation and are inserted into the decoder network through concatenation. This concatenation step provides spatial information of the tumor to the decoder, which was lost due to pooling/downlsampling during encoding. The network is trained on the BRATS-17 training dataset and validated on the validation dataset. The dice score, sensitivity and specificity of the segmented whole tumor, core tumor and enhancing tumor is computed on validation dataset. The mean dice score for whole tumor, core tumor and enhancing tumor for validation dataset were 0.824, 0.627 and 0.575, respectively.

关键词： Deep learning image segmentation computer vision CNN

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A novel gradient based method for RGB-D human action recognition 3

A novel gradient based method for RGB-D human action recogni...

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2018 3rd international conference on Insulating Materials, Material Application and Electrical Engineering, IMMAEE 2018

作者： Tang, Haohao Zhang, Hanling College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

Most action recognition approaches proposed over the years that were designed for RGB sequences cannot utilize the rich 3D-structural information to reduce large intra-class variations. This paper addresses the issue of handling sole depth structural information for accurate human action recognition. It presents and evaluates the saliency based P-DmHOG features for human action representation. Saliency based P-DmHOG features are depth features inspired by the well-known HOG descriptor. Good results namely 96.78%, 97.78% and 93.13% are achieved on three public benchmark datasets: MSR Actions 3D, MSR Action Pairs 3D, and MSR Daily Activity 3D, which show the efficiency of proposed method to spatio-temporal and shape information. © 2018 Published under licence by IOP Publishing Ltd.

关键词： image processing

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Infrared Object Recognition Based on Monogenic Features and Multiple Kernel Learning

Infrared Object Recognition Based on Monogenic Features and ...

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international conference on image, vision and Computing (ICIVC)

作者： Chen Ning Wenbo Liu Xin Wang School of Physics and Technology Nanjing Normal University Nanjing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781538649923

Infrared object recognition is an important branch in the field of image processing and computer vision. This paper proposes a novel infrared object recognition method based on monogenic features and multiple kernel learning. Specifically, the proposed features are mainly derived from the ideas of the monogenic signal. The applicability of the monogenic signal within the field of infrared object recognition is demonstrated by its capability of capturing both the spectral information and spatial localization with compact support. Second, to reduce the dimensionalities of the monogenic features, the principal component analysis is applied. Third, the reduced monogenic features are adaptively fused in the multiple kernel learning framework. At last, a multiple kernel learning support vector machine classifier is designed for recognizing the infrared objects. The experimental results show that the proposed method leads to good infrared object recognition performance.

关键词： Kernel Object recognition Principal component analysis Feature extraction Support vector machines Band-pass filters Task analysis

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Design and Implementation of T-Hash Tree in Main Memory Data Base

Design and Implementation of T-Hash Tree in Main Memory Data...

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international conference on image, vision and Computing (ICIVC)

作者： Zhiqiang Hu Meiqi Hu General Department of network and information security Beijing Institute of Computer Technology and Applications Beijing China Technical service center Shenzhou General Data Technology Co. Ltd. Beijing China

ISBN: (纸本)9781538649923

There are some shortcomings for the cache sensitivity of the index in main memory database, so a new index structure is proposed. T -tree index is studied individually ever before, so as Hash index. Combined with the analysis of the two index structure, a new index structure called the T-Hash tree is introduced. Through analyzing the times of the T -Hash tree cache sensitive and testing the performance of the query, insert, delete operation, the results show that the T -Hash tree can effectively reduce the times of cache sensitive, and as the amount of the data is large, the query, insert, delete efficiency of the T -Hash tree is higher than the T tree.

关键词： Vegetation Indexes Query processing Electronic mail Memory management Keyword search

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An image Watermarking Algorithm Based on Energy Scheme in the Wavelet Transform Domain

An Image Watermarking Algorithm Based on Energy Scheme in th...

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international conference on image, vision and Computing (ICIVC)

作者： Jinhua Liu School of Mathematics and Computer Science Shangrao Normal University Shangrao China

ISBN: (纸本)9781538649923

With the increasing demands of copyright protection, digital watermarking has been paid more and more attention. In the design of a watermarking method, the modeling of signal by a general parametric family of statistical distributions plays an important role in many image watermarking applications. In this paper, the probability density function of wavelet coefficients is modeled by the generalized Gaussian distribution (GGD), and the decision threshold is obtained by the Neyman-Pearson (NP) criterion. In the procedure of watermark embedding, the energy of image block is considered in the watermark embedding. Only those blocks whose energy exceeds a predetermined threshold are used to embed the watermark data. Its improved robustness is due to embedding in the significant wavelet coefficients based on the energy scheme and control of its strength factor from the variance of coefficient. Experimental results demonstrate that the effectiveness of the presented watermarking and its robustness against common image processing and some kinds of geometric attacks.

关键词： Watermarking Robustness Wavelet coefficients Discrete wavelet transforms Gaussian distribution

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