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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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The Method of AGV Detection and Cargo Status Recognition Based on Globe Vision

The Method of AGV Detection and Cargo Status Recognition Bas...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Junting Chen Xiang Liu Yiwen Liu Lili Chen Wei Lu School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai China

In order to solve the problem of limited vision in the process of AGV detection and cargo state identification in logistics factories, this paper proposed a method based on globe vision for AGV detection and cargo status recognition. Firstly, the fast video stitching was realized by calculating the initial frame stitching parameters and the fusion parameter matrix. Then image data were collected and labeled, and YOLO V3 model with good performance was trained by adjusting super parameters. Finally, the trained YOLO V3 model was used to realize AGV detection and cargo status recognition on the basis of globe vision. The experimental results showed that the recognition accuracy of this proposed method reached 99.65% and the detection speed reached 28-30 frames/s, which could meet the accuracy and real-time requirements in the task of AGV detection and cargo status recognition in logistics factories.

关键词： Streaming media Real-time systems Production facilities Cameras Navigation Logistics Feature extraction

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DDNet: A Novel Network for Cerebral Artery Segmentation from MRA images

DDNet: A Novel Network for Cerebral Artery Segmentation from...

引用

international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yaxian Zhang Li Chen School of Software Tsinghua University Beijing China

Brain vascular segmentation is a very important step in the diagnosis and treatment of brain diseases. Since manual segmentation of the vascular system of the brain is laborious and has problems with accuracy, automatic or semi-automatic cerebrovascular segmentation is of great significance for the study of brain diseases. Compared to hand-defined features, deep learning methods are widely used in medical image processing due to their superior feature expression capabilities. Many of the existing deep learning-based blood vessel segmentation methods do not consider multi-scale information of blood vessels, and the extracted context information has a small receptive field. Besides, the network parameters are large and difficult to train. In this paper, we proposed a network named Dense-Dilated Neural Network (DDNet) for the segmentation of cerebral artery in TOF-MRA images. The backbone of DDNet is U-Net, consisting of an encoding path, a decoding path and the skip connections between the two paths. The network is able to obtain a multi-scale representation of the brain arterial network, and has a lager respective field. In addition, the final brain arterial network segmentation results can be obtained with less computational effort. Experiments show that the network proposed in this paper is superior to UNet, Vnet and Uception.

关键词： image segmentation Convolution Feature extraction biomedical imaging Arteries Kernel

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Study on Scrolling Display Method for Submarine Geomorphologic Map 11

Study on Scrolling Display Method for Submarine Geomorpholog...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu Tianyang Xu Weiming Dalian Naval Acad Dept Mil Oceanog & Hydrog & Cartog Dalian Peoples R China Naval Inst Hydrog Surveying & Charting Tianjin Peoples R China

ISBN: (纸本)9781538676042

this paper proposes a scrolling display method for submarine geomorphologic map based on multi-core architecture. Focusing on the slow display speed of high-resolution submarine geomorphologic map, this paper decomposes the display of submarine geomorphologic map into data reading and data display and performs the two tasks, among which the memory mapping method in parallel is used in the data reading task to further improve the data reading speed, and the data parallel algorithm is used in the data display task to display images in blocks. The experimental results show that the scrolling display speed of the image can be increased by 2.4 times than that of the conventional algorithm when the designed method is used in 4-core processor.

关键词： hydrograpy image prosessing multi-core processor parallel computing

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Non-Rigid Joint Registration for Multi-Contrast MR of Infant Brain Based on the Unsupervised Deep Regression Network 12

Non-Rigid Joint Registration for Multi-Contrast MR of Infant...

