Steel Wire Infrared Imagery Defect Detection Based on Gradient Vector Flow Model

Li Li-Jun,Sun Guo-Shuai 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.3

In order to solve the problem regarding steel wire defect detection, a gradient vector flow calculation method based on extended neighborhood is proposed in this article and applied to the steel wire defect detection. The new method is used to analyze the gradient vector flow model from the angle of mask film, wherein the original four- neighborhood mask film is replaced by the mask film with a larger neighborhood, thus to obtain the gradient vector flow calculation method based on the extended neighborhood. Actually, the new calculation method only needs less iterations to obtain better effect. Experimentally, the algorithm proposed in this article is feasible and has high algorithm execution efficiency and high complexity.

기울기 벡터장과 조건부 엔트로피 결합에 의한 의료영상 정합

이명은,김수형,김선월,임준식 한국정보처리학회 2010 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.17 No.4

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods. 본 논문에서는 기울기 벡터장과 조건부 엔트로피를 결합한 의료영상 정합 방법을 제안한다. 정합 방법은 조건부 확률의 엔트로피에 기반한 측도를 수행한다. 먼저 공간적 정보를 얻기 위해 윤곽선 정보의 방향을 제공하는 기울기 정보인 기울기 벡터장을 계산한다. 다음으로 주어진 두 영상에서 픽셀의 밝기정보와 에지정보를 결합하여 조인트 히스토그램을 계산하여 조건부 엔트로피를 구하고, 이것을 두 영상의 정합측도로 사용한다. 제안된 방법의 성능평가를 위해 자기공명 영상과 변환된 컴퓨터단층촬영 영상에 기존 방법인 상호정보기반의 측도, 조건부 엔트로피만을 사용한 측도와 비교 실험을 수행한다. 실험결과로부터 제안한 방법이 기존의 최적화 방법들 보다 더 빠르고 정확한 정합임을 알 수 있다.

A Comparative Study on Left and Right Endocardium Segmentation using Gradient Vector Field and Adaptive Diffusion Flow Algorithms

Yabrin Amin,Shoaib Amin Banday,A.H.Mir 보안공학연구지원센터 2016 International Journal of Bio-Science and Bio-Techn Vol.8 No.1

The cardiac Magnetic Resonance Imaging (MRI) provides high resolution images of the heart without radiation exposure. It is an excellent noninvasive test used by radiologist for proper detection of heart diseases. The manual segmentation of left ventricle in cine short axis MRI sequences takes an ample amount of time as compared to semi-automated segmentation. In Gradient Vector Flow (GVF) model certain barriers hinder the performance such as weak edge detection, high computational time, limited capture range and its ambiguity with other parameters. In this paper segmentation of Endocardium is carried out on multistage MRI frames Using Adaptive Diffusion flow (ADF) model. This deformable model was tested on large scale number of Cardiac MRI images. We replace the smoothening energy term in GVF with active hyper-surface harmonic minimal function in order to avoid possible leakage at weak edges. The use of harmonic maps is adjusted in accordance with image characteristics. We also assimilate infinite Laplace function to move active contours into narrow concave sections. Experimental results and collation with GVF are presented in this paper which reveals several good results based on extraction of endocardium tissue from left and right ventricle, including less computational time, noise robustness and weak edge preserving on Cardiac MR Images.

광 흐름과 학습에 의한 영상 내 사람의 검지

도용태 ( Yongtae Do ) 한국센서학회 2020 센서학회지 Vol.29 No.3

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.

Analyses of Computation Time on Snakes and Gradient Vector Flow

Kwak, Young-Tae 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.2

GVF can solve two difficulties with Snakes that are on setting initial contour and have a hard time processing into boundary concavities. But GVF takes much longer computation time than the existing Snakes because of their edge map and partial derivatives. Therefore this paper analyzed the computation time between GVF and Snakes. As a simulation result, both algorithms took almost similar computation time in simple image. In real images, GVF took about two times computation than Snakes.

