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Integration of the saliency-based seeds generation and random walks with restart for image segmentation

JOURNAL OF ELECTRONIC IMAGING 2021年第4期30卷 043016-043016页

作者： Lin, Kaibin Li, Qiaoliang Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha Peoples R China Higher Educ Key Lab Data Min & Intelligent Recomm Xiamen Peoples R China

In the task of interactive image segmentation, the user initially indicates some pixels as target seeds and background seeds and propagates the seeds' labels to the rest of the image. We propose an equidistant seed generation method for interactive image segmentation that combines saliency detection and random walk (RW) with restart with optimal restarting probability. Our method includes two stages: first, we utilize the saliency detection method to obtain the initial target region and generate the initial seeds. Then we relabel the seeds using a Gaussian mixture model with limited user interaction to obtain accurate seeds. The relabeled seeds can limit the range of object;therefore, in the second stage, we can select an optimal restarting probability by calculating the area of the RW that exceeds the limited range of the object seeds. Our method can generate effective seeds and thus reduce the dependence on user interaction. Furthermore, using the optimal restarting probability, we can obtain higher segmentation accuracy. Extensive experiments on the Berkeley segmentation dataset, GrabCut dataset, and MSRA1000 dataset demonstrate that our method can reduce the dependence on user interaction and achieve better performance than the latest interactive image segmentation methods, such as RW, RW with restart, normalized RW, sub-Markov RW, and label propagation through complex networks. (C) 2021 SPIE and IS&T

关键词： interactive image segmentation random walk with restart saliency map optimal restarting probability

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Color image segmentation Using Adaptive GrowCut Method

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Procedia Computer Science 2015年 45卷 328-335页

作者： B. Basavaprasad Ravindra S. Hegadi Research and Development Centre Bharathiar University Coimbatore 641 046 Department of Computer Science Solapur University Solapur 413 255

An adaptive algorithm for interactive multi-label segmentation of 2-dimensional color images using GrowCut has been carry out in this technique. Except certain small number of user-labelled pixels, the remainder of the image is segmented automatically by a layered automaton. This technique is iterative, since the mechanism labels the image automatically. The segmentation task is complex to compute. User can monitor the progress of segmentation and redirect the algorithm with respect to human participation. The proposed system contains two phases. During the first level the original input color image is segmented using the GrowCut method. In the following phase the resulting image is processed under color image segmentation method. In the areas, where the segmentation is dependably computed automatically no further user effort is necessary. Outcomes of segmenting standard photos and images are presented. Our experiments show that a little user effort is required for segmentation of moderately tough images.

关键词： GraphCut GrowCut layered automata interactive image segmentation foreground extraction image editing.

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Constrained fixation point based segmentation via deep neural network

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NEUROCOMPUTING 2019年 368卷 180-187页

作者： Li, Gongyang Liu, Zhi Shi, Ran Wei, Weijie Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

It is an explicit mode to use the clicking points by the mouse in the interactive image segmentation, while an implicit interaction mode is to use the fixation points from the eye-tracking device. Both modes can provide a series of points. Inspired by the similarity between these two interaction modes, we propose a novel human visual system (HVS) based neural network for transferring the constrained fixation point based segmentation to the clicking point based interactive segmentation. Briefly speaking, the sequence of information transmission and processing in our model is RGB image, VGG-16 backbone, LGN-like module (LGNL) and ConvLSTM block, which correspond to the pathway of stimulus transmission and processing, i.e. stimulus, retina, lateral geniculate nucleus (LGN) and visual cortex in the HVS. First, the RGB image is fed to the VGG-16 backbone to obtain the multiple-layer feature maps. Then the LGNL is adopted to effectively incorporate edge-aware features and semantic features from different layers of the VGG-16 backbone in multiple resolutions, so as to produce rich contextual features. Finally, with the guidance of the fixation density map transformed from the fixation points, the output feature maps of LGNL are utilized to generate the segmentation map via a stack of ConvLSTM blocks in a coarse-to-fine manner. Comprehensive experiments demonstrate that the proposed HVS based neural network achieves a higher segmentation performance and outperforms seven state-of-the-art methods, and prove that the transfer from constrained fixation points to clicking points is reasonable and valid. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Human visual system Object segmentation interactive image segmentation Fixation point Deep neural network

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VISIBLY BETTER EDGE-DETECTION USING OBSERVED image CONTRASTS

