3-D Gradient and Curvature Measurement Using Local Image Information

1990 ◽  
Author(s):  
Harry S. Gallarda ◽  
Leonard H. Bieman ◽  
Kevin G. Harding
Keyword(s):  
Image Information ◽  
Curvature Measurement ◽  
Local Image

scholarly journals Active contour model for satellitale image combining local and global region based approach

2019 ◽  
Vol 15 (1) ◽  
pp. 247
Author(s):  
Mouri Hayat ◽  
Fizazi Hadria
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Multiple Objects ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Segmentation Of Images ◽  
Global And Local ◽  
Local Image

<p>Global and local image information is crucial for accurate segmentation of images with intensity inhomogeneity valuable minute details and multiple objects with various intensities. We propose a region-based active contour model which is able to utilize together local and global image information. The major contribution of this paper is to expand the LIF model which is includes only local image infofmation to a local and global model. The introduction of a new local and global signed pressure force function enables the extraction of accurate local and global image information and extracts multiple objects with several intensities. Several tests performed on some synthetic and real images indicate that our model is effective as well as less sensitivity to the initial contour location and less time compared with the related works. </p><p><em> </em></p>

A Variation Level Set Formulation Local Based C-V Model for Medical Images Segmentation

2013 ◽  
Vol 756-759 ◽  
pp. 3696-3701
Author(s):  
Yan Yu ◽  
Chao Bing Huang ◽  
Ling Li
Keyword(s):  
Active Contour ◽  
Medical Images ◽  
Local Information ◽  
Image Information ◽  
Piecewise Constant ◽  
Contour Models ◽  
Segmentation Of Images ◽  
Level Set Formulation ◽  
Local Fitting ◽  
Local Image

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity which usually occurs in medical images. However, image information in local region is not incorporated in popular region-based active contour models, such as piecewise constant models and piecewise smooth models. In this paper, a method which is able to use local information is proposed. The main point is the introduction of the local fitting information expressed by a kernel function which is crucial for segmentation. Our method is based on piecewise constant Chan-Vese model, and compare with different methods for several synthetic images and medical images.

scholarly journals An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

2018 ◽  
Vol 8 (12) ◽  
pp. 2576 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Yun Tian
Keyword(s):  
Image Segmentation ◽  
Weight Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Mean Value ◽  
Segmentation Method ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Local Image

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.

Region-Based Level Set Model for Image Segmentation

2012 ◽  
Vol 616-618 ◽  
pp. 2223-2228 ◽  
Author(s):  
Da Chuan Wei
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Energy Functional ◽  
Intensity Inhomogeneity ◽  
Image Information ◽  
Penalty Term ◽  
Proposed Model ◽  
Global Image Information ◽  
The Impact ◽  
Local Image

To reduce the impact of intensity inhomogeneity to image segmentation, a region-based level set (RBLS) model was proposed in this study. Its energy functional consists of four terms: local term, area term, length term and penalty term. The proposed model utilizes both global image information and local image information, and by using the local image information, the image with intensity inhomogeneity can be efficiently segmented. In addition, the global implementation of our RBLS model is introduced. It can detect all of the targets in the image. The experimental results showed that the proposed model can segment the image with intensity inhomogeneity efficiently, which is better than that of CV model.

scholarly journals Use of local image information in depth edge classification by humans and neural networks

2018 ◽  
Vol 18 (10) ◽  
pp. 128
Author(s):  
Krista Ehinger ◽  
Wendy Adams ◽  
Erich Graf ◽  
James Elder
Keyword(s):  
Neural Networks ◽  
Image Information ◽  
Edge Classification ◽  
Local Image

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.

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