检索结果-内蒙古大学图书馆

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Gakne, Paul Verlaine Petovello, Mark Univ Calgary Schulich Sch Engn Dept Geomat Engn Posit Locat & Nav PLAN Grp Calgary AB T2N 1N4 Canada

ISBN: (纸本)9781467384025

In order to improve the accuracy of user's position solution using Global Navigation Satellite System (GNSS) in urban canyons, it is important to know whether a satellite's signal is obstructed by surrounding buildings. This can be accomplished by using an upward-facing camera and segmenting the image into sky and non-sky. This paper evaluates the Otsu, Mean Shift, Graph cut and HMRF-EM-image image segmentation algorithms for this purpose. Since some algorithms provide two or more categories, segmentation is followed by k-means clustering techniques to yield only two categories;sky and non-sky. The algorithms are tested using images taken using an upwardfacing camera at roughly the same locations in different weather conditions: cloudy and sunny. Result shows that, when images are appropriately adjusted, the Otsu method overcomes the three other algorithms in terms of the percentage of sky accurately segmented and is also more computationally efficient. Experiment was also perform in Calgary downtown to show the effect of segmentation on the GNSS accuracy. Results show that, when obstructed satellites are removed, the RMS of the residuals decreases significantly compare to when all satellites are used.

关键词： Global Navigation Satellite System image segmentation algorithm sky satellites images segmentation Root mean square

来源：评论

学校读者我要写书评

暂无评论

Supervised Evaluation of Seed-Based Interactive image segmentation algorithms 20

Supervised Evaluation of Seed-Based Interactive Image Segmen...

引用

20th SYMPOSIUM ON SIGNAL PROCESSING, imageS AND COMPUTER VISION (STSIVA)

作者： Andrade, Fernanda Carrera, Enrique V. Univ Fuerzas Armadas ESPE Casilla 171-5-231B Sangolqui Ecuador

ISBN: (纸本)9781467394611

Extensive research has been conducted in an effort to evaluate methods and techniques for image segmentation. However, while most literature has focused on evaluating automatic and semi-automatic algorithms, works evaluating interactive segmentation algorithms are less numerous. Note that interactive segmentation can improve results by adding prior knowledge from users into the process. Although this user guidance improves segmentation results, it also makes difficult to conduct objective evaluations. For this reason, some works only present non-canonical evaluations. In this paper, we present an objective and empirical evaluation of seed-based interactive segmentation algorithms. We first compare popular metrics that are employed in image-segmentation evaluations in order to define which one reflects most accurately the performance of segmentation algorithms. Then, in the aim of presenting reproducible results, we introduce a novel seed-based user input dataset that extends the well-known GrabCut dataset. In addition, we evaluate and contrast four state-of-the-art interactive segmentation algorithms. The analysis of the results demonstrates that Jaccard coefficient and Precision-Recall curves provide a good insight into the performance of the evaluated algorithms. Finally, the GrabCut algorithm presents the most robust and useful segmentation among all the evaluated algorithms.

关键词： image segmentation interactive systems GrabCut algorithm GrabCut dataset Jaccard coefficient objective evaluations precision-recall curves seed-based interactive image segmentation algorithms seed-based user input dataset segmentation results semiautomatic algorithms supervised evaluation Accuracy algorithm design and analysis Databases image color analysis image segmentation Measurement Semantics image segmentation algorithm design and analysis image colour analysis interactive systems image segmentation algorithm Semantics segmentation Database segmentation Interactive

来源：评论

学校读者我要写书评

暂无评论

A Hybrid algorithm of Otsu and Adaptive Region for image segmentation

A Hybrid Algorithm of Otsu and Adaptive Region for Image Seg...

引用

IEEE International Conference on Power, Intelligent Computing and Systems

作者： Luo Chunlei Sha Hao Wang Hu He Yi College of Mechanical and Electrical Engineering Central South University

ISBN: (纸本)9781728137216

The traditional Region Growing (RG) algorithm is a semi-automatic image segmentation algorithm that requires manual selection of seed points, manual setting of thresholds, which may cause the cavity and over-segmentation while deals with the uneven and undivided image for noise and grayscale. Therefore, the classical Otsu method is adopted to find the optimal threshold in HSV space to obtain the initial binarized segmentation image. Then, the Adaptive Region Growing (ARG) algorithm is applied to find the image edge. To be specific, the initial seed point is confirmed automatically through histogram firstly. Secondly, the threshold of growth condition is determined according to the average similarity of image. Finally, the image is grown into the contour edges. This image is mixed together with the initial Otsu segmentation image can obtain the final processed image. Experimental results show that the proposed algorithm has strong anti-interference, which can effectively reduce the mis-segmentation rate.