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PATTERN RECOGNITION LETTERS 1994年第7期15卷 641-647页

作者： KAUSHAL, TP IBM UK Scientific Centre Aquitaine House St Clement Street Winchester SO23 9DR United Kingdom

For an interactive, computer-aided image segmentation tool to work convincingly, the edges marked by it should, where possible, match those observed by the user on the displayed image (illusory edges excluded). An improvement in edge correspondence can be obtained by calculating observed luminance contrasts. These account for both the non-linearity of the screen and for the veiling effect of room illumination on the displayed image. It is shown how a logarithmic transformation of the photopic luminance data can be used in conjunction with the Canny operator to produce a visually convincing contrast edge map.

关键词： LUMINANCE CONTRAST LOGARITHMIC TRANSFORMATION CANNY OPERATOR EDGE-DETECTION interactive image segmentation

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interactive Skin Wound segmentation Based on Feature Augment Networks

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第7期27卷 3467-3477页

作者： Zhang, Pengfei Chen, Xinjian Yin, Ziting Zhou, Xin Jiang, Qingxin Zhu, Weifang Xiang, Dehui Tang, Yun Shi, Fei Soochow Univ MIPAV Lab Sch Elect & Informat Engn Suzhou 215006 Peoples R China Soochow Univ State Key Lab Radiat Med & Protect Suzhou 215006 Peoples R China Suzhou Sci & Technol Town Hosp Suzhou 215153 Peoples R China

Skin wound segmentation in photographs allows non-invasive analysis of wounds that supports dermatological diagnosis and treatment. In this paper, we propose a novel feature augment network (FANet) to achieve automatic segmentation of skin wounds, and design an interactive feature augment network (IFANet) to provide interactive adjustment on the automatic segmentation results. The FANet contains the edge feature augment (EFA) module and the spatial relationship feature augment (SFA) module, which can make full use of the notable edge information and the spatial relationship information be-tween the wound and the skin. The IFANet, with FANet as the backbone, takes the user interactions and the initial result as inputs, and outputs the refined segmentation result. The pro-posed networks were tested on a dataset composed of miscellaneous skin wound images, and a public foot ulcer segmentation challenge dataset. The results indicate that the FANet gives good segmentation results while the IFANet can effectively improve them based on simple marking. Comprehensive comparative experiments show that our proposed networks outperform some other existing automatic or interactive segmentation methods, respectively.

关键词： Deep learning feature augment network interactive image segmentation wound image

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Dynamic Interaction Dilation for interactive Human Parsing

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 178-189页

作者： Gao, Yutong Lang, Congyan Liu, Fayao Cao, Yuanzhouhan Sun, Lijuan Wei, Yunchao Beijing Jiaotong Univ Minist Educ Key Lab Big Data & Artificial Intelligence Transpo Beijing 100044 Peoples R China ASTAR Inst Infocomm Res Singapore 138632 Singapore Beijing Jiaotong Univ Sch Comp Sci & Informat Technol Beijing 100044 Peoples R China Beijing Univ Posts & Telecommun Sch Econ & Management Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 10044 Peoples R China

interactive segmentation pursues generating high-quality pixel-level predictions with a few user-provided clicks, which is gaining attention for its convenience in segmentation data annotation. Users are allowed to iteratively refine the prediction by adding clicks until the result is satisfactory. Existing interactive methods usually transform the clicks into a set of localization maps by Euclidian distance computation or RGB texture extraction to guide the segmentation, which makes the click transformation a core module in interactive segmentation networks. However, when adopted in human images where large poses, occlusions, and bad illuminations are prevailing, prior transformation methods tend to cause uncorrectable overlapping across localization maps which are difficult to form a good match among human parts. Furthermore, the inappropriately transformed information is hard to be refined with the static transformation manner which is out of tune with the dynamically refined interaction process. Hence, we design a dynamic transformation scheme for interactive human parsing (IHP) named Dynamic Interaction Dilation Net (DID-Net), which serves as an initial attempt to break the limitations of static transformation while capturing long-range dependencies of clicks within each human part. Specifically, we construct a Dynamic Dilation Module (DD-Module) to dilate clicks radially in several directions assisted by human body edge detection to refine the dilation quality in each interaction iteration. Furthermore, we propose an Adaptive Interaction Excitation Block (AIE-Block) to exploit potential semantic clues buried in the dilated clicks. Our DID-Net achieves state-of-the-art performance on 3 public human parsing benchmarks.

