Relevant bibliographies by topics / Geometric active contour

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Geometric active contour'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Geometric active contour.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Geometric active contour"

1

Papandreou, George, and Petros Maragos. "Multigrid Geometric Active Contour Models." IEEE Transactions on Image Processing 16, no. 1 (2007): 229–40. http://dx.doi.org/10.1109/tip.2006.884952.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhu, Guopu. "Dual geometric active contour for image segmentation." Optical Engineering 45, no. 8 (2006): 080505. http://dx.doi.org/10.1117/1.2333566.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Zhenglong, Qingshan Liu, Hanqing Lu, and Dimitris N. Metaxas. "Lennard-Jones force field for geometric active contour." Signal Processing 90, no. 4 (2010): 1249–66. http://dx.doi.org/10.1016/j.sigpro.2009.10.008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

LI, Danyi, Weifeng LI, and Qingmin LIAO. "A Fuzzy Geometric Active Contour Method for Image Segmentation." IEICE Transactions on Information and Systems E96.D, no. 9 (2013): 2107–14. http://dx.doi.org/10.1587/transinf.e96.d.2107.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wei, Xueyun, Wei Zheng, Caiping Xi, and Shang Shang. "Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model." Remote Sensing 13, no. 4 (2021): 642. http://dx.doi.org/10.3390/rs13040642.

Full text
Abstract:
Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.
APA, Harvard, Vancouver, ISO, and other styles
6

HE, Chuan-Jiang. "Anisotropic Diffusion of Halting Speed Fields in Geometric Active Contour Model." Journal of Software 18, no. 3 (2007): 600. http://dx.doi.org/10.1360/jos180600.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Jong, Dong Pyo, Sun Kim, Doo Soo Lee, and Hee Lee Lee. "The segmentation of computed tomography using the geometric active contour model." Journal of Digital Imaging 11, S1 (1998): 209. http://dx.doi.org/10.1007/bf03168311.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zheng, Ying, Guangyao Li, Xiehua Sun, and Xinmin Zhou. "A geometric active contour model without re-initialization for color images." Image and Vision Computing 27, no. 9 (2009): 1411–17. http://dx.doi.org/10.1016/j.imavis.2009.01.001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Sivakumar, K., Jayashree S, Kaavya K, and Pooja S. "Active Contour driven by geometric mean and standard deviation-based energy fitting model for the left ventricle segmentation from cardiac magnetic resonance images." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1407–16. http://dx.doi.org/10.51201/jusst/21/06455.

Full text
Abstract:
This paper proposes a geometric mean and standard deviation-based energy fitting model to improve the accuracy of segmentation of the left ventricle from cardiac Magnetic Resonance Imaging (MRI). Energy-fitting-based active contour models emerged so far suffer either from intensity inhomogeneity or gives wrong segmentation result due to an inappropriate initial contour. Thus, accurate and robust segmentation of the left ventricle from cardiac MRI still a challenging problem. Therefore, to tackle both the problems, a geometric mean-based energy-fitting model is proposed. Unlike the recent energy-fitting-based models which use the arithmetic mean to calculate the local energy, the proposed method uses geometric mean and scaled standard deviation to compute the energy functional which drives the active contour to the region of interest. In addition to that completely removes the initial contour problem by automating it according to the input. The initial contour in the proposed model is a circle its radius and the center are calculated from the input sample itself. This initial contour is an appropriate and automated one that helps to reduce the computation time for segmentation. Experiments are conducted on cardiac MRI images the result obtained is compared with ground truth and evaluated by Average perpendicular distance (APD) and DICE similarity coefficient. Further the visual, as well as evaluated parameters, evidences that the proposed model performs better than the existing methods.
APA, Harvard, Vancouver, ISO, and other styles
10

Sun, Lei, Sun-an Wang, Jin-hua Zhang, and Xiao-hu Li. "Robust Object Tracking with Mutualism between Particle Filter and Geometric Active Contour." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 12 (2015): 335–50. http://dx.doi.org/10.14257/ijsip.2015.8.12.31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Geometric active contour"

1

Tsang, Po-Yan. "Multi-resolution Image Segmentation using Geometric Active Contours." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/907.

