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3rd IEEE International Conference on image, Vision and Computing (ICIVC)

作者： Utah, Matee Iltaf, Adnan Hou, Qiujun Ali, Farman Liu, Chuancai Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538649916

In the literature of computer vision, many techniques have demonstrated their potentials for interactive image segmentation. However, most of these state-of-the-art algorithms are unable to produce accurate boundaries without more user interaction, as they are highly sensitive to the seed's quantity and quality. These techniques frequently depend on more user interaction to refine the boundaries. In order to solve the problem and get accurate boundaries via less user interaction, in this work, a robust interactive image segmentation method is proposed based on generic multiscale oriented contours via single forward pass Convolutional Neural Networks (CNNs) and graph cut framework. We first utilize CNN to construct the boundary-level information and then combine this boundary-level information with the boundary energy term of graph cut framework. The proposed method exhibits a significant leap in robustness to user interaction, smooth boundaries, accurate segmentation and the ability to handle the changes. We show that the sensitivity to the seeds can be controlled and accuracy can be improved via boundary-level information. We further conduct both qualitative and quantitative experiments on benchmark datasets, showing that our proposed method outperforms the state-of-the-art interactive image segmentation techniques.

关键词： interactive image segmentation deep learning CNN graph cut

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A Bayesian Network Model for Automatic and interactive image segmentation

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IEEE TRANSACTIONS ON image PROCESSING 2011年第9期20卷 2582-2593页

作者： Zhang, Lei Ji, Qiang UtopiaCompression Corp Los Angeles CA 90064 USA Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention.

关键词： Active labeling Bayesian network (BN) image segmentation interactive image segmentation

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Graph-based Methods for interactive image segmentation

Graph-based Methods for Interactive Image Segmentation

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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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SuperCut: Superpixel Based Foreground Extraction With Loose Bounding Boxes in One Cutting

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IEEE SIGNAL PROCESSING LETTERS 2017年第12期24卷 1803-1807页

作者： Wu, Shuqiong Nakao, Megumi Matsuda, Tetsuya Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

interactive image segmentation that uses a bounding box containing the foreground has gained great popularity because of its convenience. However, its performance is often degraded when the bounding box is not tight enough or covers large background regions. To solve this problem, this letter proposes a novel segmentation algorithm called "SuperCut". This algorithm provides robust segmentation in one cut even with loose bounding boxes. In the proposed SuperCut, superpixels of the whole image are first computed, and then a background similarity map for the superpixels inside the bounding box is created based on Haar-Wavelet feature and pixel intensities. Finally, a max-flow cut is made based on the map. The background similarity map reduces the influence from possible background regions inside the bounding box, which leads to the efficient segmentation of SuperCut. To evaluate the effectiveness of the proposed SuperCut, we compared it with some recent published interactive image segmentation techniques. The experimental results show that the proposed SuperCut outperforms other compared methods especially when using loose bounding boxes.

关键词： Bounding box GrabCut Graph cut interactive image segmentation OneCut

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Human Part segmentation in Depth images with Annotated Part Positions

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SENSORS 2018年第6期18卷 1900-1900页

作者： Hynes, Andrew Czarnuch, Stephen Mem Univ Dept Elect & Comp Engn St John NF A1B 3X5 Canada

We present a method of segmenting human parts in depth images, when provided the image positions of the body parts. The goal is to facilitate per-pixel labelling of large datasets of human images, which are used for training and testing algorithms for pose estimation and automatic segmentation. A common technique in image segmentation is to represent an image as a two-dimensional grid graph, with one node for each pixel and edges between neighbouring pixels. We introduce a graph with distinct layers of nodes to model occlusion of the body by the arms. Once the graph is constructed, the annotated part positions are used as seeds for a standard interactive segmentation algorithm. Our method is evaluated on two public datasets containing depth images of humans from a frontal view. It produces a mean per-class accuracy of 93.55% on the first dataset, compared to 87.91% (random forest and graph cuts) and 90.31% (random forest and Markov random field). It also achieves a per-class accuracy of 90.60% on the second dataset. Future work can experiment with various methods for creating the graph layers to accurately model occlusion.

关键词： human parts interactive image segmentation occlusion grid graph

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

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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

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interactive Exemplar-based segmentation Toolkit for Biomedical image Analysis 12

Interactive Exemplar-based Segmentation Toolkit for Biomedic...

