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检索条件"任意字段=17th Image Processing: Algorithms and Systems Conference, IPAS 2019"

共 241 条记录，以下是181-190 订阅

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Breast MRI tumour segmentation using modified automatic seeded region growing based on particle swarm optimization image clustering 17

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17th Online World conference on Soft Computing in Industrial Applications, WSC17

作者： Al-Faris, Ali Qusay Ngah, Umi Kalthum Isa, Nor Ashidi Mat Shuaib, Ibrahim Lutfi Universiti Sains Malaysia Penang Malaysia Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9783319009292

In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. the paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. the PSO clusters’ intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological thinning. the system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. the results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset. © Springer International Publishing Switzerland 2014.

关键词： Magnetic resonance imaging

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Can Masses of Non-Experts Train Highly Accurate image Classifiers? A Crowdsourcing Approach to Instrument Segmentation in Laparoscopic images

Can Masses of Non-Experts Train Highly Accurate Image Classi...

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17th International conference on Medical image Computing and Computer-Assisted Intervention (MICCAI)

作者： Maier-Hein, Lena Mersmann, Sven Kondermann, Daniel Bodenstedt, Sebastian Sanchez, Alexandro Stock, Christian Kenngott, Hannes Gotz Eisenmann, Mathias Speidel, Stefanie German Canc Res Ctr Comp Assisted Intervent Heidelberg Germany Heidelberg Univ Heidelberg Collaboratory Image Proc Heidelberg Germany Karlsruhe Inst Technol Inst Anthropomat & Robot D-76021 Karlsruhe Germany Heidelberg Univ Inst Med Biometry & Informat D-69115 Heidelberg Germany Heidelberg Univ Dept Gen Visceral & Transplantat Surg D-69115 Heidelberg Germany

ISBN: (纸本)9783319104706;9783319104690

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

关键词： Learning systems

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Optimized Multilevel threshold Selection Using Evolutionary Computing

Optimized Multilevel Threshold Selection Using Evolutionary ...

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International conference on Network-Based Information systems (NBIS)

作者： M. Sridevi C. Mala E. Sivasankar Ilsun You National Institute of Technology Calicut Calicut Kerala IN Korean Bible University Seoul KR

thresholding is the method used for segmenting an image to isolate regions of interest from the image. the result of segmentation mainly depends on the selection of proper threshold values and number of classes. this paper proposes a method for optimal selection of threshold values using Evolutionary computing. the proposed method decomposes the given image to reduce its size so that it can be processed faster using Genetic Algorithm. the resultant image is finally mapped onto the original image space. the efficiency of the proposed method is compared with the other multilevel thresholding techniques namely GA-Otsu and GA-Kapur with and without wavelets. From the experimental results, it is inferred that the proposed method takes less time for processing and provides better results compared to existing methods.

关键词： image segmentation Histograms Discrete wavelet transforms Linear programming Genetic algorithms Sociology

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A new pixel-based fusion framework to enhance object detection in automotive applications

A new pixel-based fusion framework to enhance object detecti...

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International conference on Information Fusion

作者： Jan thomanek Gerd Wanielik Ingenieurgesellschaft Auto und Verkehr GmbH Berlin Germany

ISBN: (纸本)9781479916344

Modern cooperative Advanced Driver Assistance systems (ADASs) require efficient algorithms and methods for the real-time processing of all sensor data. In particular, systems for combination of different sensors are gaining increasing importance. this paper proposes an early fusion technique to combine the advantages of two or more vision sensors. It creates a single composite image that will be more comprehensive for further computer vision tasks, e.g. Object Detection. After image registration, the presented pixel-based fusion framework transforms the registered sensor images into a common representational format by a multiscale decomposition. A denoising and a multiscale edge detection are applied on the transformed data. Only data with a high Activity Level are considered for the fusion process that based on a probabilistic approach. Finally, the fused image can be the input for a subsequent feature extraction task. the proposed fusion technique is examined on a Pedestrian Detection System based on an infrared and a visible light camera.

关键词： image edge detection Wavelet transforms Cameras Feature extraction Object detection Wavelet domain

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Research on Passenger Flow Counting based on Embedded System

Research on Passenger Flow Counting based on Embedded System

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IEEE International conference on Intelligent Transportation systems

作者： Bin Hu Gang Xiong Yiyue Li Zizhang Chen Weisi Zhou Xin Wang Qiang Wang Inst. of Autom. Qingdao Acad. of Intell. Ind. Qingdao China

ISBN: (纸本)9781479960798

Passenger flow counting is an important issue for management and decision in many public areas such as shopping centers, airports, gyms and galleries. this paper presents some algorithms of Head-counting and Head-tracking for embedded systems in vertically-installed cameras. the system is able to count the number of passengers who enter or leave the detection area. the results are reliable, accurate and stable.

关键词： Kalman filters image processing learning (artificial intelligence) target tracking traffic engineering computing Kalman filter embedded system head-counting head-tracking machine learning passenger flow counting vertically-installed cameras conferences Intelligent transportation systems

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Snowfall detection under bad visibility scene

Snowfall detection under bad visibility scene

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IEEE International conference on Intelligent Transportation systems

作者： Hiroshi Kawarabuki Kazunori Onoguchi Graduate School of Science and Technology Hirosaki University Hirosaki Japan

An effect of weather is very critical in outdoor camera surveillance applications. So, we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera images. the proposed algorithm increases a contrast of an image by haze removal. A contrast of hazy image is reduced by haze color of atmosphere. When several haze colors exist in an image, the degree of contrast degradation is different for each haze color. To deal with this problem, the proposed method performs segmentation of an image for every haze color and estimates the airlight, the transmission, and the weight of dehaze in each segmentation area individually. Most of haze removal algorithms require a tone curve correction as post processing. However, it is very difficult to select an optimal tone curve correction for any image. To deal with this problem, we propose a novel algorithm that does not require any tone curve correction. the general haze removal algorithm treats only haze image. However, in the field of outdoor camera surveillance, it's desirable to realize the algorithm which can be applied to various weather conditions. Our algorithm can detect snowfall at high speed and stably not only in bad weather but also in good weather.