Full text
Abstract:
Image segmentation is an important step in image processing, with many applications such as pattern recognition, object detection, and medical image analysis. It is a technique that separates objects of interests from the background in an image. Geometric active contour is a recent image segmentation method that overcomes previous problems with snakes. It is an attractive method for medical image segmentation as it is able to capture the object of interest in one continuous curve. The theory and implementation details of geometric active contours are discussed in this work. The robustness of the algorithm is tested through a series of tests, involving both synthetic images and medical images. Curve leaking past boundaries is a common problem in cases of non-ideal edges. Noise is also problematic for the advancement of the curve. Smoothing and parameters selection are discussed as ways to help solve these problems. This work also explores the incorporation of the multi-resolution method of Gaussian pyramids into the algorithm. Multi-resolution methods, used extensively in the areas of denoising and edge-selection, can help capture the spatial structure of an image. Results show that similar to the multi-resolution methods applied to parametric active contours, the multi-resolution can greatly increase the computation without sacrificing performance. In fact, results show that with successive smoothing and sub-sampling, performance often improves. Although smoothing and parameter adjustment help improve the performance of geometric active contours, the edge-based approach is still localized and the improvement is limited. Region-based approaches are recommended for further work on active contours.
APA, Harvard, Vancouver, ISO, and other styles
2

Yan, Lin. "REGION-BASED GEOMETRIC ACTIVE CONTOUR FOR CLASSIFICATION USING HYPERSPECTRAL REMOTE SENSING IMAGES." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315344636.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Niu, Xutong. "Highway extraction from high resolution aerial photography using a geometric active contour model." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1101833084.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Judkovich, Michael. "An Active Contour Approach for 3D Thigh Muscle Segmentation." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1618866341802777.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Westlund, Arvid. "Image analysis tool for geometric variations of the jugular veins in ultrasonic sequences : Development and evaluation." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-348336.

Full text
Abstract:
The aim of this project is to develop and perform a first evaluation of a software, based on the active contour, which automatically computes the cross-section area of the internal jugular veins through a sequence of 90 ultrasound images. The software is intended to be useful in future research in the field of intra cranial pressure and its associated diseases. The biomechanics of the internal jugular veins and its relationship to the intra cranial pressure is studied with ultrasound. It generates data in the form of ultrasound sequences shot in seven different body positions, supine to upright. Vein movements in cross section over the cardiac cycle are recorded for all body positions. From these films, it is interesting to know how the cross-section area varies over the cardiac cycle and between body positions, in order to estimate the pressure. The software created was semi-automatic, where the operator loads each individual sequence and sets the initial contour on the first frame. It was evaluated in a test by comparing its computed areas with manually estimated areas.  The test showed that the software was able to track and compute the area with a satisfactory accuracy for a variety of sequences. It is also faster and more consistent than manual measurements. The most difficult sequences to track were small vessels with narrow geometries, fast moving walls, and blurry edges. Further development is required to correct a few bugs in the algorithm. Also, the improved algorithm should be evaluated on a larger sample of sequences before using it in research.
APA, Harvard, Vancouver, ISO, and other styles
6

Tsang, Po-Yan. "Multi-resolution image segmentation using geometirc active contours." Waterloo, Ont. : University of Waterloo, 2004. http://etd.uwaterloo.ca/etd/ptsang2004.pdf.

Full text
Abstract:
Thesis (M.A.Sc.)--University of Waterloo, 2004.<br>"A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master in Applied Science in Systems Design Engineering". Includes bibliographical references.
APA, Harvard, Vancouver, ISO, and other styles
7

Xu, Yanli. "Une mesure de non-stationnarité générale : Application en traitement d'images et du signaux biomédicaux." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0090/document.

