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1st IAPR International Joint Conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Barcelos, Isabela Borlido Belem, Felipe Miranda, Paulo Falcao, Alexandre Xavier do Patrocinio, Zenilton K. G., Jr. Guimaraes, Silvio Jamil F. Pontificia Univ Catolica Minas Gerais Lab Image & Multimedia Data Sci ImSci BR-31980110 Belo Horizonte Brazil Univ Estadual Campinas Lab Image Data Sci LIDS BR-13083852 Sao Paulo Brazil Univ Estadual Campinas Inst Math & Stat BR-05508090 Sao Paulo Brazil

ISBN: (纸本)9783030766566;9783030766573

Interactive image segmentation aims to partition the image into background and foreground objects by taking into account seeds inserted by users. Nowadays, many methods are capable of generating segmentations with few user interactions, especially region-based techniques. However, such methods are highly sensitive to seed displacement and quantity, and delineation errors are often propagated to the final segmentation result. Recently, a novel superpixel segmentation framework, named Dynamic and Iterative Spanning Forest (DISF), was proposed, which achieved top delineation performance while assessing many seed-based state-of-the-art methods' drawbacks. In this work, we propose interactive DISF (iDISF), an interactive segmentation framework, by modifying each step of DISF to consider user-validated information. DISF uses the image Foresting Transform (IFT) framework for computing an optimum-path forest rooted in a seed set in the delineation step. To consider path and image gradient variation, we propose three new connectivity functions for the IFT. Finally, we also propose two new seed removal strategies for detecting relevant seeds for subsequent iterations. Results show segmentation improvements for minimal user effort-i.e., a single click-and show theoretical advances that may benefit recent optimum-path-based interactive methods from scribbles.

关键词： graph-based image segmentation Dynamic and iterative spanning forest Interactive segmentation

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graph-based Horizon Line Detection for UAV Navigation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 11683-11698页

作者： Xu, Yong Yan, Hongtao Ma, Yue Guo, Pengyu Aerosp Technol Inst CARDC Mianyang 621000 Sichuan Peoples R China Acad Mil Natl Innovat Inst Def Technol Beijing 100071 Peoples R China

Perceiving the horizon line is a critical alternative for unmanned aerial vehicle (UAV) autonomous navigation, especially in the presence of noise-induced drift, unavailability of satellite navigation, and multipath errors. However, it's quite tough to detect the horizon line, due to the remotely sensed big data, the dynamic changes in flight, and the serious consequences of failure. To address these problems, we propose a graph-based horizon line detection technique that is composed of graph-based image segmentation, connected domain cascade filtering, horizon line extraction, and UAV attitude estimation. We improve the graph-based image segmentation algorithm so that the segmentation results are more conducive to horizon line detection. We then determine the sky-component by cascade filtering and extract the horizon line based on the boundaries of the sky-component. Furthermore, we directly compute the roll and pitch according to the extracted horizon line and eliminate the ambiguity of the angles. To validate our approach qualitatively and quantitatively, we designed a fixed-wing UAV system. We then validated our algorithm through extensive flights under various conditions and compared the estimated rolls and pitches to the IMU ones. Statistical results show that the proposed technique provides unbiased attitude angles with error variance of about 2(o), which verify the validity and robustness of our method. For engineering, our program runs at approximately 60 fps on the fly after optimizing.

关键词： Unmanned aerial vehicles image segmentation image edge detection Aircraft navigation Satellite navigation systems Task analysis Simultaneous localization and mapping Connected domain cascade filtering graph-based image segmentation horizon line detection unmanned aerial vehicle (UAV) navigation

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image segmentation using dense and sparse hierarchies of superpixels

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PATTERN RECOGNITION 2020年 108卷 107532-107532页

作者： Galvao, Felipe Lemes Guimaraes, Silvio Jamil Ferzoli Falcao, Alexandre Xavier Univ Estadual Campinas Inst Comp Lab Image Data Sci Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil Pontificia Univ Catolica Minas Gerais Comp Sci Dept BR-31980110 Belo Horizonte MG Brazil

We investigate the intersection between hierarchical and superpixel image segmentation. Two strategies are considered: (i) the classical region merging, that creates a dense hierarchy with a higher number of levels, and (ii) the recursive execution of some superpixel algorithm, which generates a sparse hierarchy with fewer levels. We show that, while dense methods can capture more intermediate or higher-level object information, sparse methods are considerably faster and usually with higher boundary adherence at finer levels. We first formalize the two strategies and present a sparse method, which is faster than its superpixel algorithm and with similar boundary adherence. We then propose a new dense method to be used as post-processing from the intermediate level, as obtained by our sparse method, upwards. This combination results in a unique strategy and the most effective hierarchical segmentation method among the compared state-of-the-art approaches, with efficiency comparable to the fastest superpixel algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Superpixel segmentation Hierarchical image segmentation image foresting transform Iterative spanning forest graph-based image segmentation Irregular image pyramid

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Grab Cut image segmentation based on image Region 3

Grab Cut Image Segmentation Based on Image Region

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

作者： Li, Yubing Zhang, Jinbo Gao, Peng Jiang, Liangcheng Chen, Ming Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Jiangsu Peoples R China

ISBN: (纸本)9781538649916

Grab Cut algorithm is one of the most popular method in the field of image segmentation. It uses texture infor- mation and boundary information of image, and achieves good segmentation results with a small number of user interaction. But there are two significant drawbacks about this algorithm. Firstly, If the background is complex or the background and the object are very similar, the segmentation will not be very good. On the other hand, the relatively slow speed and Complex iterative process of the algorithm are greatly limited its application. In this paper, to develop these aspects, we proposed an improved grab cut algorithm. This algorithm is the combination of grab cut and graph-based image segmentation [1]. After the experiment, the improved algorithm is applied to more complex situation.

关键词： image segmentation grab cut graph-based image segmentation

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Method for selection macular edema region using optical coherence tomography data

COMPUTER OPTICS

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COMPUTER OPTICS 2020年第2期44卷 250-+页

作者： Ilyasova, N. Yu Demin, N. S. Shirokanev, A. S. Kupriyanov, A., V Zamytskiy, E. A. Samara Natl Res Univ Moskovskoye Shosse 34 Samara 443086 Russia IPSI RAS Molodogvardeyskaya 151 Samara 443001 Russia Samara Reg Clin Ophthalmol Hosp Samara Russia

The paper proposes a method for selection the region of diabetic macular edema in fundus images using OCT data. The relevance of the work is due to the need to create support systems for laser coagulation to increase its effectiveness. The proposed algorithm is based on a set of image segmentation methods, as well as searching for specific points and compiling their descriptors. The Canny method is used to find the boundary between the vitreous body and the retina in OCT images. The segmentation method, based on the Kruskal algorithm for constructing the minimum spanning tree of a weighted connected undirected graph, is used to select the retina to the pigment layer in the image. Using the results of segmentation, a map of the thickness of the retina of the eye and its deviation from the norm were constructed. In the course of the research, the optimal parameter values were selected in the Canny and graph segmentation algorithms, which allow to achieve a segmentation error of 5 %. SIFT, SURF, and AKAZE methods were considered for superimposing calculated maps of the retina thickness and its deviation from the norm on the fundus image. In cases where a picture from the fundus camera of the OCT apparatus is provided along with OCT data, using the SURF method, it is possible to accurately combine with the fundus image.

关键词： laser coagulation eye fundus diabetic retinopathy OCT images graph-based image segmentation SIFT SURF descriptors

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Efficient Interactive Multi-object segmentation in Medical images 15th

Efficient Interactive Multi-object Segmentation in Medical I...

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15th European Conference on Computer Vision (ECCV)

作者： Castaneda Leon, Leissi Margarita Vechiatto de Miranda, Paulo Andre Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil

ISBN: (纸本)9783030110185;9783030110178

In medical image segmentation, it is common to have several complex objects that are difficult to detect with simple models without user interaction. Hence, interactive graph-based methods are commonly used in this task, where the image is modeled as a connected graph, since graphs can naturally represent the objects and their relationships. In this work, we propose an efficient method for the multiple object segmentation of medical images. For each object, the method constructs an associated weighted digraph of superpixels, attending its individual high-level priors. Then, all individual digraphs are integrated into a hierarchical graph, considering structural relations of inclusion and exclusion. Finally, a single energy optimization is performed in the hierarchical weighted digraph satisfying all the constraints and leading to globally optimal results. The experimental evaluation on 2D medical images indicates promising results comparable to the state-of-the-art methods, with low computational complexity.

关键词： Medical image segmentation Interactive segmentation graph-based image segmentation Superpixels segmentation

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Including the Size of Regions in image segmentation by Region-based graph

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IEEE TRANSACTIONS ON image PROCESSING 2014年第2期23卷 635-644页

作者： Rezvanifar, Alireza Khosravifard, Mohammadali Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Applying a fast over-segmentation algorithm to image and working on a region-based graph (instead of the pixel-based graph) is an efficient approach to reduce the computational complexity of graph-based image segmentation methods. Nevertheless, some undesirable effects may arise if the conventional cost functions, such as Ncut, AverageCut, and MinCut, are employed for partitioning the region-based graph. This is because these cost functions are generally tailored to pixel-based graphs. In order to resolve this problem, we first introduce a new class of cost functions (containing Ncut and AverageCut) for graph partitioning whose corresponding suboptimal solution can be efficiently computed by solving a generalized eigenvalue problem. Then, among these cost functions, we propose one that considers the size of regions in the partitioning procedure. By simulation, the performance of the proposed cost function is quantitatively compared with that of the Ncut and AverageCut.

关键词： graph-based image segmentation Ncut AverageCut spectral clustering region-based graph mean-shift algorithm

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No-reference image quality assessment using Prewitt magnitude based on convolutional neural networks

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SIGNAL image AND VIDEO PROCESSING 2016年第4期10卷 609-616页

作者： Li, Jie Zou, Lian Yan, Jia Deng, Dexiang Qu, Tao Xie, Guihui Wuhan Univ Sch Elect Informat Wuhan 430072 Hubei Peoples R China

No-reference image quality assessment is of great importance to numerous image processing applications, and various methods have been widely studied with promising results. These methods exploit handcrafted features in the transformation or space domain that are discriminated for image degradations. However, abundant a priori knowledge is required to extract these handcrafted features. The convolutional neural network (CNN) is recently introduced into the no-reference image quality assessment, which integrates feature learning and regression into one optimization process. Therefore, the network structure generates an effective model for estimating image quality. However, the image quality score obtained by the CNN is based on the mean of all of the image patch scores without considering the human visual system, such as edges and contour of images. In this paper, we combine the CNN and the Prewitt magnitude of segmented images and obtain the image quality score using the mean of all the products of the image patch scores and weights based on the result of segmented images. Experimental results on various image distortion types demonstrate that the proposed algorithm achieves good performance.

关键词： No-reference image quality assessment Convolutional neural networks (CNNs) graph-based image segmentation Prewitt magnitude

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Traffic Sign Detection via graph-based Ranking and segmentation Algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2015年第12期45卷 1509-1521页

作者： Yuan, Xue Guo, Jiaqi Hao, Xiaoli Chen, Houjin Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

The majority of existing traffic sign detection systems utilize color or shape information, but the methods remain limited in regard to detecting and segmenting traffic signs from a complex background. In this paper, we propose a novel graph-based traffic sign detection approach that consists of a saliency measure stage, a graph-based ranking stage, and a multithreshold segmentation stage. Because the graph-based ranking algorithm with specified color and saliency combines the information of color, saliency, spatial, and contextual relationship of nodes, it is more discriminative and robust than the other systems in terms of handling various illumination conditions, shape rotations, and scale changes from traffic sign images. Furthermore, the proposed multithreshold segmentation algorithm focuses on all the nodes with a nonzero ranking score, which can effectively solve problems such as complex background, occlusion, various illumination conditions, and so on. The results for three public traffic sign sets show that our proposed approach leads to better performance than the current state-of-the-art methods. Moreover, the results are satisfactory even for images containing traffic signs that have been rotated or undergone occlusion, as well as for images that were photographed under different weather and illumination conditions.

关键词： graph-based image analysis graph-based image segmentation traffic sign detection

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Salient Region Detection based on SLIC and graph-based segmentation

Salient Region Detection Based on SLIC and Graph-based Segme...

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2016 4th International Conference on Advanced Materials and Information Technology Processing(AMITP 2016)

作者： Xiaofei Sun Wenwen Pan Xia Wang Xuhong Li Guan Wang College of Information Science and Engineering Zaozhuang University

At present, the most recent saliency detection algorithms are still not satisfactory. A saliency detection algorithm based on SLIC and graph-based segmentation is proposed. Firstly, graph-based segmentation is used to obtain larger image partitions, and the partitions with good contours are acquired. Then the relatively detailed partitions are obtained using SLIC image segmentation. Sparse color histogram is applied to these two methods. By using the color and spatial distance information, the saliency of each partition is calculated. Contours of salient objects from saliency maps generated using graph-based segmentation are acquired and the gray scale values of the two kinds of saliency maps in the area surrounded by contours are merged. Experimental results show that compared with other detection methods, the proposed algorithm can effectively detect the salient object in the image.

关键词： saliency map graph-based image segmentation SLIC color sparse histogram

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