关键词： image segmentation Machine vision Obstacle detective RG algorithm Otsu algorithm image segmentation robot vision image images imagery (Psychotherapy) edge image suppression of interference region growing image segmentation algorithm algorithms

来源：评论

学校读者我要写书评

暂无评论

A Method of Two-Dimensional Otsu image Threshold segmentation Based on Improved Firefly algorithm

A Method of Two-Dimensional Otsu Image Threshold Segmentatio...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Chenhang Zhou Liwei Tian Hongwei Zhao Kai Zhao Department of Electronic Engineering Shenyang University Liaoning Information Integration Technology Engineering Research Center of Internet of things

ISBN: (纸本)9781479987313

In order to solve the problems in Two-Dimensional image threshold segmentation such as, time consuming, low accuracy and easy to produce false segmentation image, a novel image segmentation algorithm that combing improved Firefly algorithm with Two-Dimensional Otsu(2-D Otsu) is proposed. First of all, an improved Firefly algorithm is proposed considering the influence of the historical best position of group when updating the location of fireflies. Then, searching the optimal segmenting threshold by using image threshold segmentation method with Two-Dimensional Otsu based on improved Firefly algorithm. Finally, completing image segmentation by the optimal segmentation threshold. It is shown that good segmentation performance is achieved by the improved algorithm, which may not only shorten the run time and improve the quality of image segmentation effectively, but also be applied to the real-time processing of image segmentation.

关键词： image segmentation Firefly algorithm Historical best position Two-Dimensional Otsu Optimal threshold image segmentation Lampyridae image thresholding segmentation segmentation Runtime image segmentation algorithm

来源：评论

学校读者我要写书评

暂无评论

Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain

引用

INTERNATIONAL JOURNAL OF REMOTE SENSING 2013年第13期34卷 4550-4569页

作者： Chen, Mi Strobl, Josef Capital Normal Univ Coll Resources Environm & Tourism Beijing 100048 Peoples R China Salzburg Univ Dept Geoinformat A-5020 Salzburg Austria

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas;image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms.

关键词： label field label data field simulation remote sensing images image segmentation Multi-resolution Parameter estimation image segmentation algorithm Fuzzy Wavelet domain Markov random fields Wavelet coefficients

来源：评论

学校读者我要写书评

暂无评论

Automatic segmentation of contrast-enhanced time resolved image series

引用

JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2003年第4期11卷 241-251页

作者： Kim, Junhwan Zabih, Ramin Cornell Univ Dept Comp Sci Ithaca NY 14853 USA Cornell Univ Dept Radiol Ithaca NY 14853 USA

Medical imaging often involves the injection of contrast agents and the subsequent analysis of tissue enhancement patterns. Many important types of tissue have characteristic enhancement patterns;for example, in MR mammography, malignancies exhibit a characteristic "wash out" temporal pattern, while in MR angiography, arteries, veins and parenchyma each have their own distinctive temporal signature. In such time resolved image series, there are substantial changes in intensities;however, this change is due primarily to the contrast agent, rather than to motion. As a result, the task of automatically segmenting contrast-enhanced images poses interesting new challenges. In this paper, we propose a new image segmentation algorithm for time resolved image series with contrast enhancement, using a model-based time series analysis of individual pixels. We take an energy minimization approach to ensure spatial coherence. The energy is minimized in an expectation-maximization fashion that alternates between segmenting the image into a number of non-overlapping regions and finding the temporal profile parameters which describe the behavior of each region. Preliminary experiments on MR angiography and MR mammography studies show the algorithm's ability to find an accurate segmentation.

