检索结果-内蒙古大学图书馆

IEEE International Conference on Systems, Man, and Cybernetics

作者： Syoji Kobashi Kei Kuramoto Yutaka Hata Himeji Initiative in Computational Medical and Health Technology Graduate school of engineering University of Hyogo Himeji Japan

ISBN: (纸本)9781479906505

image segmentation plays a fundamental work to analyze medical images. Although many literatures studied automated image segmentation, it is still difficult to segment region-of-interest in any kind of images. Thus, manual delineation is important yet. In order to shorten the processing time and to decrease the effort of users, this paper introduces two approaches of interactive image segmentation method based on fuzzy connectedness image segmentation (FCIS). The first approach interactively updates object affinity of FCIS according to users' additional seed voxels. The second approach models the profile of the object affinity using radial-basis function network (RBFN), and applies online training for users' additional seed voxels. The proposed methods updates segmentation results for not only the seed voxels but also the other miss-classified voxels. The methods had been applied to neonatal brain magnetic resonance (MR) images. The experimental results showed the second approach produced the best results.

关键词： interactive image segmentation fuzzy connectedness image segmentation radial-basis-function network neonatal brain magnetic resoance images image segmentation Online instruction interactive Newborn Infant processing time Consumers Fuzzy

来源：评论

学校读者我要写书评

暂无评论

Click Prompt Learning with Feature Encoding for segmentation of Remote Sensing images

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Song, Bingze Liu, Peng Li, Jing Zhao, Lingjun Chen, Lajiao Xu, Mengzhen Zeng, Yi Liu, Jianbo Chinese Academy of Sciences Aerospace Information Research Institute Beijing10094 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing101408 China Tsinghua University State Key Laboratory of Hydro science and Engineering Department of Hydraulic Engineering Beijing100084 China Beijing Forestry University School of Information Science and Technology Beijing100083 China

Pixel-level annotation tasks are important in the intelligent processing of remote sensing images. For these tasks, interactive image segmentation (IIS) models using click prompts are developing fast in the field of natural images. However, most interactive segmentation models using click prompts are unsuitable for remote sensing images with their current design of click prompts and their interaction schemes with image information. Based on the situation, we used a DETR-like model as the basic framework and redesigned the pixel decoder and the transformer decoder to better suit the task of IIS for remote sensing images. In the pixel decoder, we designed a click prompt with feature encoding to learn click information and a composite attention structure to facilitate interaction between click and image information, allowing the image feature at the click locations to more easily dominate annotation masks. In the transformer decoder, we utilized deformable attention, using only a single initialized query to obtain annotation masks and IoU prediction. In this paper, we trained our model on a composite remote sensing dataset and evaluated its performance on external datasets. The results showcased the model’s adaptability, achieving superior performance compared to existing methods. © 1980-2012 IEEE.

关键词： Click Prompt Learning interactive image segmentation Remote Sensing image Transformer

来源：评论

学校读者我要写书评

暂无评论

image segmentation algorithms based on the machine learning of features

引用

PATTERN RECOGNITION LETTERS 2010年第14期31卷 2325-2336页

作者： Lee, Sang Hak Koo, Hyung Il Cho, Nam Ik Seoul Natl Univ Sch Elect Engn Seoul South Korea

For general purpose image segmentation, it is required to find and integrate the features that best characterize the regions to be segmented. This paper proposes a machine learning approach to finding the appropriate features and also a new segmentation method based on the information obtained while learning. Precisely, our method is based on the AdaBoost algorithm for learning the difference between regions, and the CRF-based (conditional random fields) energy formulation for the segmentation using the information from the learning. We have applied our method to interactive (semi-automatic) and unsupervised (fully-automatic) segmentation problems. While the interactive case is relatively straightforward due to the nature of our machine learning scheme, the unsupervised case is not. Hence, for the unsupervised segmentation, we devise a new initialization method and an EM-like (Expectation-Maximization) optimization method that iterates AdaBoost learning and graph-cuts. The analysis shows that the number of regions is automatically determined so that only distinguishable regions are survived. Experimental results also show that the proposed method gives promising results in diverse applications such as texture segmentation, color-texture segmentation, and page segmentation. 2010 Elsevier B.V. All rights reserved.