Full text
Abstract:
La variation des intensités est souvent exploitée comme une propriété importante du signal ou de l’image par les algorithmes de traitement. La grandeur permettant de représenter et de quantifier cette variation d’intensité est appelée une « mesure de changement », qui est couramment employée dans les méthodes de détection de ruptures d’un signal, dans la détection des contours d’une image, dans les modèles de segmentation basés sur les contours, et dans des méthodes de lissage d’images avec préservation de discontinuités. Dans le traitement des images et signaux biomédicaux, les mesures de changement existantes fournissent des résultats peu précis lorsque le signal ou l’image présentent un fort niveau de bruit ou un fort caractère aléatoire, ce qui conduit à des artefacts indésirables dans le résultat des méthodes basées sur la mesure de changement. D’autre part, de nouvelles techniques d'imagerie médicale produisent de nouveaux types de données dites à valeurs multiples, qui nécessitent le développement de mesures de changement adaptées. Mesurer le changement dans des données de tenseur pose alors de nouveaux problèmes. Dans ce contexte, une mesure de changement, appelée « mesure de non-stationnarité (NSM) », est améliorée et étendue pour permettre de mesurer la non-stationnarité de signaux multidimensionnels quelconques (scalaire, vectoriel, tensoriel) par rapport à un paramètre statistique, et en fait ainsi une mesure générique et robuste. Une méthode de détection de changements basée sur la NSM et une méthode de détection de contours basée sur la NSM sont respectivement proposées et appliquées aux signaux ECG et EEG, ainsi qu’a des images cardiaques pondérées en diffusion (DW). Les résultats expérimentaux montrent que les méthodes de détection basées sur la NSM permettent de fournir la position précise des points de changement et des contours des structures tout en réduisant efficacement les fausses détections. Un modèle de contour actif géométrique basé sur la NSM (NSM-GAC) est proposé et appliqué pour segmenter des images échographiques de la carotide. Les résultats de segmentation montrent que le modèle NSM-GAC permet d’obtenir de meilleurs résultats comparativement aux outils existants avec moins d'itérations et de temps de calcul, et de réduire les faux contours et les ponts. Enfin, et plus important encore, une nouvelle approche de lissage préservant les caractéristiques locales, appelée filtrage adaptatif de non-stationnarité (NAF), est proposée et appliquée pour améliorer les images DW cardiaques. Les résultats expérimentaux montrent que la méthode proposée peut atteindre un meilleur compromis entre le lissage des régions homogènes et la préservation des caractéristiques désirées telles que les bords ou frontières, ce qui conduit à des champs de tenseurs plus homogènes et par conséquent à des fibres cardiaques reconstruites plus cohérentes<br>The intensity variation is often used in signal or image processing algorithms after being quantified by a measurement method. The method for measuring and quantifying the intensity variation is called a « change measure », which is commonly used in methods for signal change detection, image edge detection, edge-based segmentation models, feature-preserving smoothing, etc. In these methods, the « change measure » plays such an important role that their performances are greatly affected by the result of the measurement of changes. The existing « change measures » may provide inaccurate information on changes, while processing biomedical images or signals, due to the high noise level or the strong randomness of the signals. This leads to various undesirable phenomena in the results of such methods. On the other hand, new medical imaging techniques bring out new data types and require new change measures. How to robustly measure changes in theos tensor-valued data becomes a new problem in image and signal processing. In this context, a « change measure », called the Non-Stationarity Measure (NSM), is improved and extended to become a general and robust « change measure » able to quantify changes existing in multidimensional data of different types, regarding different statistical parameters. A NSM-based change detection method and a NSM-based edge detection method are proposed and respectively applied to detect changes in ECG and EEG signals, and to detect edges in the cardiac diffusion weighted (DW) images. Experimental results show that the NSM-based detection methods can provide more accurate positions of change points and edges and can effectively reduce false detections. A NSM-based geometric active contour (NSM-GAC) model is proposed and applied to segment the ultrasound images of the carotid. Experimental results show that the NSM-GAC model provides better segmentation results with less iterations that comparative methods and can reduce false contours and leakages. Last and more important, a new feature-preserving smoothing approach called « Nonstationarity adaptive filtering (NAF) » is proposed and applied to enhance human cardiac DW images. Experimental results show that the proposed method achieves a better compromise between the smoothness of the homogeneous regions and the preservation of desirable features such as boundaries, thus leading to homogeneously consistent tensor fields and consequently a more reconstruction of the coherent fibers
APA, Harvard, Vancouver, ISO, and other styles
8

Lee, Jehoon. "Statistical and geometric methods for visual tracking with occlusion handling and target reacquisition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43582.

