DYNAMIC GRAPH MERGING FOR IMAGE SEGMENTATION

2013 ◽  
Vol 11 (06) ◽  
pp. 1350051
Author(s):  
JIANPING GU ◽  
LI ZHANG ◽  
CUN CHENG
Keyword(s):  
Image Segmentation ◽  
Elastic Energy ◽  
Linear Time ◽  
Space Complexity ◽  
Variational Model ◽  
Dynamic Graph ◽  
3D Images ◽  
Time Space ◽  
New Energy ◽  
Merging Process

A new algorithm named dynamic graph merging (DGM) for automatic image segmentation is explored. Firstly a novel variational model for multi-section cut is introduced by decomposing the traditional cut into two parts, the harmonic cut and the elastic energy of the boundary. The new energy is called the continuous combined cut. Secondly a new algorithm that removes those edges with higher energy and synchronously merges their starting and ending vertices in an ordered manner is proposed. The continual merging process would iteratively contract the graph, merge those homogeneous vertices into bigger and bigger super-pixels, and fuse the remainder edges into longer and longer boundaries. So we call this algorithm dynamic graph merging. Merging criterions based on the continuous combined cut model are also discussed, which will be used to determine whether a given edge should collapse. Since the merging condition should be highly related to the image content, we present different predicates for structure images and texture images respectively. This algorithm whose efficiency is showed by experiments has a linear time/space complexity, and can efficiently segment gray/color and 2D/3D images.

scholarly journals Reconstruction of Multi-Temporal Satellite Imagery by Coupling Variational Segmentation and Radiometric Analysis

2021 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Nicola Case ◽  
Alfonso Vitti
Keyword(s):  
Image Segmentation ◽  
Variational Model ◽  
Great Level ◽  
Radiometric Analysis ◽  
Variational Segmentation ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Original Procedure ◽  
Variational Image ◽  
Homogeneous Regions

Digital images, and in particular satellite images acquired by different sensors, may present defects due to many causes. Since 2013, the Landsat 7 mission has been affected by a well-known issue related to the malfunctioning of the Scan Line Corrector producing very characteristic strips of missing data in the imagery bands. Within the vast and interdisciplinary image reconstruction application field, many works have been presented in the last few decades to tackle the specific Landsat 7 gap-filling problem. This work proposes another contribution in this field presenting an original procedure based on a variational image segmentation model coupled with radiometric analysis to reconstruct damaged images acquired in a multi-temporal scenario, typical in satellite remote sensing. The key idea is to exploit some specific features of the Mumford–Shah variational model for image segmentation in order to ease the detection of homogeneous regions which will then be used to form a set of coherent data necessary for the radiometric reconstruction of damaged regions. Two reconstruction approaches are presented and applied to SLC-off Landsat 7 data. One approach is based on the well-known histogram matching transformation, the other approach is based on eigendecomposition of the bands covariance matrix and on the sampling from Gaussian distributions. The performance of the procedure is assessed by application to artificially damaged images for self-validation testing. Both of the proposed reconstruction approaches had led to remarkable results. An application to very high resolution WorldView-3 data shows how the procedure based on variational segmentation allows an effective reconstruction of images presenting a great level of geometric complexity.

Variational model with kernel metric-based data term for noisy image segmentation

2018 ◽  
Vol 78 ◽  
pp. 42-55 ◽  
Author(s):  
Yang Liu ◽  
Chuanjiang He ◽  
Yongfei Wu
Keyword(s):  
Image Segmentation ◽  
Variational Model ◽  
Noisy Image ◽  
Data Term

On the Time-Space Complexity of Geometric Elimination Procedures

2001 ◽  
Vol 11 (4) ◽  
pp. 239-296 ◽  
Author(s):  
Joos Heintz ◽  
Guillermo Matera ◽  
Ariel Waissbein
Keyword(s):  
Space Complexity ◽  
Time Space

FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES

2021 ◽  
pp. 2150012
Author(s):  
Kuo-Lung Lor ◽  
Chung-Ming Chen
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Linear Time ◽  
Mean Shift ◽  
Texture Features ◽  
Color Image Segmentation ◽  
Image Size ◽  
Region Merging ◽  
Tissue Segmentation ◽  
Regional Pattern

The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.

How significant are the known collision and element distinctness quantum algorithms?