关键词： Semantics image edge detection Annotations Location awareness Feature extraction Transforms Task analysis Human parsing interactive image segmentation semantic image segmentation

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Global graph diffusion for interactive object extraction

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INFORMATION SCIENCES 2018年 460卷 103-114页

作者： Wang, Tao Yang, Jian Sun, Quansen Ji, Zexuan Fu, Peng Ge, Qi Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing Jiangsu Peoples R China

Many interactive image segmentation methods design energy functions based on the local relationship of neighboring pixels, which is insufficient at capturing the abundant information of an image, and these methods are susceptible to many problems, such as sensitivity to seeds and under-segmentation of boundaries. To solve these problems, this paper explores utilizing the global relationship for interactive image segmentation. To effectively obtain the global information of the image, we first propose a robust affinity diffusion (RAD) method to propagate the local affinity graph. Compared with the existing diffusion based approaches, the advantage of RAD is that it can converge to an effective limit value, which makes the diffusion process more computationally efficient and easier to control. The segmentation model is then constructed based on this convergent global graph. To efficiently utilize the global information, the energy function is designed by the multiplication of the global affinity matrix and a prior probability vector. The use of global information can significantly improve the segmentation performance. Experiments on challenging data sets demonstrate that RAD can obtain better results than state-of-the-art methods. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Color image interactive image segmentation Global information Graph diffusion

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3D visualization to assist iterative object definition from medical images

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2006年第4期30卷 217-230页

作者： Audigier, Romaric Lotufo, Roberto Falcao, Alexandre Univ Estadual Campinas UNICAMP Inst Comp BR-13084971 Campinas SP Brazil Univ Estadual Campinas UNICAMP Sch Elect & Comp Engn BR-13083852 Campinas SP Brazil

In medical imaging, many applications require visualization and/or analysis of three-dimensional (3D) objects (e.g. organs). At same time, object definition often requires considerable user assistance. In this process, objects are usually defined in an iterative way and their visualization during the process is very important to guide the user's actions for the next iteration. The usual procedure provides slice visualization during object definition (segmentation) and 3D visualization afterward. In this paper, we propose and evaluate efficient methods to provide 3D visualization during iterative object definition. The methods combine the differential image foresting transform for segmentation with voxel splatting/ray casting for visualization. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： 3D visualization interactive image segmentation watershed transform image foresting transform voxel splatting raycasting rendering of medical images

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Neutro-Connectedness Cut

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IEEE TRANSACTIONS ON image PROCESSING 2016年第10期25卷 4691-4703页

作者： Xian, Min Zhang, Yingtao Cheng, Heng-Da Xu, Fei Ding, Jianrui Utah State Univ Dept Comp Sci Logan UT 84322 USA Harbin Inst Technol Sch Comp Sci Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci Weihai 264209 Peoples R China

interactive image segmentation is a challenging task and receives increasing attention recently;however, two major drawbacks exist in interactive segmentation approaches. First, the segmentation performance of region of interest (ROI)-based methods is sensitive to the initial ROI: different ROIs may produce results with great difference. Second, most seed-based methods need intense interactions, and are not applicable in many cases. In this paper, we generalize the neutro-connectedness (NC) to be independent of top-down priors of objects and to model image topology with indeterminacy measurement on image regions, propose a novel method for determining object and background regions, which is applied to exclude isolated background regions and enforce label consistency, and put forward a hybrid interactive segmentation method, NC Cut (NC-Cut), which can overcome the above two problems by utilizing both pixelwise appearance information and region-based NC properties. We evaluate the proposed NC-Cut by employing two image data sets (265 images), and demonstrate that the proposed approach outperforms the state-of-the-art interactive image segmentation methods (Grabcut, MILCut, One-Cut, MGC(max)(sum), and pPBC).

关键词： interactive image segmentation topology neutro-connectedness NC-Cut

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Fast Cost-Volume Filtering for Visual Correspondence and Beyond

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第2期35卷 504-511页

作者： Hosni, Asmaa Rhemann, Christoph Bleyer, Michael Rother, Carsten Gelautz, Margrit Vienna Univ Technol Inst Software Technol & Interact Syst Interact Media Syst Grp Favoritenstr 9-11-188-2 A-1040 Vienna Austria Microsoft Res Cambridge Cambridge England

Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.

关键词： Stereo matching optical flow interactive image segmentation

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