关键词： image segmentation interactive image segmentation Unsupervised image segmentation AdaBoost image features

来源：评论

学校读者我要写书评

暂无评论

image segmentation Based on GrabCut Framework Integrating Multiscale Nonlinear Structure Tensor

引用

IEEE TRANSACTIONS ON image PROCESSING 2009年第10期18卷 2289-2302页

作者： Han, Shoudong Tao, Wenbing Wang, Desheng Tai, Xue-Cheng Wu, Xianglin Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Nanyang Technol Univ Div Math Sci Singapore 63761 Singapore Nanyang Technol Univ Div Math Sci Sch Math & Phys Sci Singapore 637616 Singapore

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.

关键词： Adaptive fusion graph cuts interactive image segmentation multiscale nonlinear structure tensor (MSNST)

来源：评论

学校读者我要写书评

暂无评论

Rock CT image Fracture segmentation Based on Convolutional Neural Networks

引用

ROCK MECHANICS AND ROCK ENGINEERING 2024年第8期57卷 5883-5898页

作者： Lei, Jian Fan, Yufei Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Peoples R China

image-based automatic fracture extraction methods have many practical applications in geological and engineering. Fracture identification and quantitative characterization require the means of interpreting and statistically analyzing image data. In comparison to traditional digital image processing methods, supervised semantic segmentation methods based on Convolutional Neural Networks (CNN) offer distinct advantages in extracting fractures from CT images. The study analyzes the characteristics of fracture areas in CT images and compares the results obtained through traditional threshold segmentation methods with those achieved using deep learning techniques. An integrated approach combining interactive image segmentation was proposed in this study to extract the fractures, in which deep learning methods were to determine the fracture areas, as well as morphological operation and threshold segmentation methods were adopted to extract the fractures from the small target areas. The implementation of this method significantly enhances the efficiency of extraction results compared to manual fracture extractions. This study selected CT images of igneous rock with a resolution of 0.7 um as the research object. DeepLab V3 + and UNet3 + network models in the PaddleSeg framework were used for the deep learning process. The characteristics of fracture morphology, opening degree and gray level in CT images of rock are *** effects of traditional segmentation algorithm and deep learning algorithm in fracture extraction were compared.A new method combining deep learning and threshold segmentation is proposed to extract fractures.

关键词： Fracture extraction Convolutional neural networks Threshold segmentation interactive image segmentation DeepLab V3+ UNet3+

来源：评论

学校读者我要写书评

暂无评论

Geodesic Paths for image segmentation With Implicit Region-Based Homogeneity Enhancement

引用

IEEE TRANSACTIONS ON image PROCESSING 2021年 30卷 5138-5153页

作者： Chen, Da Zhu, Jian Zhang, Xinxin Shu, Minglei Cohen, Laurent D. Qilu Univ Technol Shandong Acad Sci Shandong Artificial Intelligence Inst Jinan 250014 Peoples R China Shandong First Med Univ Shandong Canc Hosp Dept Radiat Oncol Phys & Technol Jinan 250117 Peoples R China Shandong Univ Sch Software Jinan 250100 Peoples R China PSL Res Univ Univ Paris Dauphine CNRS UMR 7534CEREMADE F-75016 Paris France

Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.

关键词： image segmentation Measurement Computational modeling Active contours image edge detection Mathematical model Shape Geodesic path Eikonal equation asymmetric Finsler metric region-based homogeneity interactive image segmentation

来源：评论

学校读者我要写书评

暂无评论

Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

引用

JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2016年第Jan.期34卷 12-27页

作者： Zhu, Hongyuan Meng, Fanman Cai, Jianfei Lu, Shijian ASTAR Inst Infocomm Res Singapore Singapore Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation. (C) 2015 Elsevier Inc. All rights reserved.