2004 ◽  
Vol 4 (3) ◽  
pp. 201-206
Author(s):  
L. Grover ◽  
T. Rudolph
Keyword(s):  
Search Algorithm ◽  
Quantum Algorithms ◽  
Search Space ◽  
Space Complexity ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Time Space ◽  
Simple Quantum ◽  
Structured Problems ◽  
Better Than

Quantum search is a technique for searching $N$ possibilities for a desired target in $O(\sqrt{N})$ steps. It has been applied in the design of quantum algorithms for several structured problems. Many of these algorithms require significant amount of quantum hardware. In this paper we propose the criterion that an algorithm which requires $O(S)$ hardware should be considered significant if it produces a speedup of better than $O\left(\sqrt{S}\right)$ over a simple quantum search algorithm. This is because a speedup of $O\left(\sqrt{S}\right)$ can be trivially obtained by dividing the search space into $S$ separate parts and handing the problem to $S$ independent processors that do a quantum search (in this paper we drop all logarithmic factors when discussing time/space complexity). Known algorithms for collision and element distinctness exactly saturate the criterion.

scholarly journals An Image Segmentation Method Based on Improved Regularized Level Set Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2393 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
External Energy ◽  
Regularization Term ◽  
Level Set Function ◽  
New Energy ◽  
Segmentation Approach ◽  
Set Function ◽  
Distance Regularization

When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.

scholarly journals Shannon Wavelet Precision Integration Method for Pathologic Onion Image Segmentation Based on Homotopy Perturbation Technology

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haihua Wang ◽  
Shu-Li Mei
Keyword(s):  
Image Segmentation ◽  
Burkholderia Cepacia ◽  
Integration Method ◽  
Interpolation Property ◽  
Variational Model ◽  
Nonlinear Odes ◽  
Precise Integration ◽  
Homotopy Perturbation ◽  
Analytical Scheme ◽  
Shannon Wavelet

Image segmentation variational method is good at processing the images with blurry and complicated contours, which is useful in quality identification of pathologic picture of onion. An adaptive Shannon wavelet precise integration method (WPIM) on digital image segmentation was proposed based on the image processing variational model to improve the processing speed and eliminate the artifacts of the images. First, taking full advantage of the interpolation property of the Shannon wavelet function, a multiscale Shannon wavelet interpolation scheme was constructed based on the homotopy perturbation method (HPM). The image pixels of the Burkholderia cepacia (ex-Burkholder) infected onions were taken as the collocation points of the WPIM. Then, with this scheme, the image segmentation model (C-V model) can be discretized into a system of nonlinear ODEs and solved by the half-analytical scheme combining the HPM and the precision integration method. At last, the numerical precision and efficiency of WPIM were discussed and compared with other common segmentation methods such as OSTU method and Sobel operator. The results show that the contour curve of the segmentation object obtained by the new method has many excellent properties such as closed and clear topological structure and the artifacts can be eliminated.

Globally convex variational model for multiphsae image segmentation

2015 ◽  
Author(s):  
Huaxiang Liu ◽  
Jiangxiong Fang ◽  
Liting Zhang ◽  
Huaxiang Liu ◽  
Jing Xiao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Variational Model

scholarly journals Relaxation of nonlinear elastic energies involving the deformed configuration and applications to nematic elastomers

2019 ◽  
Vol 25 ◽  
pp. 19 ◽  
Author(s):  
Carlos Mora-Corral ◽  
Marcos Oliva
Keyword(s):  
Sobolev Space ◽  
Elastic Energy ◽  
Lower Semicontinuous ◽  
Deformation Gradient ◽  
Variational Model ◽  
Nematic Elastomer ◽  
Nematic Elastomers ◽  
Lower Semicontinuous Envelope ◽  
Nonlinear Elastic

We start from a variational model for nematic elastomers that involves two energies: mechanical and nematic. The first one consists of a nonlinear elastic energy which is influenced by the orientation of the molecules of the nematic elastomer. The nematic energy is an Oseen–Frank energy in the deformed configuration. The constraint of the positivity of the determinant of the deformation gradient is imposed. The functionals are not assumed to have the usual polyconvexity or quasiconvexity assumptions to be lower semicontinuous. We instead compute its relaxation, that is, the lower semicontinuous envelope, which turns out to be the quasiconvexification of the mechanical term plus the tangential quasiconvexification of the nematic term. The main assumptions are that the quasiconvexification of the mechanical term is polyconvex and that the deformation is in the Sobolev space W1,p (with p > n − 1 and n the dimension of the space) and does not present cavitation.

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