Research on Automatic Identification of Substation Circuit Breaker Based on Shape Priors

2014 ◽  
Vol 511-512 ◽  
pp. 923-926 ◽  
Author(s):  
Ting Wang ◽  
Chen Yao ◽  
Jun Liu ◽  
Long Zhang Chao ◽  
Gang Hu Shao ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Circuit Breaker ◽  
Functional Model ◽  
Energy Functional ◽  
Automatic Identification ◽  
Shape Priors ◽  
Shape Prior ◽  
Shape Information ◽  
Contour Model

High-pressure circuit breakers are very important and it undertakes the disconnection and connection control of high voltage transmission lines. It is one of the equipment of substation daily inspection. State of breakers are judged by open and close characters label, so a shape-prior active contour model to realize state automatic recognition of breaker images collected by inspection robot is presented in this paper. Shape-prior active contour model combines the shape information with CV model to build energy functional model, then set up initial position curve by a priori knowledge and drives the curve evolution in minimize energy functional process, the curve position is the character label contour when energy functional shows minimum. We do experiment for the algorithm on different images, demonstrate that the algorithm based on known character contour, have good segmentation results of circuit breaker in the image character recognition accuracy and applicability when the circuit breaker character is actually partial occlusion, local deformation, scale changes.

scholarly journals A Shape Prior-Based Active Contour Model for Automatic Images Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 200541-200550
Author(s):  
Xiaoliang Jiang ◽  
Jinyun Jiang
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Shape Prior ◽  
Contour Model

IMAGE COSEGMENTATION BASED ON LOCAL AND GLOBAL LEVEL SET METHODS

2012 ◽  
Vol 12 (03) ◽  
pp. 1250019 ◽  
Author(s):  
LIHE ZHANG ◽  
ZHENZHEN LIU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Level Set Methods ◽  
Energy Functional ◽  
Image Statistics ◽  
Contour Model ◽  
Contour Models ◽  
Homogeneous Regions ◽  
The Given ◽  
Edge Indicator Function

In this paper, we propose a novel cosegmentation algorithm based on active contour model which utilizes local and global image statistics. Many localized region-based active contour models have been proposed to solve a challenging problem of the property (such as intensity, color, texture, etc.) inhomogeneities that often occurs in real images, but these models usually cannot reasonably evolve the curve in this situation that some center points along the curve are in homogeneous regions and their local regions are far away from the object. In order to overcome the difficulties we selectively enlarge the driven force of some points and introduce the edge indicator function to avoid the curve over-shrinking or over-expanding on the salient boundaries. In addition, we introduce global image statistics to better the curve evolution and try to avoid the given energy functional converging to a local minimum. Practical experiments show that our algorithm can obtain better segmentation results.

A SEGMENTATION FRAMEWORK FOR PHASE CONTRAST AND FLUORESCENCE MICROSCOPY IMAGES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460013 ◽  
Author(s):  
MITCHEL ALIOSCHA-PEREZ ◽  
RONNIE WILLAERT ◽  
HICHEM SAHLI
Keyword(s):  
Active Contour ◽  
Phase Contrast ◽  
Active Contour Model ◽  
Region Of Interest ◽  
Energy Functional ◽  
Living Cells ◽  
Shape Fitting ◽  
Contour Model ◽  
Microscopy Images

The noninvasive imaging of unstained living cells is a widely used technique in biotechnology for determining biological and biochemical role of proteins, since it allows studying living specimens without altering them. Usually, fluorescence and contrast (or transmission) images are both used complementarily, as their combination allows possible better outcomes. However, segmentation of contrast images is particularly difficult due to the presence of defocused scans, lighting/shade-off artifacts and cells overlapping. In this work, we investigate the optical properties intervening during the image formation process, and propose different segmentation strategies that can benefit from these properties. The proposed scheme (i) combines the estimated phase and the fluorescence information in order to obtain initial markers for a latter segmentation stage; and (ii) use the shear oriented polar snakes, an active contour model that implicitly involves phase information on its energy functional. The obtained contour can be used as region of interest estimation, as data for a latter shape-fitting process, or as smart markers for a more detailed segmentation process (i.e. watershed). Experimental results provide a comparison of the different segmentation schemes, and confirm the suitability of the proposed strategy and model for cell images segmentation.

A Local Statistical Information Active Contour Model for Image Segmentation

2014 ◽  
Vol 6 (2) ◽  
pp. 33-49 ◽  
Author(s):  
Shigang Liu ◽  
Yali Peng ◽  
Guoyong Qiu ◽  
Xuanwen Hao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Statistical Information ◽  
Intensity Inhomogeneity ◽  
Image Domain ◽  
Contour Model ◽  
Proposed Model ◽  
Spatially Varying ◽  
Level Set Formulation

This paper presents a local statistical information (LSI) active contour model. Assuming that the distribution of intensity belonging to each region is a Gaussian distribution with spatially varying statistical information, and defining an energy function, the authors integrate the entire image domain. Then, this energy is incorporated into a variational level set formulation. Finally, by minimizing the energy functional, a curve evolution equation can be obtained. Because the image local information is considered, the proposed model can effectively deal with the image with intensity inhomogeneity. Experimental results on synthetic and real images demonstrate that the proposed model can effectively segment the image with intensity inhomogeneity.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

scholarly journals Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Liu ◽  
Masoom A. Haider ◽  
Imam Samil Yetik
Keyword(s):  
Diffusion Weighted Imaging ◽  
Active Contour ◽  
Active Contour Model ◽  
Training Dataset ◽  
Accurate Estimation ◽  
Morphological Operations ◽  
Shape Prior ◽  
Shape Information ◽  
Diffusion Weighted

Accurate estimation of the prostate location and volume fromin vivoimages plays a crucial role in various clinical applications. Recently, magnetic resonance imaging (MRI) is proposed as a promising modality to detect and monitor prostate-related diseases. In this paper, we propose an unsupervised algorithm to segment prostate with 3D apparent diffusion coefficient (ADC) images derived from diffusion-weighted imaging (DWI) MRI without the need of a training dataset, whereas previous methods for this purpose require training datasets. We first apply a coarse segmentation to extract the shape information. Then, the shape prior is incorporated into the active contour model. Finally, morphological operations are applied to refine the segmentation results. We apply our method to an MR dataset obtained from three patients and provide segmentation results obtained by our method and an expert. Our experimental results show that the performance of the proposed method is quite successful.

scholarly journals Local- and Global-Statistics-Based Active Contour Model for Image Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Boying Wu ◽  
Yunyun Yang
Keyword(s):  
Image Segmentation ◽  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Split Bregman ◽  
Distribution Fitting ◽  
Total Variation Norm ◽  
Contour Model ◽  
Global Statistics

This paper presents a local- and global-statistics-based active contour model for image segmentation by applying the globally convex segmentation method. We first propose a convex energy functional with a local-Gaussian-distribution-fitting term with spatially varying means and variances and an auxiliary global-intensity-fitting term. A weight function that varies dynamically with the location of the image is applied to adjust the weight of the global-intensity-fitting term dynamically. The weighted total variation norm is incorporated into the energy functional to detect boundaries easily. The split Bregman method is then applied to minimize the proposed energy functional more efficiently. Our model has been applied to synthetic and real images with promising results. With the local-Gaussian-distribution-fitting term, our model can also handle some texture images. Comparisons with other models show the advantages of our model.

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