Full text
Abstract:
Computer vision is the science that studies how machines understand scenes and automatically make decisions based on meaningful information extracted from an image or multi-dimensional data of the scene, like human vision. One common and well-studied field of computer vision is visual tracking. It is challenging and active research area in the computer vision community. Visual tracking is the task of continuously estimating the pose of an object of interest from the background in consecutive frames of an image sequence. It is a ubiquitous task and a fundamental technology of computer vision that provides low-level information used for high-level applications such as visual navigation, human-computer interaction, and surveillance system. The focus of the research in this thesis is visual tracking and its applications. More specifically, the object of this research is to design a reliable tracking algorithm for a deformable object that is robust to clutter and capable of occlusion handling and target reacquisition in realistic tracking scenarios by using statistical and geometric methods. To this end, the approaches developed in this thesis make extensive use of region-based active contours and particle filters in a variational framework. In addition, to deal with occlusions and target reacquisition problems, we exploit the benefits of coupling 2D and 3D information of an image and an object. In this thesis, first, we present an approach for tracking a moving object based on 3D range information in stereoscopic temporal imagery by combining particle filtering and geometric active contours. Range information is weighted by the proposed Gaussian weighting scheme to improve segmentation achieved by active contours. In addition, this work present an on-line shape learning method based on principal component analysis to reacquire track of an object in the event that it disappears from the field of view and reappears later. Second, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose in 3D space. In this work, we take advantage of knowledge of a 3D model of an object and we employ particle filtering to generate and propagate the translation and rotation parameters in a decoupled manner. Moreover, to continuously track the object in the presence of occlusions, we propose an occlusion detection and handling scheme based on the control of the degree of dependence between predictions and measurements of the system. Third, we introduce the fast level-set based algorithm applicable to real-time applications. In this algorithm, a contour-based tracker is improved in terms of computational complexity and the tracker performs real-time curve evolution for detecting multiple windows. Lastly, we deal with rapid human motion in context of object segmentation and visual tracking. Specifically, we introduce a model-free and marker-less approach for human body tracking based on a dynamic color model and geometric information of a human body from a monocular video sequence. The contributions of this thesis are summarized as follows: 1. Reliable algorithm to track deformable objects in a sequence consisting of 3D range data by combining particle filtering and statistics-based active contour models. 2. Effective handling scheme based on object's 2D shape information for the challenging situations in which the tracked object is completely gone from the image domain during tracking. 3. Robust 2D-3D pose tracking algorithm using a 3D shape prior and particle filters on SE(3). 4. Occlusion handling scheme based on the degree of trust between predictions and measurements of the tracking system, which is controlled in an online fashion. 5. Fast level set based active contour models applicable to real-time object detection. 6. Model-free and marker-less approach for tracking of rapid human motion based on a dynamic color model and geometric information of a human body.
APA, Harvard, Vancouver, ISO, and other styles
9

Niethammer, Marc. "Dynamic Level Sets for Visual Tracking." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/7606.

Full text
Abstract:
This thesis introduces geometric dynamic active contours in the context of visual tracking, augmenting geometric curve evolution with physically motivated dynamics. Adding additional state information to an evolving curve lifts the curve evolution problem to space dimensions larger than two and thus forbids the use of classical level set techniques. This thesis therefore develops and explores level set methods for problems of higher codimension, putting an emphasis on the vector distance function based approach. This formalism is very general, it is interesting in its own right and still a challenging topic. Two different implementations for geometric dynamic active contours are explored: the full level set approach as well as a simpler partial level set approach. The full level set approach results in full topological flexibility and can deal with curve intersections in the image plane. However, it is computationally expensive. On the other hand the partial level set approach gives up the topological flexibility (intersecting curves cannot be represented) for increased computational efficiency. Contours colliding with different dynamic information (e.g., objects crossing in the image plane) will be merged in the partial level set approach whereas they will correctly traverse each other in the full level set approach. Both implementations are illustrated on synthetic and real examples. Compared to the traditional static curve evolution case, fundamentally different evolution behaviors can be obtained by propagating additional information along with every point on a curve.
APA, Harvard, Vancouver, ISO, and other styles
10

Dambreville, Samuel. "Statistical and geometric methods for shape-driven segmentation and tracking." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22707.