关键词： Magnetic Resonance Angiography series (mathematics) contrast enhancement ENERGY MINIMISATION image segmentation algorithm AUTOMATIC Medical imaging time series analysis profile parameter image Time resolved

来源：评论

学校读者我要写书评

暂无评论

Automatic image segmentation using fuzzy hit or miss and homogeneity index

引用

INTERNATIONAL JOURNAL OF REMOTE SENSING 2006年第1期27卷 203-221页

作者： Intajag, S Paithoonwatanakij, K Cracknell, AP King Mongkuts Inst Technol Fac Engn Dept Elect Bangkok 10520 Thailand King Mongkuts Inst Technol Fac Engn Dept Instrumentat Engn Bangkok 10520 Thailand Univ Dundee Dept Appl Phys & Elect & Mech Engn Dundee DD1 4HN Scotland

This paper proposes an automatic image segmentation algorithm. Our hierarchical algorithm uses recursive segmentation that consists of two major steps. First, local thresholding is carried out by the fuzzy hit-or-miss operator, which allows dynamic separation of a grey-scale image into two classes, based on local intensity distributions. The fuzzy hit-or-miss, being an operator of fuzzy mathematical morphology, plays an important role in performing the dynamic local segmentation. This operator gives a better shape description than global thresholding methods. It also retains small but significant regions in satellite images. Second, the homogeneity index is measured in each class based on the quality of normalized intra-region uniformity. The proposed method has been tested using both synthetic and satellite images successfully;moreover, the algorithm can estimate the number of classes automatically.

关键词： Fuzzy image segmentation algorithm homogeneity image segmentation AUTOMATIC mathematical morphology Satellite imagery

来源：评论

学校读者我要写书评

暂无评论

LEARNING BASED OBJECTIVE EVALUATION OF image segmentation algorithmS

LEARNING BASED OBJECTIVE EVALUATION OF IMAGE SEGMENTATION AL...

引用

IET Conference on image Processing

作者： E. Askari A. M. Eftekhari Department of Computer Engineering Qazvin Branch Islamic Azad University of Qazvin-IRAN

ISBN: (纸本)9781622762675

image segmentation plays an important role in a broad range of applications and many image segmentation methods have been proposed, therefore it is necessary to be able to evaluate the performance of image segmentation algorithms objectively. In this paper we present a new fuzzy metric to evaluate the accuracy of image segmentation algorithms, based on the features of each segments using neural networks. The neural network after training can distinguish the similarity or dissimilarity of each pairs of segments and finally the segmentation algorithms accuracy have been computed by novel presented metric quantitatively. Our proposed method does not require a manually-segmented reference image for comparison therefore can be used for real-time evaluation and is sensitive to both over-segmentation and under-segmentation. Experimental results were obtained for a selection of images from Berkeley segmentation data set and demonstrated that it's a proper measure for comparing image segmentation algorithms.

关键词： Objective evaluation metric Neural network image segmentation Under-segmentation Over-segmentation image segmentation algorithm image segmentation images algorithms Learning

来源：评论

学校读者我要写书评

暂无评论

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

引用

BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Hill, Andrew A. LaPan, Peter Li, Yizheng Haney, Steve Wyeth Ayerst Res Dept Biol Technol Cambridge MA 02140 USA

Background: High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. Results: Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells. Conclusion: image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.

关键词： Texture Feature segmentation algorithm Segmented Object image segmentation algorithm Segmented Cell

来源：评论

学校读者我要写书评

暂无评论

image segmentation on spherical coordinate representation of RGB colour space

引用

IET image PROCESSING 2012年第9期6卷 1275-1283页

作者： Moreno, R. Grana, M. Ramik, D. M. Madani, K. Univ Basque Country Computat Intelligence Grp E-48080 Bilbao Spain Univ PARIS EST Creteil Senart FB Inst Technol LISSI EA 3956 F-77127 Lieusaint France

This study presents an image segmentation algorithm working on the spherical coordinates of RGB colour space. The algorithm uses a hybrid chromatic distance inspired in the human vision system which shifts from the chromatic to the greyscale distance depending on the pixel's luminance value. In dark areas of the image the chromatic distance is too sensitive to image noise, so that the greyscale distance is used instead. Colour constancy properties of this segmentation approach follow from the dichromatic reflection model. The approach is strongly robust regarding highlights and dark spots and does not need illuminant source colour normalisation. The authors give results on public benchmark image databases and robot camera images. A public implementation is made available for independent test of the algorithm image segmentation results.

关键词： Dichroism Spherical coordinates computer vision visual databases Illuminating composition image segmentation algorithm RGB colour space image noise Grey scale

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：