关键词： image color analysis image edge detection Brightness Snow Cameras image segmentation

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Fast curvilinear structure extraction and delineation from line scan images using Multi-scale yin-yang discrete-point computing

Fast curvilinear structure extraction and delineation from l...

引用

International conference on Intelligent Transportation

作者： Zongxiao Zhu Xiaoji Dong Yihui Wang Information Processing Laboratory for Minority Language South-Central University for Nationalities Wuhan China

ISBN: (纸本)9781479960798

Extracting and delineating curvilinear structure is important for many applications. However, most of the existing algorithms cannot meet the real-time processing requirement. this paper presents a novel method for fast curvilinear structure extraction and delineation from TFDS-3 line-scan images using multi-scale yin-yang discrete-point computing, which can be divided into two stages: multi-scale yin-yang discrete-point sampling and multi-scale yin-yang discrete-point grouping. Multi-scale discrete-point sampling can effectually overcome the influences of interference from noise, textures and uneven illumination, greatly reduce the difficulty of centerline extraction, and tolerate small geometric transformations. By analyzing discrete-points' features in different belt-shaped regions of yin-yang discrete-point maps, multi-scale yin-yang discrete-point grouping produces a three-level detection system using feature points, line segments and centerlines, which can extract centerlines with their types quickly, smoothly and accurately from these belt-shaped regions, and achieve the final curvilinear structure results by clustering these centerlines with Gestalt rules.

关键词： image edge detection Feature extraction image segmentation Shape Computational modeling Real-time systems Adaptation models

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Snowfall detection under bad visibility scene

Snowfall detection under bad visibility scene

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17th International IEEE conference on Intelligent Transportation systems & the Asia-Pacific Council on systems Engineering conference 2014

作者： Hiroshi Kawarabuki Kazunori Onoguchi Graduate School of Science and Technology Hirosaki University

An effect of weather is very critical in outdoor camera surveillance ***,we focus on bad weather by snow and we propose an algorithm of detecting snowfall from surveillance camera *** proposed algorithm increases a contrast of an image by haze removal.A contrast of hazy image is reduced by haze color of *** several haze colors exist in an image,the degree of contrast degradation is different for each haze *** deal with this problem,the proposed method performs segmentation of an image for every haze color and estimates the airlight,the transmission,and the weight of dehaze in each segmentation area *** of haze removal algorithms require a tone curve correction as post ***,it is very difficult to select an optimal tone curve correction for any *** deal with this problem,we propose a novel algorithm that does not require any tone curve *** general haze removal algorithm treats only haze ***,in the field of outdoor camera surveillance,it's desirable to realize the algorithm which can be applied to various weather *** algorithm can detect snowfall at high speed and stably not only in bad weather but also in good weather.

关键词： image processing,snowfall detection,haze removal,segmentation,background subtraction

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Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

Attributed Relational SIFT-Based Regions Graph for Art Paint...

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17th International conference on image Analysis and processing (ICIAP)

作者： Manzo, Mario Petrosino, Alfredo Univ Naples Parthenope Ctr Direz Dept Sci & Technol Naples Italy

ISBN: (纸本)9783642411816;9783642411809

Recently, image retrieval and analysis algorithms have been extensively applied to art related domains. In this field, state-of-the-art approaches mainly focus on feature extraction with the aim of improving reliability of authentication, classification and retrieval of art paintings. In this paper we propose an effective modeling, based on a graph structure, and a retrieval strategy, based on a graph matching algorithm, for art paintings. the proposed approach has been tested on different datasets with high quality results allowing an user to run effective content-based queries on painting records.

关键词： CBIR systems graph matching graph based image representation local invariant features extraction

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Parameter assisted HE colored tissue image classification

Parameter assisted HE colored tissue image classification

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17th IEEE International conference on Intelligent Engineering systems, INES 2013

作者： Kozlovszky, M. Hegedus, K. Szenasi, S. Kiszler, G. Wichmann, B. Bandi, I. Eigner, Gy. Sas, P.I. Kovacs, L. Garaguly, Z. Jonas, V. Kiss, G. Valcz, G. Molnar, B. Biotech Laboratory Obuda University Budapest Hungary 2nd Department of Internal Medicine Semmelweis University Budapest Hungary Molecular Medicine Research Unit Hungarian Academy of Sciences Budapest Hungary

ISBN: (纸本)9781479908288

the aim of our work was to design and implement a software solution, which supports quantitative histological analysis of hematoxilin eozin (HE) stained colon tissue samples, identify tissue structures - nuclei, glands and epithelium - using image processing methods. Furthermore, based on the result of the histological segmentation, it gives a suggestion for the negative or malignant status of the samples automatically. In this paper we describe the algorithm which builds up mainly by two software components: MorphCheck -our software framework-, which is capable to make effective, morphometric evaluation of high resolution digital tissue images and a modified WND-CHARM (Weighted Neighbor Distance Using Compound Hierarchy of algorithms Representing Morphology), which is a multi-purpose image classifier. the image classification was performed mainly based on 75+15 pre-defined colon tissue specific parameters, which were measured by MorphCheck, and other 2873 in-built generic image parameters, which were measured by WND-CHARM. We appended WND-CHARM's learning and classification capabilities with our colon tissue specific parameters and with this act we have increased its classification accuracy significantly on HE stained colon tissue sample images. © 2013 IEEE.

关键词： image classification

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