共查询到20条相似文献,搜索用时 15 毫秒
1.
3D object segmentation is important in computer vision such as target detection in biomedical image analysis. A new method, called B-Surface algorithm, is generated for 3D object segmentation. An improved 3D external force field combined with the normalized GVF is utilized. After the initialization of a surface model near the target, B-Surface starts to deform to locate the boundary of the object. First, it overcomes the difficulty that comes from analyzing 3D volume image slice by slice. And the speed of B-Surface deformation is enhanced since the internal forces are not needed to compute in every iteration deformation step. Next, the normal at every surface point can be calculated easily since B-Surface is a continuous deformable model. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Experimental results and analysis are presented in this paper. We can see that the B-Surface algorithm can find the surface of the target efficiently. 相似文献
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Partitioning 3D surface meshes using watershed segmentation 总被引:14,自引:0,他引:14
Mangan A.P. Whitaker R.T. 《IEEE transactions on visualization and computer graphics》1999,5(4):308-321
This paper describes a method for partitioning 3D surface meshes into useful segments. The proposed method generalizes morphological watersheds, an image segmentation technique, to 3D surfaces. This surface segmentation uses the total curvature of the surface as an indication of region boundaries. The surface is segmented into patches, where each patch has a relatively consistent curvature throughout, and is bounded by areas of higher, or drastically different, curvature. This algorithm has applications for a variety of important problems in visualization and geometrical modeling including 3D feature extraction, mesh reduction, texture mapping 3D surfaces, and computer aided design 相似文献
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Farhang Sahba Hamid R. Tizhoosh Magdy M.M.A. Salama 《Expert systems with applications》2008,35(3):772-780
The principal contribution of this work is to design a general framework for an intelligent system to extract one object of interest from ultrasound images. This system is based on reinforcement learning. The input image is divided into several sub-images, and the proposed system finds the appropriate local values for each of them so that it can extract the object of interest. The agent uses some images and their ground-truth (manually segmented) version to learn from. A reward function is employed to measure the similarities between the output and the manually segmented images, and to provide feedback to the agent. The information obtained can be used as valuable knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images. The experimental results for prostate segmentation in trans-rectal ultrasound images show high potential of this approach in the field of ultrasound image segmentation. 相似文献
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This paper presents different methods, some based on geometric algebra, for ultrasound probe tracking in endoscopic images, 3D allocation of the ultrasound probe, ultrasound image segmentation (to extract objects like tumors), and 3D reconstruction of the surface defined by a set of points. The tracking of the ultrasound probe in endoscopic images is done with a particle filter and an auxiliary method based on thresholding in the HSV space. The 3D pose of the ultrasound probe is calculated using conformal geometric algebra (to locate each slide in 3D space). Each slide (ultrasound image) is segmented using two methods: the level-set method and the morphological operators approach in order to obtain the object we are interested in. The points on the object of interest are obtained from the segmented ultrasound images, and then a 3D object is obtained by refining the convex hull. To do that, a peeling process with an adaptive radius is applied, all of this in the geometric algebra framework. Results for points from ultrasound images, as well as for points from objects from the AimatShape Project, are presented (A.I.M.A.T.S.H.A.P.E. – Advanced an Innovative Models And Tools for the development of Semantic-based systems for Handling, Acquiring, and Processing knowledge Embedded in multidimensional digital objects). 相似文献
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2D-to-3D conversion that would be a solution of the lack of 3D contents has been a worthy and challenging research field. In this paper, we propose a computer interactive conversion method to capture components which is used to generate 3D sequences. First, we divide the key frame into foreground and background, and then label the objects by convenient computer interactive operation. Depth information of objects is labeled after segmentation. Second, we use object tracking technique which synthesizes the advantages of kernel-based mean shift tracker and contour tracker to accomplish object depth capture for non-key frame. Finally, all the 3D information is prepared to render 3D sequences. After all, we propose our future work direction: a 2D-to-3D system which can generate 3D sequence interactively. 相似文献
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We propose an algorithm for automatically obtaining a segmentation of a rigid object in a sequence of images that are calibrated for camera pose and intrinsic parameters. Until recently, the best segmentation results have been obtained by interactive methods that require manual labelling of image regions. Our method requires no user input but instead relies on the camera fixating on the object of interest during the sequence. We begin by learning a model of the object’s colour, from the image pixels around the fixation points. We then extract image edges and combine these with the object colour information in a volumetric binary MRF model. The globally optimal segmentation of 3D space is obtained by a graph-cut optimisation. From this segmentation an improved colour model is extracted and the whole process is iterated until convergence. 相似文献
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This paper presents a genetic based incremental neural network (GINeN) for the segmentation of tissues in ultrasound images. Performances of the GINeN and the Kohonen network are investigated for tissue segmentation in ultrasound images. Feature extraction is carried out by using continuous wavelet transform. Pixel intensities at the same spatial location on 12 wavelet planes and on the original image are considered as features, leading to 13-dimensional feature vectors. The same training set is used for the training of the Kohonen network and the GINeN.
This paper proposes the use of wavelet transform and genetic based incremental neural network together in order to increase the segmentation performance. It is observed that genetic based incremental neural network gives satisfactory segmentation performance for ultrasound images. 相似文献
9.
