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检索条件"主题词=Interactive image segmentation"

共 189 条记录，以下是71-80 订阅

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interactive image segmentation VIA CASCADED METRIC LEARNING

INTERACTIVE IMAGE SEGMENTATION VIA CASCADED METRIC LEARNING

引用

IEEE International Conference on image Processing

作者： Wenbin Li Yinghuan Shi Wanqi Yang Hao Wang Yang Gao State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (纸本)9781479983407

In this paper, we propose an interactive image segmentation method from a novel perspective of cascaded metric learning. Given an image with user-marked scribbles that are essentially uncertain and noisy, our method completes the segmentation task by solving a binary classification problem. Starting from the initial training samples with known class labels (i.e., regions of the image that are believed with high confidence to be foreground or background), we first find an optimal metric that can best describe the classification of these samples. After that, we classify the unlabeled samples using the learnt metric. Samples classified with high confidence are used as new training samples to refine the metric. This cycle of metric learning and classification repeats until the accomplishment of the image segmentation task. The proposed method is extensively evaluated on the MSRC image set. Experiment results show that our method outperforms the state-of-the-art methods.

关键词： Cascaded Metric Learning interactive image segmentation Sample Selection image segmentation images Metric Metronidazole imagery (Psychotherapy) Learning Noise

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A SYSTEMATIC PERFORMANCE EVALUATION OF interactive image segmentation METHODS BASED ON SIMULATED USER INTERACTION

A SYSTEMATIC PERFORMANCE EVALUATION OF INTERACTIVE IMAGE SEG...

引用

7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

作者： Moschidis, Emmanouil Graham, Jim Univ Manchester Sch Canc & Enabling Sci Manchester M13 9PT Lancs England

ISBN: (纸本)9781424441266

In this paper we report on the results of a systematic performance evaluation of three efficient image segmentation algorithms, namely Graph-Cuts, Random-Walker and Grow-Cut. The evaluation focuses on their function as the computational part of an interactive segmentation system. The implications caused by the human involvement in the overall process are avoided by simulating two different patterns of user interaction. The methods are evaluated with respect to accuracy, precision, efficiency and parameter sensitivity on three dimensional medical images. The results provide useful insight regarding the algorithmic performance of the selected techniques and the effect of the identified patterns of user interaction on the segmentation outcome.

关键词： Performance Evaluation interactive image segmentation Simulated User Interaction 3-D Medical images Graph-Cuts Random-Walker Grow-Cut

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KM_GrabCut - A Fast interactive image segmentation Algorithm 6

KM_GrabCut - A Fast Interactive Image Segmentation Algorithm

引用

6th International Conference on Graphic and image Processing (ICGIP)

作者： Li, Jianbo Yao, Yiping Tang, Wenjie Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781628415582

image segmentation is critical for image processing. Among several algorithms, GrabCut is well known by its little user interaction and desirable segmentation result. However, it needs to take a lot of time to adjust the Gaussian Mixture Model (GMM) and to cut the weighted graph with Max-Flow/Min-Cut Algorithm iteratively. To solve this problem, we first build a common algorithmic framework which can be shared by the class of GrabCut-like segmentation algorithms, and then propose KM_GrabCut algorithm based on this framework. The KM_GrabCut first uses K-means clustering algorithm to cluster pixels in foreground and background respectively, and then constructs a GMM based on each clustering result and cuts the corresponding weighted graph only once. Experimental results demonstrate that KM_GrabCut outperforms GrabCut with higher performance, comparable segmentation result and user interaction.

关键词： interactive image segmentation GrabCut GMM Max-Flow/Min-Cut Algorithm K-means

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AntSeg: The Application of Ant Colony Optimization to interactive image segmentation

AntSeg: The Application of Ant Colony Optimization to Intera...

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31st International Conference of the Chilean-Computer-Science-Society (SCCC)

作者： Beraldi Versuti, Tiago Alexandre Flores, Franklin Cesar Mulati, Mauro Henrique Polidorio, Airton Marco State Univ Maringa UEM Dept Informat Maringa Parana Brazil Midwest State Univ UNICENTRO Dept Comp Sci Guarapuava Brazil