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12th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2019

作者： Zhang, Lintao Li, Guoqiang Fu, Deqian Zhang, Wenyin Liu, Mingtao Hu, Shunbo School of Information Science and Engineering Linyi University Linyi Shandong China

ISBN: (纸本)9781728148526

At present, the non-rigid registration for adult brain Magnetic Resonance (MR) images is widely used, but it still faces challenges in the registration of infant brain MR images because of the low SNR. In this paper, we proposed a multi-input channel unsupervised deep regression network (DRN) to achieve non-rigid joint registration for multi-contrast MR, aiming to improve the registration accuracy of infant brain MR images. We firstly designed a U-Net-like network with multiple input channels, which can separately learn the information of T1- and T2- weighted MR images. The model can estimate one deformation field with information within both images to improve registration accuracy. For the multi-contrast MR, other registration algorithms produce two deformation fields but it is hard to evaluate which one is better. Secondly, the model can be trained in an unsupervised and auto-context manner with a multi-scale loss function. That allows the model to perform coarse registration with the large-scale loss function, and then fine registration with the small-scale loss function to achieve better results. Experimental results show that our algorithm reaches state-of-the-art accuracy and outperforms the others in some aspects for the registration of infant brain MR. © 2019 IEEE.

关键词： Magnetic resonance

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Detection of Mycobacterium Tuberculosis in Ziehl-Neelsen Sputum Smear images 11

Detection of Mycobacterium Tuberculosis in Ziehl-Neelsen Spu...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yan, Sineng Liu, Hongying Sun, Li Zhou, Mei Xiao, ZhiRui Zhuang, Quanjie East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Shanghai Lanche Biol Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781538676042

Tuberculosis is one of the top 10 causes of death worldwide. In order to reduce the workload of doctors and the probability of human error, this paper presents an automatic detection algorithm for Mycobacterium tuberculosis in Ziehl-Neelsen Sputum Smear images. The algorithm uses color feature and three morphological characters, which are aspect ratio, circularity and area. Background Equalization algorithm is proposed to utilize color feature sufficiently. This algorithm takes advantage of the watershed algorithm and the channel "a" in Lab color space. Experimental results accuracy of the proposed algorithm.

关键词： Mycobacterium tuberculosis image segmentation image recognition watershed algorithm

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Remote Sensing image Colorization Based on Multi-scale SEnet GAN

Remote Sensing Image Colorization Based on Multi-scale SEnet...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Min Wu Xin Jin Qian Jiang Shin-jye Lee Lin Guo Yide Di Shanshan Huang Jinfang Huang School of Software Yunnan University Kunming Yunnan China Institute of Technology Management National Chiao Tung University Hsinchu Taiwan China

image colorization technique is to colorize the grayscale images or single-channel images. In the research of image colorization, the coloring of remote sensing images is a challenging problem. This paper proposes a new method of remote sensing image colorization method based on Deep Convolution Generative Adversarial Network (DCGAN). We combine multi-scale convolution with Squeeze-and-Excitation Networks (SEnet) to propose a new model that is applied to the generator of DCGAN. Therefore, the generator not only retains the largest image features in the process of the generating images, but also can adjust the channel weights in the training process. We have compared the proposed method with other image colorization methods, and the results show that the proposed method has a good performance on both human vision and image evaluation indicators on the colorization of remote sensing images.

关键词： image color analysis Generators Feature extraction Gallium nitride Generative adversarial networks Remote sensing Gray-scale

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A Light Intensity Reverse Algorithm For Improving Dark Channel Prior Dehazing 11

A Light Intensity Reverse Algorithm For Improving Dark Chann...

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11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu, Qize Renmin Univ China High Sch Class 16Senior 3 Beijing Peoples R China

ISBN: (纸本)9781538676042

image haze-removal algorithm based on the dark channel prior is effective and simple. But it can also encounter problems such as color distortion, noise, detail loss on processing images with large bright patches filled with sky, water, snow and almost white object, because the dark channel prior assumption is invalid within these patches. In this paper, the light intensity reverse (Lgt-Int-Rev) algorithm is proposed which simply reduces the light intensity of high luminance regions of an image and successfully solved the above-mentioned problems with images in snow scene, and images including large sky regions. This algorithm needs not to change He's original algorithm, so all the advantages such as edge-preserving smoothing and gradient preserving of the original algorithm are completely reserved. A large number of experiments have been conducted and results demonstrate the effectiveness of the Lgt-Int-Rev algorithm.