Piergiorgio Cerello Author Vitae Sorin Christian Cheran Author Vitae Author Vitae Roberto Bellotti Author Vitae Author Vitae Ezio Catanzariti Author Vitae Author Vitae Maria Evelina Fantacci Author Vitae Author Vitae Gianfranco Gargano Author Vitae Author Vitae Ernesto López Torres Author Vitae Author Vitae Cristiana Peroni Author Vitae Author Vitae 《Pattern recognition》2010,43(4):1476-1490
3-D object segmentation is an important and challenging topic in computer vision that could be tackled with artificial life models.A Channeler Ant Model (CAM), based on the natural ant capabilities of dealing with 3-D environments through self-organization and emergent behaviours, is proposed.Ant colonies, defined in terms of moving, pheromone laying, reproduction, death and deviating behaviours rules, is able to segment artificially generated objects of different shape, intensity, background.The model depends on few parameters and provides an elegant solution for the segmentation of 3-D structures in noisy environments with unknown range of image intensities: even when there is a partial overlap between the intensity and noise range, it provides a complete segmentation with negligible contamination (i.e., fraction of segmented voxels that do not belong to the object). The CAM is already in use for the automated detection of nodules in lung Computed Tomographies. 相似文献
10.
Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool
in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this
paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple
model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex,
non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation
of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image
intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting
point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the
contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images.
Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise. 相似文献
11.
The segmentation of left ventricle in ultrasound imaging of human heart would provide an important clinical parameter for the evaluation of cardiac functions including volume stroke or ejection fraction and wall motion tracking. We propose a fast segmentation method to reduce laborious manual efforts and conveniently provide robust and stable cardiac quantification to users. The proposed method provides a very simple energy functional form using a predetermined Rayleigh distribution parameter so that the corresponding steepest descent approach with some shape constraints on contour is still capable of fast segmentation. We present several experimental results on two-dimensional echocardiography data for the performance of the proposed model. The experiments show that the proposed model is especially useful when a part of target boundary is seriously corrupted. 相似文献
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Multimedia Tools and Applications - Point cloud segmentation is the premise and basis of many 3D perception tasks, such as intelligent driving, object detection and recognition, scene recognition... 相似文献
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Jarrell Waggoner Youjie Zhou Jeff Simmons Marc De Graef Song Wang 《Machine Vision and Applications》2014,25(6):1615-1629
Segmenting materials’ images is a laborious and time-consuming process, and automatic image segmentation algorithms usually contain imperfections and errors. Interactive segmentation is a growing topic in the areas of image processing and computer vision, which seeks to find a balance between fully automatic methods and fully-manual segmentation processes. By allowing minimal and simplistic interaction from the user in an otherwise automatic algorithm, interactive segmentation is able to simultaneously reduce the time taken to segment an image while achieving better segmentation results. Given the specialized structure of materials’ images and level of segmentation quality required, we show an interactive segmentation framework for materials’ images that has three key contributions: (1) a multi-labeling approach that can handle a large number of structures while still quickly and conveniently allowing manual addition and removal of segments in real-time, (2) multiple extensions to the interactive tools which increase the simplicity of the interaction, and (3) a web interface for using the interactive tools in a client/server architecture. We show a full formulation of each of these contributions and example results from their application. 相似文献
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This paper describes a man-made object segmentation method for aerial images based on a modified watershed segmentation algorithm. Our segmentation procedure includes three steps: (1) a multi-scaled geometric image analysis of aerial images by the non-subsampled contourlet transform (NSCT) method, (2) watershed segmentation, and (3) region classification of man-made objects. First, background of multi-scaled geometric image analysis is introduced briefly, and NSCT is used to represent the features for the purpose of man-made object segmentation. Thanks to the properties of NSCT, it not only avoids pseudo-Gibbs phenomena around singularities in image de-noising with regard to shift invariance, but it also enriches the set of basis functions, which makes it possible to extract orientational contour of man-made objects more effectively. In the NSCT decomposition step, the best basis selection is employed for ensuring maximum information content. Second, the “texture gradient” of combined features is calculated based on the first NSCT decomposition step and the resulting best basis selection, afterward the watershed transform is applied. According to their feature values, the aerial images are divided into several homogenous regions. Third, the fractional Brownian motion (fBm) model is used to determine the man-made object regions. Last, the experimental results show that the outcome of man-made object segmentation becomes more continuous and satisfying as a result of the homogenous texture-regions extraction and the modified watershed procedure. 相似文献
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This paper presents an incremental neural network (INeN) for the segmentation of tissues in ultrasound images. The performances of the INeN and the Kohonen network are investigated for ultrasound image segmentation. The elements of the feature vectors are individually formed by using discrete Fourier transform (DFT) and discrete cosine transform (DCT). The training set formed from blocks of 4x4 pixels (regions of interest, ROIs) on five different tissues designated by an expert is used for the training of the Kohonen network. The training set of the INeN is formed from randomly selected ROIs of 4x4 pixels in the image. Performances of both 2D-DFT and 2D-DCT are comparatively examined for the segmentation of ultrasound images. 相似文献
19.
A new algorithm is presented for interpreting two-dimensional (2D) line drawings as three-dimensional (3D) objects without models. Even though no explicit models or additional heuristics are included, the algorithm tends to reach the same 3D interpretations of 2D line drawings that humans do. The algorithm explicitly calculates the partial derivatives of Marill's Minimum Standard Deviation of Angles (MSDA) with respect to all adjustable parameters, and follows this gradient to minimize SDA. For an image with lines meeting atm points formingn angles, the gradient descent algorithm requiresO(n) time to adjust all the points, while Marill's method requiredO(mn) time to do so. Experimental results on various line drawing objects show that this gradient descent algorithm running on a Macintosh II is one to two orders of magnitude faster than the MSDA algorithm running on a Symbolics, while still giving comparable results. 相似文献