ISBN: (纸本)9781479929375

The computational process of image segmentation performs the partitioning of it in disjoint regions by the specification and application of specific criteria. This process can be automatic or interactive. The interactive process allows the user interference in this process by means of the insertion of new criteria at any instant of the process execution. This new criterion can lead to the reaching of more appropriate results for the solution to the problem. However, interactive process application requires the user to have a high ability, context knowledge and accuracy executing the tasks. The interactive process can include facilities by allowing that some user actions may be executed with relative accuracy. Using computational resources, such task is made easier, since a computer can perform with more accuracy and speed, from the guides interactively defined by the user. The literature presents many methods that perform this task, among them the live wire, intelligent scissors and the interactive watershed with markers. The first two use a minimum cost system, in other words, optimization, to their working. This paper proposes an interactive method applied to image segmentation that combines the artificial ant colonies behavior and the image gradient technique. The gradient, supplied by heuristic rules, is the surface the ants search the path to the desired solution. The segmentation starts with the imposition of an initial point at the border of the object of interest and, by the user adding new points along its border, new segments belonged to object border are conquered. The proposed method shows a high repeatability of the segmentation task.

关键词： interactive image segmentation ant colony optimization morphological gradient

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RCFormer : interactive image segmentation via Reconstructing Click Vision Transformers

RCFormer : Interactive Image Segmentation via Reconstructing...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Chen, PanPan Wang, DaHan Wu, JiaHua Wu, Yun He, YiFan Zhang, XuYao Zhu, ShunZhi Xiamen Univ Technol Fujian Key Lab Pattern Recognit & Image Understan Xiamen Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing Peoples R China Reconova Technol Co Ltd Xiamen Key Lab Visual Percept Technol & Applicat Xiamen Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Click-based interactive image segmentation intends to segment an object from the background under user click guidance. Recently, Vision Transformer has made significant strides in interactive image segmentation. However, the previous studies 1) overlook the importance of different clicks in terms of their contribution to the segmentation results;and 2) suffer from inconsistency across different feature scales in the multi-scale structure. In this paper, we propose a new interactive segmentation framework, named RCFormer, with two novel components: reconstruct click patch embedding (RCPE) for encoding the importance of clicks, and multi-scale adaptive fusion (MSAF) for the adaptive fusion of feature maps across different scales. RCPE enhances the effectiveness of click interactions by spatially distinguishing the importance of clicks. MSAF adaptively fuses useful spatial information and filters the redundant feature at multi-scales. The experiments on several benchmarks show that our proposed approach achieves state-of-the-art performance. Notably, our method achieves 2.31 NoC@90 on the Berkeley dataset, improving by 8.6% over the previous best results.

关键词： interactive image segmentation Vision transformer Multi scale fusion

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GazeSAM: interactive image segmentation with Eye Gaze and Segment Anything Model 2

GazeSAM: Interactive Image Segmentation with Eye Gaze and Se...

引用

2nd Gaze Meets Machine Learning Workshop

作者： Wang, Bin Aboah, Armstrong Zhang, Zheyuan Pan, Hongyi Bagci, Ulas Northwestern Univ Chicago IL 60611 USA

interactive image segmentation aims to assist users in efficiently generating high-quality data annotations through user-friendly interactions such as clicking, scribbling, and bounding boxes. However, mouse-based interaction methods can induce user fatigue during large-scale dataset annotation and are not entirely suitable for some domains, such as radiology. This study introduces eye gaze as a novel interactive prompt for image segmentation, different than previous model-based applications. Specifically, leveraging the real-time interactive prompting feature of the recently proposed Segment Anything Model (SAM), we present the GazeSAM system to enable users to collect target segmentation masks by simply looking at the region of interest. GazeSAM tracks users' eye gaze and utilizes it as the input prompt for SAM, generating target segmentation masks in real time. To our best knowledge, GazeSAM is the first work to combine eye gaze and SAM for interactive image segmentation. Experimental results demonstrate that GazeSAM can improve nearly 50% efficiency in 2D natural image and 3D medical image segmentation tasks. The code is available in https://***/ukaukaaaa/GazeSAM.

关键词： Eye Gaze Segment Anything Model interactive image segmentation Eye Tracking

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ROBUST interactive image segmentation WITH AUTOMATIC BOUNDARY REFINEMENT`

ROBUST INTERACTIVE IMAGE SEGMENTATION WITH AUTOMATIC BOUNDAR...

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IEEE International Conference on image Processing

作者： Liu, Dingding Xiong, Yingen Shapiro, Linda Pulli, Kari Nokia Res Ctr Palo Alto CA 94304 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781424479948

We propose an effective image segmentation approach with a novel automatic boundary refinement procedure that requires little user interaction and makes the object cutout process more robust and convenient. It achieves these goals by the following three steps. First, merge over-segmented regions according to the maximal similarity rule using a few marking strokes as input. Second, detect possible erroneous low-contrast object boundaries by analyzing image content. Third, automatically refine those boundary regions using both local and global information. Experimental results are good even on very complex images.