Analyses of Computation Time on Snakes and Gradient Vector Flow

Young Tae Kwak 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.2

Analyses of Computation Time on Snakes and Gradient Vector Flow

곽영태 한국데이터정보과학회 2007 한국데이터정보과학회지 Vol.18 No.2

GVF can solve two difficulties with Snakes that are on setting initial contour and have a hard time processing into boundary concavities. But GVF takes much longer computation time than the existing Snakes because of their edge map and partial derivatives. Therefore this paper analyzed the computation time between GVF and Snakes. As a simulation result, both algorithms took almost similar computation time in simple image. In real images, GVF took about two times computation than Snakes.

Study of Feature-Based Image Capturing and Recognition Algorithm

Chih-Hong Kao,Sheng-Ping Hsieh,Chih-Cheng Peng 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10

In the field of applied computer vision, the three-dimensional (3D) object recognition is a very important technique which can be used to determine objects from a certain direction of the image for arbitrary 3D objects. These skills are useful in military applications, such as moving target recognition and coastal surveillance. Computer vision recognition allows fast response and all day long reconnaissance. The purpose of establishing a ship recognition system is to research and develop effective ship contour capture in the natural sea environment. We can develop a ship recognition system that is reliable and fast based on a database of ship images. We propose a recognition algorithm for ship images. This system utilizes gradient vector flow to capture the ship image contour first and calculates the geometric eigenvalue using this contour and Fourier descriptor. The eigenvalues are used to perform separate rough and detailed recognition. A graphic user interface is developed and the validity of the proposed technique is demonstrated using identifying images.

Automatic Segmentation of Phalanges Regions on CR Images Based on MSGVF Snakes

Shota KAJIHARA,Seiichi MURAKAMI,Hyoungseop KIM,Joo Kooi TAN 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10

Rheumatoid arthritis and osteoporosis are two common orthopedic diseases. Rheumatoid arthritis is a disease that inflammation occurs in the joint, which always causes the joints are able to move freely. Osteoporosis is a disease that bone mineral content is reduced and risk of fragility fracture increases. As one of the diagnostic methods, medical imaging by photographed CR equipment has been widely accepted. However, some problems such as mass screening data sets and mis-diagnosis are still remained in visual screening. In order to solve these problems and reduce the burden to physicians, needs of an automatic diagnosis system capable of performing quantitative analysis is anticipated. In this paper, we carry out the development of a segmentation method of phalanges regions from CR images of the hand to perform a quantitative evaluation of rheumatoid arthritis and osteoporosis. The proposed method is carried out crude segmentation of phalanges regions from CR images of the hand, and extracts the detailed phalanges regions by Multi Scale Gradient Vector Flow Snakes (MSGVF) method. In our study, we performed Snakes algorithm to give an initial control points on MSGVF algorithm. We applied our method on three pairs of CR temporal images of phalanges regions, which are called as the previous images and the current images. We got the segmentation results of 5.95 [%] of false-positive rate and 92.9 [%] of true-positive rate.

Automatic Initialization Active Contour Model for the Segmentation of the Chest Wall on Chest CT

최석윤,김창수 대한의료정보학회 2010 Healthcare Informatics Research Vol.16 No.1

Objectives: Snake or active contours are extensively used in computer vision and medical image processing applications, and particularly to locate object boundaries. Yet problems associated with initialization and the poor convergence to boundary concavities have limited their utility. The new method of external force for active contours, which is called gradient vector flow (GVF), was recently introduced to address the problems. Methods: This paper presents an automatic initialization value of the snake algorithm for the segmentation of the chest wall. Snake algorithms are required to have manually drawn initial contours, so this needs automatic initialization. In this paper, our proposed algorithm is the mean shape for automatic initialization in the GVF. Results: The GVF is calculated as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the medical images. Finally, the mean shape coordinates are used to automatic initialize thepoint of the snake. The proposed algorithm is composed of three phases: the landmark phase, the procrustes shape distance metric phase and aligning a set of shapes phase. The experiments showed the good performance of our algorithm in segmenting the chest wall by chest computed tomography. Conclusions: An error analysis for the active contours results on simulated test medical images is also presented. We showed that GVF has a large capture range and it is able to move a snake into boundary concavities. Therefore, the suggested algorithm is better than the traditional potential forces of image segmentation.