On Image Segmentation Based on Local Entropy Fitting Under Nonconvex Regularization Term Constraints

2021 ◽  
pp. 2154025
Author(s):  
Ming Han ◽  
Jing Qin Wang ◽  
Jing Tao Wang ◽  
Jun Ying Meng
Keyword(s):  
Image Segmentation ◽  
Local Minimum ◽  
Energy Functional ◽  
Local Optimum ◽  
Global Information ◽  
Local Entropy ◽  
Regularization Term ◽  
Target Segmentation ◽  
Segmentation Process ◽  
Nonconvex Regularization

The energy functional of the CV and LBF model is single, which makes the curve to get into the local minimum easily during the evolution process, and results inaccurate segmentation of the images with nonuniform grayscale and nonsmooth edges. The proposed algorithm, which is based on local entropy fitting under the constraint of nonconvex regularization term, is used to deal with such problems. In this algorithm, global information and local entropy are fitted to avoid segmentation falling into local optimum, and nonconvex regularization term is imported for constraint to protect edge smoothing. First, global information is used to evolve the approximate contour curve of the target segmentation. Then, a local energy functional with local entropy information is constructed to avoid the segmentation process from falling into a local minimum, and to precisely segment the image. Finally, nonconvex regularization terms are used in the energy functional to protect the smoothness of edge information during image segmentation process. The experimental results clearly indicate that the new algorithm can effectively resist noise, precisely segment images with nonuniform grayscale, and achieve the global optimal.

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.

A Nighttime Vehicle License Character Segmentation Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1583-1587
Author(s):  
Shang Bing Gao ◽  
Dong Jin
Keyword(s):  
Image Segmentation ◽  
Energy Functional ◽  
Character Segmentation ◽  
Uneven Distribution ◽  
Intensity Inhomogeneity ◽  
Image Information ◽  
Regularization Term ◽  
Proposed Model ◽  
Local Image ◽  
Local Term

Chan-Vese model often leads to poor segmentation results for images with intensity inhomogeneity. Aiming at the gray uneven distribution in the night vehicle images, a new local Chan–Vese (LCV) model is proposed for image segmentation. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. Finally, experiments on nighttime plate images have demonstrated that our model can segment the nighttime plate images efficently. Moreover, comparisons with recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times.

MEDICAL IMAGES SEGMENTATION USING ACTIVE CONTOURS DRIVEN BY GLOBAL AND LOCAL IMAGE FITTING ENERGY

2012 ◽  
Vol 12 (02) ◽  
pp. 1250015 ◽  
Author(s):  
HAIJUN WANG ◽  
MING LIU
Keyword(s):  
Image Segmentation ◽  
Active Contours ◽  
Energy Functional ◽  
Intensity Inhomogeneity ◽  
Image Information ◽  
Regularization Term ◽  
Global Image Information ◽  
Global And Local ◽  
Local Image ◽  
Local Term

In this paper, we propose a global and local Chan-Vese model which utilizes both global image information and local image information for image segmentation. We define an energy functional with a global term, which incorporates global image information to improve the robustness of the proposed method, and a local term which is dominant near the object boundaries. The regularization term is added to the energy functional to avoid the time-consuming re-initialization. The comparisons with the C–V model, LBF model and LGIF model show that our model can segment images with intensity inhomogeneity in less iteration steps and take less time.

Active Contour Driven by Edge and Region Image Fitting Energy

2012 ◽  
Vol 532-533 ◽  
pp. 892-896
Author(s):  
Hai Yong Xu ◽  
Ming Hua Liu
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Global Information ◽  
Geodesic Curve ◽  
Regularization Term ◽  
Level Set Function ◽  
Contour Model ◽  
Set Function

In this paper, we propose a novel edge and region-based active contour model. We consider geodesic curve and region-based model, and evolve a contour based on global information. Moreover, we add to the level set regularization term in the energy functional to ensure accurate computation and avoids expensive re-initialization of the level set function. Experiments on synthetic and real images show desirable performances of our method.

scholarly journals A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

2016 ◽  
Vol XLI-B7 ◽  
pp. 241-247 ◽  
Author(s):  
T. Kavzoglu ◽  
M. Yildiz Erdemir ◽  
H. Tonbul
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Classification Accuracy ◽  
Scale Parameter ◽  
Rate Of Change ◽  
Multi Scale ◽  
Object Based Image Analysis ◽  
Optimal Scale ◽  
Segmentation Process ◽  
Object Based

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C-Means Algorithm (NIS-IFCM)

2019 ◽  
Vol 34 (05) ◽  
pp. 2055011
Author(s):  
Jing Zhao ◽  
Xiaoli Wang ◽  
Ming Li
Keyword(s):  
Computer Vision ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Classical Problem ◽  
Local Optimum ◽  
The Novel ◽  
Swarm Optimization ◽  
Novel Method ◽  
Fuzzy C Means Algorithm ◽  
Novel Algorithm

Image segmentation is a classical problem in the field of computer vision. Fuzzy [Formula: see text]-means algorithm (FCM) is often used in image segmentation. However, when there is noise in the image, it easily falls into the local optimum, which results in poor image boundary segmentation effect. A novel method is proposed to solve this problem. In the proposed method, first, the image is transformed into a neutrosophic image. In order to improve the ability of global search, a combined FCM based on particle swarm optimization (PSO) is proposed. Finally, the proposed algorithm is applied to the neutrosophic image segmentation. The results of experiments show that the novel algorithm can eliminate image noise more effectively than the FCM algorithm, and make the boundary of the segmentation area clearer.

Multi-Frame Super-Resolution Reconstruction Algorithm Based on Diffusion Tensor Regularization Term

2014 ◽  
Vol 543-547 ◽  
pp. 2828-2832 ◽  
Author(s):  
Xiao Dong Zhao ◽  
Zuo Feng Zhou ◽  
Jian Zhong Cao ◽  
Long Ren ◽  
Guang Sen Liu ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Lagrange Equation ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Energy Functional ◽  
L1 Norm ◽  
Regularization Term ◽  
Sr Reconstruction ◽  
Image Edges ◽  
Anisotropic Diffusion Equation

This paper presents a multi-frame super-resolution (SR) reconstruction algorithm based on diffusion tensor regularization term. Firstly, L1-norm structure is used as data fidelity term, anisotropic diffusion equation with directional smooth characteristics is introduced as a prior knowledge to optimize reconstruction result. Secondly, combined with shock filter, SR reconstruction energy functional is established. Finally, Euler-Lagrange equation based on nonlinear diffusion model is exported. Simulation results validate that the proposed algorithm enhances image edges and suppresses noise effectively, which proves better robustness.

UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING

Fractals ◽  
1994 ◽  
Vol 02 (03) ◽  
pp. 363-369 ◽  
Author(s):  
WALTER S. KUKLINSKI
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Fractal Dimension ◽  
Medical Image ◽  
Tissue Characterization ◽  
Optimization Technique ◽  
Medical Image Processing ◽  
Fractal Image ◽  
Segmentation Process ◽  
Image Models

One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

Sign in / Sign up

Export Citation Format

Share Document