关键词： interactive image segmentation object cutout graph cut mobile image editing mobile image processing

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SUPERCUT: AN ACCURATE AND EFFECTIVE interactive image segmentation ALGORITHM

SUPERCUT: AN ACCURATE AND EFFECTIVE INTERACTIVE IMAGE SEGMEN...

引用

20th IEEE International Conference on image Processing (ICIP)

作者： Zhu, Qingsong Shao, Ling Song, Zhan Xie, Yaoqin Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Key Lab Hlth Informat Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England Stanford Univ Sch Med Stanford CA 94305 USA

ISBN: (纸本)9781479923410

The task of interactive image segmentation has attracted a significant attention in recent years. The ultimate goal is to extract an object with as few user interactions as possible. In this paper, we present SUPERCUT, a novel interactive algorithm for foreground object extraction and segmentation in images. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently pre-segment the original image into super-pixels with precise boundary. Secondly, a Bayes decision theory is introduced to model and cluster the super-pixels so as to obtain an initial effective classification of super-pixels. To achieve a more accurate object segmentation result, a boundary refinement using interactive rectangle box with GMM learning is adopted. Experimental results on a benchmark data set show that the proposed framework is highly effective and can accurately segment a wide variety of natural images with ease.

关键词： interactive image segmentation Mean shift algorithm Graph cut interactive box

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Boosting interactive image segmentation by Exploiting Semantic Clues

Boosting Interactive Image Segmentation by Exploiting Semant...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei, Qiaoqiao Zhang, Hui Yong, Jun-Hai Tsinghua Univ BNRist Sch Software Beijing Peoples R China

ISBN: (纸本)9781665468916

This paper presents a refinement framework for enhancing the accuracy of interactive image segmentation by exploiting all available semantic clues. interactive image segmentation iteratively improves segmentation masks using an input image and user annotations. The information available in this process ranges from low-level visual features like colors and textures to high-level semantic information, such as user annotations and segmentation results. Despite tremendous efforts to segment the overall object shapes, existing methods underutilize the available semantic clues, causing unsatisfactory boundary quality for segmentation masks. The proposed framework first extracts confidence guidance maps, then suppresses and lifts the predicted probabilities for confident pixels, and finally utilizes color similarities as bases and prediction confidence as guidance to refine the segmentation boundaries. Experimental results demonstrate that the framework has a low computational cost and significantly boosts existing methods on standard benchmarks.

关键词： interactive image segmentation boundary refinement semantic clues

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Graph-based Methods for interactive image segmentation

Graph-based Methods for Interactive Image Segmentation

引用

作者： FILIP MALMBERG Uppsala University

学位级别：博士

The subject of digital image analysis deals with extracting relevant information from image data, stored in digital form in a computer. A fundamental problem in image analysis is image segmentation, i. e., the identiﬁcation and separation of relevant objects and stmctures in an image. Accurate segmentation of objects of interest is often required before further processing and analysis can be performed. Despite years of active research, fully automatic segmentation of arbitrary images remains an unsolved problem. interactive, or semi-automatic, segmentation methods use human expert knowledge as additional input, thereby making the segmentation problem more tractable. The goal of interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results. Methods for interactive segmentation typically operate under one of two paradigms for user guidance: (1) Speciﬁcation of pieces of the boundary of the desired object(s). (2) Speciﬁcation of correct segmentation labels for a small subset of the image elements. These types of user input are referred to as boundary constraints and regional constraints, respectively. This thesis concerns the development of methods for interactive segmentation, using a graph- theoretic approach. We view an image as an edge weighted graph, whose vertex set is the set of image elements, and whose edges are given by an adjacency relation among the image elements. Due to its discrete nature and mathematical simplicity, this graph based image representation lends itselfwell to the development of efficient, and provably correct, methods. The contributions in this thesis may be summarized as follows: - Existing graph-based methods for interactive segmentation are modiﬁed to improve their performance on images with noisy or missing data, while maintaining a low computational cost. - Fuzzy techniques are utilized to obtain segmentations from which feature meas

关键词： Digital image analysis interactive image segmentation Fuzzy image segmentation image foresting transform Graph labeling Graph cuts

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