Full text
Abstract:
Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Allen Tannenbaum; Committee Member: Anthony Yezzi; Committee Member: Marc Niethammer; Committee Member: Patricio Vela; Committee Member: Yucel Altunbasak.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Geometric active contour"

1

Wang, Ying, Xinbo Gao, Xuelong Li, Dacheng Tao, and Bin Wang. "Embedded Geometric Active Contour with Shape Constraint for Mass Segmentation." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_121.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chan, T. F., and L. A. Vese. "Active Contour and Segmentation Models using Geometric PDE’s for Medical Imaging." In Mathematics and Visualization. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55987-7_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Li, Ling, Jia Gu, Tiexiang Wen, Wenjian Qin, Hua Xiao, and Jiaping Yu. "Multiscale Geometric Active Contour Model and Boundary Extraction in Kidney MR Images." In Health Information Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06269-3_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

de Vieilleville, François, and Jacques-Olivier Lachaud. "Digital Deformable Model Simulating Active Contours." In Discrete Geometry for Computer Imagery. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04397-0_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

He, Ning, Peng Zhang, and Ke Lu. "A Geometric Active Contours Model for Multiple Objects Segmentation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87442-3_141.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Biswas, Ankur, Santi P. Maity, and Paritosh Bhattacharya. "Optimal Geometric Active Contours: Application to Human Brain Segmentation." In Social Transformation – Digital Way. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_53.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Molina-Abril, Helena, and Alejandro F. Frangi. "Topo-Geometric Filtration Scheme for Geometric Active Contours and Level Sets: Application to Cerebrovascular Segmentation." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10404-1_94.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sierra, Gabriel Hernández, Edel Garcia Reyes, and Gerardo Iglesias Ham. "Active Contour and Morphological Filters for Geometrical Normalization of Human Face." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11578079_75.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Caselles, Vicent, Ron Kimmel, and Guillermo Sapiro. "Geometric Active Contours for Image Segmentation." In Handbook of Image and Video Processing. Elsevier, 2005. http://dx.doi.org/10.1016/b978-012119792-6/50099-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Mezghich, Mohamed Amine, Maweheb Saidani, Slim M’Hiri, and Faouzi Ghorbel. "An affine shape constraint for geometric active contours." In Emerging Trends in Image Processing, Computer Vision and Pattern Recognition. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-12-802045-6.00033-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Geometric active contour"

1

Dang, Hongshe, Ying Hong, Xin Fang, and Feili Qiang. "Initial Contour Automatic Selection of Geometric Active Contour Model." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.253.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhenglong Li, Qingshan Liu, Hanqing Lu, and Dimitris Metaxas. "Lennard-Jones force field for Geometric Active Contour." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761330.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Albalooshi, Fatema A., Paheding Sidike, and Vijayan K. Asari. "Efficient hyperspectral image segmentation using geometric active contour formulation." In SPIE Remote Sensing, edited by Lorenzo Bruzzone, Jon Atli Benediktsson, and Francesca Bovolo. SPIE, 2014. http://dx.doi.org/10.1117/12.2067475.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Cui, Hua, Jianhua Wu, Liqun Gao, and Kong Zhi. "Inertia: A new force field for geometric active contour." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4587230.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Krishnan, N., and S. Naga Nandini Sujatha. "A fast geometric active contour model with automatic region grid." In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705794.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wang, Heng, Zihan Zhuo, Jianan Wu, and Jingtian Tang. "Self-adaptive level set methods combined with geometric active contour." In 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, 2016. http://dx.doi.org/10.1109/siprocess.2016.7888328.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Byreddy, Dinesh Reddy, and MV Raghunadh. "An application of geometric active contour in bio-medical engineering." In 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA). IEEE, 2014. http://dx.doi.org/10.1109/cscita.2014.6839280.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

He, Ning, Lulu Zhang, and Yixue Wang. "Remote sensing image object extraction using convex geometric active contour model." In the Fifth International Conference. ACM Press, 2013. http://dx.doi.org/10.1145/2499788.2499824.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Fan, Jianchao, Jialan Chu, Dawei Jiang, Fanglei Liu, and Wei Yao. "Remote sensing images coastline detection based on geometric active contour models." In 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2015. http://dx.doi.org/10.1109/icicip.2015.7388197.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Han, and Qi-sheng Wu. "Motion object tracking algorithm using an improved geometric active contour model." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646255.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Geometric active contour' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography