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7th Annual IEEE International Systems Conference (SysCon)

作者： Karasev, Peter Vela, Patricio Tannenbaum, Allen Univ Alabama ECE Georgia Tech ECE Ind Percept Inc Palo Alto CA 94303 USA Georgia Tech Sch ECE Atlanta GA USA Univ Alabama Birmingham Dept ECE Birmingham AL 35487 USA

ISBN: (纸本)9781467331081;9781467331074

Traditionally, image segmentation algorithms are viewed as black-box measurement sources. In this work, the system-level formulation of closed-loop tracking is reformulated under the assumption of access to physical sensor data in addition to the video stream. The scope is restricted to algorithms that iteratively minimize a functional to localize the target in each image frame. A nominal iterative algorithm is an open-loop system;IMU data is then used to generate a control to modify the algorithm's behavior and thus create a closed-loop system. Compensating using IMU data in a feedback setting creates the possibility of induced track loss, whereby the compensation effectively undoes the action of the motion control. This issue is overcome by solving an optimization program that exploits known control intent to separate ego-motion disturbances from the control. As a result, the reformulation proposed in this paper enables improved tracking of a fast-moving target even in the presence of strong disturbances/jitter in the camera platform.

关键词： control performance image segmentation image segmentation algorithm algorithms Standing Orders physical sensor Targets optimization program Video stream fast-moving

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segmentation of Tumor Ultrasound image via Region-Based Ncut Method

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Wuhan University Journal of Natural Sciences 2013年第4期18卷 313-318页

作者： QUAN Long ZHANG Dong YANG Yan LIU Yu QIN Qianqing School of Physics and Technology Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensoringWuhan University

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.

关键词： tumor ultrasound image Ncut method pre-segment image segmentation algorithm

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Medical image segmentation Based on Biomimetic Pattern Recognition

Medical Image Segmentation Based on Biomimetic Pattern Recog...

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World Congress on Software Engineering

作者： Jiafu Jiang He Wei Qi Qi Changsha Univ Sci & Technol Coll Comp & Commun Engn Changsha 410114 Hunan Peoples R China

ISBN: (纸本)9780769535708

A medical image segmentation algorithm based on biomimetic pattern recognition is proposed First, psi(3) -neurons' weights are determined according to training samples and then Multi-weight Neuron Networks are established. Second, the neuron networks are used to completely cover the samples' high-dimensional feature space Finally, medical images are recognized and segmented based on the results of the optimal coverage of the samples The experimental results show that the proposed method has higher accuracy, reliability and better generalization ability than the traditional medical image segmentation methods Besides, from the cognitive respective, this algorithm emphasizes on "cognition", which can effectively integrate transcendental knowledge and obtain the desired object from medical images quickly and reliably, therefore it is highly intelligent

关键词： image segmentation Pattern recognition Medical best covering image segmentation algorithm Biomimetics Eigenvector Neurons

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Intuition in Medical image segmentation: Visualizing Graph Edge Weights

Intuition in Medical Image Segmentation: Visualizing Graph E...

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15th International Conference on Information Visualisation (IV)

作者： Beasley, Ryan A. Wagner, Christopher R. Texas A&M Univ College Stn TX 77843 USA Orthorun Ltd Ware Herts England

ISBN: (纸本)9780769544762

Weighting functions for graph-based medical image segmentation algorithms (e.g., Graphcut) have a significant effect on the segmentation, but to our knowledge no tool provides the user with intuition towards their proper selection. The large variety, their complexity, and the limited feedback hinders comparison of choices. This paper describes a package developed to visualize the effects of various edge weighting functions and parameters, in which the image of interest is overlaid with colors depicting the relative distances from the nearest seed to each voxel. By seeing the colors vary while changing parameters, the user gains intuition into the various options for the edge weighting function. A user study demonstrating the benefits of the package is presented. It is our hope that the intuition provided by the software will result in less time required to segment medical images in the clinical work-flow.

关键词： Intuition WEIGHTING FUNCTIONS encapsulating image segmentation algorithm Edge of a figure or solid Medical pixel image segmentation

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Geometric image segmentation via transform invariant rank cuts

Geometric image segmentation via transform invariant rank cu...

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作者： Abdulhussein, Hussein University of Missouri - Columbia

学位级别：M.S.

This research propose a novel image segmentation algorithm, named as Transform Invariant Rank Cuts (TIRC). Based on salient 3D geometric information of natural scenes. The segmentation algorithm unities an emerging robust statistics technique called Robust PCA and its recent application in Transform Invariant Low-Rank Texture (TILT) extraction. This proposed novel algorithms address two critical issues that have handicapped the applications of the TILT feature. First, we propose a simple yet efficient algorithm to detect low-rank texture regions in natural images. Second, TIRC is a principled graph-cut solution to partition the TILT features into groups; each group represents a unique 3D planar structure. Using a TILT adjacency graph, the algorithm assigns a TILT feature as a node. Two nodes are connected if they are spatially adjacent, with the cut cost function defined as the total coding length of encoding the two texture regions as low-rank matrices separately. Finally, the classical graph-cut algorithm can be applied to partition the graph into sub-graphs, each of which represents a unique surface texture and 3D orientation. The efficacy and visual quality of this geometric image segmentation algorithm is demonstrated on a large urban scene database.

关键词： image segmentation algorithm Robust PCA low-rank texture regions

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Applying morphology to improve Canny operator's image segmentation method

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8816-8819页

作者： Xu, Hongyun Xu, Xiaoli Zuo, Yunbo Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Modern Educ Beijing 100192 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

When a single image segmentation method is used for image segmentation of wind turbine blade images under a complex background, the results obtained are not accurate and complete. This article proposes an image segmentation algorithm that applies morphology to Canny edge detection. It uses morphological opening to erode and dilate the binary image after Canny algorithm processing, and removes redundant edge information to obtain a complete fan blade image. Experimental results show that the results obtained by the image segmentation method proposed here have good integrity and accuracy, and can improve the segmentation effect of the image.

关键词： edge detection wind turbines blades image segmentation Canny algorithm processing redundant edge information complete fan blade image segmentation effect Canny operator single image segmentation method wind turbine blade images complex background image segmentation algorithm Canny edge detection morphological opening binary image

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KNOWLEDGE-BASED 3D ANALYSIS FROM 2D MEDICAL imageS

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IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE 1991年第4期10卷 30-37页

作者： DHAWAN, AP ARATA, L Department of Electrical and Computer Engineering University of Cincinnati USA

An anatomical knowledge-based system for image analysis that interprets CT/MR (computed tomography/magnetic resonance) images of the human chest cavity is reported. The approach utilizes a low-level image analysis system with the ability to analyze the data in bottom-up (or data-driven) and top-down (or model-driven) modes to improve the high-level recognition process. Several image segmentation algorithms, including K-means clustering, pyramid-based region extraction, and rule-based merging, are used for obtaining the segmented regions. To obtain a reasonable number of well-segmented regions that have a good correlation with the anatomy, a priori knowledge in the form of masks is used to guide the segmentation process. segmentation of the brain is also considered.

关键词： Thoracic Cavity image analysis system Anatomy Masks Medical imaging image segmentation algorithm three dimensional analysis image analysis

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RGB-D image segmentation BASED ON MULTIPLE RANDOM WALKERS

RGB-D IMAGE SEGMENTATION BASED ON MULTIPLE RANDOM WALKERS

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IEEE International Conference on image Processing

作者： Se-Ho Lee Won-Dong Jang Byung Kwan Park Chang-Su Kim School of Electrical Engineering Korea University Video Tech. Lab. SK Telecom

ISBN: (纸本)9781467399623

A novel RGB-D image segmentation algorithm is proposed in this work. This is the first attempt to achieve image segmentation based on the theory of multiple random walkers (MRW). We construct a multi-layer graph, whose nodes are superpixels divided with various parameters. Also, we set an edge weight to be proportional to the similarity of color and depth features between two adjacent nodes. Then, we segment an input RGB-D image by employing MRW simulation. Specifically, we decide the initial probability distribution of agents so that they are far from each other. We then execute the MRW process with the repulsive restarting rule, which makes the agents repel one another and occupy their own exclusive regions. Experimental results show that the proposed MRW image segmentation algorithm provides competitive segmentation performances, as compared with the conventional state-of-the-art algorithms.

关键词： Multiple random walkers segmentation Random walk RGB-D image segmentation Random walk Probability distribution image segmentation algorithm images Agents image segmentation adjacent nodes

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Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2019年第2期40卷 468-505页

作者： Yilmaz, Volkan Gungor, Oguz Karadeniz Tech Univ Dept Geomat TR-61080 Trabzon Turkey

Field measurements are the main source of information when determining stand parameters, which are essential to produce an effective forest management plan. However, conducting terrestrial measurements is neither time- nor cost-efficient in most cases. In recent years, the advent of sophisticated remote sensing technologies has enabled the extraction of accurate and robust information about the physical characteristics of trees. Crown diameter is one of the most important stand parameters that should be measured or estimated. This study proposes a Polynomial Fitting Based (PFB) methodology to estimate crown diameters of urban trees with Unmanned Aerial System (UAS)-based data. Crown diameters estimated with the PFB methodology were compared not only to a reference data but also to those estimated based on five widely used image segmentation algorithms, which were the Mean Shift segmentation (MSS), Morphological Profiles Based segmentation (MPBS), Multiresolution segmentation (MRS), Seeded Region Growing segmentation (SRGS) and Watershed segmentation (WS). Quantitative investigations revealed that the PFB approach outperformed the other segmentation-based approaches. The PFB approach estimated the crown diameters with root-mean-square errors (RMSE) ranging from 0.69 m to 0.92 m. The PFB methodology was found to be a practical and robust approach for the estimation of crown diameters, which plays a very significant role in effective forest management.

关键词： Urban forestry Information source image segmentation algorithm Diameter UNMANNED

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A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2018年第3期39卷 684-703页

作者： Jozdani, Shahab E. Momeni, Mehdi Johnson, Brian A. Sattari, Mehran Univ Isfahan Dept Geomat Engn Esfahan Iran Inst Global Environm Strategies Hayama Kanagawa Japan

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding 'optimal' (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration;and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable re

关键词： Buildings data processing scale parameter urban area image segmentation algorithm built structure image resolution

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