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IEEE 12th International Symposium on Biomedical Imaging

作者： Li, Xiang Zhou, Zhi Keller, Philipp Zeng, Hongkui Liu, Tianming Peng, Hanchuan Univ Georgia Dept Comp Sci Cort Architecture Imaging & Discovery Lab Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA Allen Inst Brain Sci Seattle WA 98109 USA HHMI Janelia Farm Res Campus Ashburn VA USA

ISBN: (纸本)9781479923748

In the field of biomedical imaging analysis on single-cell level, reliable and fast segmentation of the cell nuclei from the background on three-dimensional images is highly needed for the further analysis. In this work we propose an interactive cell segmentation toolkit that first establishes a set of exemplar regions from user input through an easy and intuitive interface in both 2D and 3D in real-time, then extracts the shape and intensity features from those exemplars. Based on a local contrast-constrained region growing scheme, each connected component in the whole image would be filtered by the features from exemplars, forming an "exemplar-matching" group which passed the filtering and would be part of the final segmentation result, and a "non-exemplar-matching" group in which components would be further segmented by the gradient vector field based algorithm. The results of the filtering process are visualized back to the user in near real-time, thus enhancing the experience in exemplar selecting and parameter tuning. The toolkit is distributed as a plugin within the open source Vaa3D system (http://***).

关键词： interactive image segmentation learning based approach

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interactive RGB-D image segmentation Using Hierarchical Graph Cut and Geodesic Distance 16th

Interactive RGB-D Image Segmentation Using Hierarchical Grap...

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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Ge, Ling Ju, Ran Ren, Tongwei Wu, Gangshan Nanjing Univ Collaborat Innovat Ctr Novel Software Technol & I State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9783319240756;9783319240749

In this paper, we propose a novel interactive image segmentation method for RGB-D images using hierarchical Graph Cut. Considering the characteristics of RGB channels and depth channel in RGB-D image, we utilize Euclidean distance on RGB space and geodesic distance on 3D space to measure how likely a pixel belongs to foreground or background in color and depth respectively, and integrate the color cue and depth cue into a unified Graph Cut framework to obtain the optimal segmentation result. Moreover, to overcome the low efficiency problem of Graph Cut in handling high resolution images, we accelerate the proposed method with hierarchical strategy. The experimental results show that our method outperforms the state-of-the-art methods with high efficiency.

关键词： interactive image segmentation RGB-D image Graph cut Geodesic distance Scale space

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USER DRIVEN SPARSE POINT-BASED image segmentation 23

USER DRIVEN SPARSE POINT-BASED IMAGE SEGMENTATION

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23rd IEEE International Conference on image Processing (ICIP)

作者： Meena, Sachin Palaniappan, Kannappan Seetharaman, Guna Univ Missouri Dept Comp Sci Columbia MO 65211 USA US Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9781467399616

Reducing the amount of user driven input for interactive image segmentation enables faster and more precise foreground extraction of objects. A sparse collection of labeled seed points sampled over image regions can be quickly provided by the user using a few mouse clicks. Seed points are used for training an Elastic Body Spline classifier mapping function. We evaluate the efficiency and accuracy of user defined point inputs compared to fully manual boundary drawing that can be time consuming and automatic image segmentation methods that may not have sufficient accuracy. We show that using an average of just 8 labeled pixels (i.e. sparse set of seed points) the proposed EBS foreground-background thresholding method can achieve 90 percent accuracy compared to manual ground truth on the Berkeley BSDS500 benchmark.

关键词： interactive image segmentation seed points semi-supervised regression elastic body splines

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interactive image segmentation BASED ON OBJECT CONTOUR FEATURE image

INTERACTIVE IMAGE SEGMENTATION BASED ON OBJECT CONTOUR FEATU...

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IEEE International Conference on image Processing

作者： Chen, Qiang Xue, Benben Sun, Quansen Xia, Deshen Nanjing Univ Sci & Technol Sch Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781424479948

This paper presents a new interactive image segmentation method. The key idea is to generate the object contour feature image according to a small number of user supplied object contour points, and then adopt the parametric active contour model for object segmentation. An image patch matching with rotation-invariance is utilized to generate object contour feature image. In order to prevent the evolving curve running into local optimal solution, the initial evolving curve of the parametric active contour is constructed by local intensity values of the object contour feature image. Medical image segmentation results indicate that the proposed method is superior to the traditional parametric active contour model, and is an effective semi-automatic image segmentation method.

关键词： interactive image segmentation parametric active contour image patch matching rotation-invariance

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