关键词： channel prior transmission guided image filtering light intensity reverse snow scene large sky region

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A Strategy for a Six-Way Synthetic Large Power HIFU Therapy System

A Strategy for a Six-Way Synthetic Large Power HIFU Therapy ...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yunqing Liu Aijun He Congcong Xu Ninglei La Zhangping Yan Xiaoliang Ji School of Electronic Science and Engineering Nanjing University Nanjingy China Haike Medical Equipment Co. Ltd. Nanjing China New Drug Application Center China Medical City CMC Taizhou China

In the high-intensity focused ultrasound (HIFU) surgery, there is a problem of poor therapeutic efficacy due to the inevitable depth-dependent decline in ultrasound energy along the ultrasound pathway. In order to address this issue, the article provides a strategy for a six-way synthetic HIFU therapy system aiming to improve the power of the output ultrasonic beam. In this study, a quasi-self-focusing ultrasonic transducer is presented as a solution to the design of large-caliber self-focusing ultrasonic transducers, which makes it possible to achieve higher ultrasonic utilization by adding a backing layer. Its six-way synthetic large power radio frequency (RF) drive system is also discussed. With the realization of adjustable frequency, phase and excitation value of the driving signal, the presented drive system can operate effectively. Additionally, the acoustic field radiated by the transducer was scanned and a few parameters were measured to assess the transducer's appropriateness for HIFU therapy.

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A CNN-based Cleanliness Evaluation for Bowel Preparation in Colonoscopy

A CNN-based Cleanliness Evaluation for Bowel Preparation in ...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Youxing Zhu Yiwen Xu Weiling Chen Tiesong Zhao Shaohua Zheng Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou China

Colonoscopy is an effective measure for detecting early colorectal lesions. To obtain high-quality pictures during colonoscopy examination, the bowel preparation, or cleansing, is considered as the first and critical step. To date, the evaluation of bowel preparation quality is performed by colonoscopists in most of gastroenterology departments. This manual labeling is time-consuming, uneconomical and sometimes unreliable subject to expertise of physicians. In this work, we propose an automatic evaluation model that labels the cleanliness for bowel preparation in accordance with the Boston Bowel Preparation Scale (BBPS). A compact convolutional neural network (CNN) with 2 Densenet layers which have feature reuse mechanism embedded before the softmax classifier is utilized. The proposed model achieves promising evaluation performance on our dataset as well as on a public dataset named Nerthus where is better than the existing research results. The average correct evaluation ratio is up to 89.58% in 20 individual cases (573 images in total), where each case includes 16 images at least. The method proposed in this paper is considered to be effective and feasible by professional colonoscopists and can be used for clinical auxiliary diagnosis.

关键词： Colonoscopy Colon Training Testing Cancer Convolution

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Detection of HEVC Double Compression with Different Quantization Parameters Based on Property of DCT Coefficients and TUs

Detection of HEVC Double Compression with Different Quantiza...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Qianan Fang Xinghao Jiang Tanfeng Sun Qiang Xu Ke Xu School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

High Efficiency Video Coding (HEVC) double compression detection is of great significance in video forensics. In this paper, a novel algorithm of detection of HEVC video double compression with different Quantization Parameters (QPs) based on Property of DCT Coefficients and TUs (PDCT) feature is proposed. Firstly, the impact of Transforming Unit (TU) type and QP on Discrete Cosine Transform (DCT) coefficients is analyzed. Next, the PDCT feature is extracted based on the statistical characteristics of DCT coefficients and TUs. Then, the framework of our proposed algorithm is given, consisting of four steps described in detail. Finally, experiments are conducted on high definition videos, which are more common in HEVC, while excluding the influence of video content on the detection accuracy. Experimental results and the corresponding analysis are also provided, demonstrating the superiority of the proposed algorithm to the current state-of-the-art algorithm of detecting HEVC double compression with different QPs.

关键词： Discrete cosine transforms Correlation Feature extraction Encoding High efficiency video coding Quantization (signal) signal processing algorithms

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