关键词： image segmentation Superpixel interactive image segmentation Object proposal Semantic image parsing image cosegmentation Unsupervised image segmentation Weakly-supervised image segmentation

来源：评论

学校读者我要写书评

暂无评论

interactively multiphase image segmentation based on variational formulation and graph cuts

引用

PATTERN RECOGNITION 2010年第10期43卷 3208-3218页

作者： Tao, Wenbing Chang, Feng Liu, Liman Jin, Hai Wang, Tianjiang Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

In this paper the multiple piecewise constant (MPC) active contour model is extended to deal with multiphase case. This proposed multiphase model can be effectively optimized by solving the minimum cuts problem of a specially devised multilayer graph. Based on the proposed energy functional and its graph cuts optimization, an interactively multiphase partition method for image segmentation is presented. The user places some scribbles with different colors on the image according to the practical application demand and each group of scribbles with the same color corresponds to a potential image region. The distribution of each region can be learned from the input scribbles with some particular color. Then the corresponding multilayer graph can be constructed and its minimum cuts can be computed to determine the segmentation result of the image. Numerical experiments show that the proposed interactively multiphase segmentation method can accurately segment the image into different regions according to the input scribbles with different color. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Multiphase multiple piecewise constant Graph cuts optimization Multilayer graph

来源：评论

学校读者我要写书评

暂无评论

Fast image segmentation based on multilevel banded closed-form method

引用

PATTERN RECOGNITION LETTERS 2010年第3期31卷 216-225页

作者： Han, Shoudong Tao, Wenbing Wu, Xianglin Tai, Xue-cheng Wang, Tianjiang Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China Nanyang Technol Univ Div Math Sci Sch Math & Phys Sci Singapore 637616 Singapore

In this paper. a fast interactive image segmentation method is developed. The method combines the GrabCut algorithm with the multilevel banded closed-form (MLBCF) technique to achieve the acceleration. The GrabCut method is first applied on a low-resolution image to obtain the segmentation. The coarse labeling is then propagated to the higher-resolution level by using the banded closed-form method with the locally linear assumption. Some post-processing, such as alpha thresholding, probability classification and multi-seeds banded Graph Cuts, is applied to assign the final labeling. For experimental comparison, we also implement the multilevel banded Graph Cuts (MLBGC) method based on GrabCut algorithm. Experiments using synthesized noisy images and real natural scene images demonstrate the superior performance of the proposed method in terms of segmentation accuracy, computation efficiency and memory usage. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Graph Cuts Closed-form Multi-seeds interactive image segmentation

来源：评论

学校读者我要写书评

暂无评论

Texture aware image segmentation using graph cuts and active contours

引用

PATTERN RECOGNITION 2013年第6期46卷 1719-1733页

作者： Zhou, Hailing Zheng, Jianmin Wei, Lei Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Deakin Univ Ctr Intelligent Syst Res Geelong Vic 3217 Australia

The problem of segmenting a foreground object out from its complex background is of great interest in image processing and computer vision. Many interactive segmentation algorithms such as graph cut have been successfully developed. In this paper, we present four technical components to improve graph cut based algorithms, which are combining both color and texture information for graph cut, including structure tensors in the graph cut model, incorporating active contours into the segmentation process, and using a "softbrush" tool to impose soft constraints to refine problematic boundaries. The integration of these components provides an interactive segmentation method that overcomes the difficulties of previous segmentation algorithms in handling images containing textures or low contrast boundaries and producing a smooth and accurate segmentation boundary. Experiments on various images from the Brodatz, Berkeley and MSRC data sets are conducted and the experimental results demonstrate the high effectiveness of the proposed method to a wide range of images. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Graph cut Active contour Color-texture Structure tensor

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：